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Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices to potentially higher dimensions.

We have utility functions for common cases like Scalar, 1D, 2D, 3D and 4D tensors, as well a number of functions to initialize tensors in ways useful for machine learning.

tf.tensor (values, shape?, dtype?) function Source

Creates a tf.Tensor with the provided values, shape and dtype.

// Pass an array of values to create a vector.
tf.tensor([1, 2, 3, 4]).print();
// Pass a nested array of values to make a matrix or a higher
// dimensional tensor.
tf.tensor([[1, 2], [3, 4]]).print();
// Pass a flat array and specify a shape yourself.
tf.tensor([1, 2, 3, 4], [2, 2]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape (number[]) The shape of the tensor. Optional. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor
tf.scalar (value, dtype?) function Source

Creates rank-0 tf.Tensor (scalar) with the provided value and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.scalar() as it makes the code more readable.

tf.scalar(3.14).print();
Parameters:
  • value (number|boolean|string) The value of the scalar.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Scalar
tf.tensor1d (values, dtype?) function Source

Creates rank-1 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor1d() as it makes the code more readable.

tf.tensor1d([1, 2, 3]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be array of numbers, or a TypedArray.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor1D
tf.tensor2d (values, shape?, dtype?) function Source

Creates rank-2 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor2d() as it makes the code more readable.

// Pass a nested array.
tf.tensor2d([[1, 2], [3, 4]]).print();
// Pass a flat array and specify a shape.
tf.tensor2d([1, 2, 3, 4], [2, 2]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape ([number, number]) The shape of the tensor. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor2D
tf.tensor3d (values, shape?, dtype?) function Source

Creates rank-3 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor3d() as it makes the code more readable.

// Pass a nested array.
tf.tensor3d([[[1], [2]], [[3], [4]]]).print();
// Pass a flat array and specify a shape.
tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape ([number, number, number]) The shape of the tensor. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor3D
tf.tensor4d (values, shape?, dtype?) function Source

Creates rank-4 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor4d() as it makes the code more readable.

// Pass a nested array.
tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();
// Pass a flat array and specify a shape.
tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape ([number, number, number, number]) The shape of the tensor. Optional. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor4D
tf.tensor5d (values, shape?, dtype?) function Source

Creates rank-5 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor5d() as it makes the code more readable.

// Pass a nested array.
tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();
// Pass a flat array and specify a shape.
tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape ([number, number, number, number, number]) The shape of the tensor. Optional. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor5D
tf.tensor6d (values, shape?, dtype?) function Source

Creates rank-6 tf.Tensor with the provided values, shape and dtype.

The same functionality can be achieved with tf.tensor(), but in general we recommend using tf.tensor6d() as it makes the code more readable.

// Pass a nested array.
tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();
// Pass a flat array and specify a shape.
tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();
Parameters:
  • values (TypedArray|Array) The values of the tensor. Can be nested array of numbers, or a flat array, or a TypedArray.
  • shape ([number, number, number, number, number, number]) The shape of the tensor. Optional. If not provided, it is inferred from values. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type. Optional
Returns: tf.Tensor6D
tf.buffer (shape, dtype?, values?) function Source

Creates an empty tf.TensorBuffer with the specified shape and dtype.

The values are stored in CPU as TypedArray. Fill the buffer using buffer.set(), or by modifying directly buffer.values.

When done, call buffer.toTensor() to get an immutable tf.Tensor with those values.

// Create a buffer and set values at particular indices.
const buffer = tf.buffer([2, 2]);
buffer.set(3, 0, 0);
buffer.set(5, 1, 0);

// Convert the buffer back to a tensor.
buffer.toTensor().print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • dtype ('float32') The dtype of the buffer. Defaults to 'float32'. Optional
  • values (DataTypeMap['float32']) The values of the buffer as TypedArray. Defaults to zeros. Optional
Returns: tf.TensorBuffer
tf.clone (x) function Source

Creates a new tensor with the same values and shape as the specified tensor.

const x = tf.tensor([1, 2]);

x.clone().print();
Parameters:
Returns: tf.Tensor
tf.complex (real, imag) function Source

Converts two real numbers to a complex number.

Given a tensor real representing the real part of a complex number, and a tensor imag representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form [r0, i0, r1, i1], where r represents the real part and i represents the imag part.

The input tensors real and imag must have the same shape.

const real = tf.tensor1d([2.25, 3.25]);
const imag = tf.tensor1d([4.75, 5.75]);
const complex = tf.complex(real, imag);

complex.print();
Parameters:
Returns: tf.Tensor
tf.eye (numRows, numColumns?, batchShape?, dtype?) function Source

Create an identity matrix.

Parameters:
  • numRows (number) Number of rows.
  • numColumns (number) Number of columns. Defaults to numRows. Optional
  • batchShape ([ number ]|[number, number]|[number, number, number]|[number, number, number, number]) If provided, will add the batch shape to the beginning of the shape of the returned tf.Tensor by repeating the identity matrix. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') Data type. Optional
Returns: tf.Tensor2D
tf.fill (shape, value, dtype?) function Source

Creates a tf.Tensor filled with a scalar value.

tf.fill([2, 2], 4).print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • value (number|string) The scalar value to fill the tensor with.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The type of an element in the resulting tensor. Defaults to 'float'. Optional
Returns: tf.Tensor
tf.imag (input) function Source

Returns the imaginary part of a complex (or real) tensor.

Given a tensor input, this operation returns a tensor of type float that is the imaginary part of each element in input considered as a complex number. If input is real, a tensor of all zeros is returned.

const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
tf.imag(x).print();
Parameters:
Returns: tf.Tensor
tf.linspace (start, stop, num) function Source

Return an evenly spaced sequence of numbers over the given interval.

tf.linspace(0, 9, 10).print();
Parameters:
  • start (number) The start value of the sequence.
  • stop (number) The end value of the sequence.
  • num (number) The number of values to generate.
Returns: tf.Tensor1D
tf.oneHot (indices, depth, onValue?, offValue?) function Source

Creates a one-hot tf.Tensor. The locations represented by indices take value onValue (defaults to 1), while all other locations take value offValue (defaults to 0). If indices is rank R, the output has rank R+1 with the last axis of size depth.

tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
Parameters:
  • indices (tf.Tensor|TypedArray|Array) tf.Tensor of indices with dtype int32.
  • depth (number) The depth of the one hot dimension.
  • onValue (number) A number used to fill in the output when the index matches the location. Optional
  • offValue (number) A number used to fill in the output when the index does not match the location. Optional
Returns: tf.Tensor
tf.ones (shape, dtype?) function Source

Creates a tf.Tensor with all elements set to 1.

tf.ones([2, 2]).print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The type of an element in the resulting tensor. Defaults to 'float'. Optional
Returns: tf.Tensor
tf.onesLike (x) function Source

Creates a tf.Tensor with all elements set to 1 with the same shape as the given tensor.

const x = tf.tensor([1, 2]);
tf.onesLike(x).print();
Parameters:
Returns: tf.Tensor
tf.print (x, verbose?) function Source

Prints information about the tf.Tensor including its data.

const verbose = true;
tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
Parameters:
  • x (tf.Tensor) The tensor to be printed.
  • verbose (boolean) Whether to print verbose information about the Tensor, including dtype and size. Optional
Returns: void
tf.range (start, stop, step?, dtype?) function Source

Creates a new tf.Tensor1D filled with the numbers in the range provided.

The tensor is a is half-open interval meaning it includes start, but excludes stop. Decrementing ranges and negative step values are also supported.

tf.range(0, 9, 2).print();
Parameters:
  • start (number) An integer start value
  • stop (number) An integer stop value
  • step (number) An integer increment (will default to 1 or -1) Optional
  • dtype ('float32'|'int32') The data type of the output tensor. Defaults to 'float32'. Optional
Returns: tf.Tensor1D
tf.real (input) function Source

Returns the real part of a complex (or real) tensor.

Given a tensor input, this operation returns a tensor of type float that is the real part of each element in input considered as a complex number.

If the input is real, it simply makes a clone.

const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);
tf.real(x).print();
Parameters:
Returns: tf.Tensor
tf.truncatedNormal (shape, mean?, stdDev?, dtype?, seed?) function Source

Creates a tf.Tensor with values sampled from a truncated normal distribution.

tf.truncatedNormal([2, 2]).print();

The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • mean (number) The mean of the normal distribution. Optional
  • stdDev (number) The standard deviation of the normal distribution. Optional
  • dtype ('float32'|'int32') The data type of the output tensor. Optional
  • seed (number) The seed for the random number generator. Optional
Returns: tf.Tensor
tf.variable (initialValue, trainable?, name?, dtype?) function Source

Creates a new variable with the provided initial value.

const x = tf.variable(tf.tensor([1, 2, 3]));
x.assign(tf.tensor([4, 5, 6]));

x.print();
Parameters:
  • initialValue (tf.Tensor) Initial value for the tensor.
  • trainable (boolean) If true, optimizers are allowed to update it. Optional
  • name (string) Name of the variable. Defaults to a unique id. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') If set, initialValue will be converted to the given type. Optional
Returns: tf.Variable
tf.zeros (shape, dtype?) function Source

Creates a tf.Tensor with all elements set to 0.

tf.zeros([2, 2]).print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The type of an element in the resulting tensor. Can be 'float32', 'int32' or 'bool'. Defaults to 'float'. Optional
Returns: tf.Tensor
tf.zerosLike (x) function Source

Creates a tf.Tensor with all elements set to 0 with the same shape as the given tensor.

const x = tf.tensor([1, 2]);
tf.zerosLike(x).print();
Parameters:
Returns: tf.Tensor

This section shows the main Tensor related classes in TensorFlow.js and the methods we expose on them.

A tf.Tensor object represents an immutable, multidimensional array of numbers that has a shape and a data type.

See tf.tensor() for details on how to create a tf.Tensor.

flatten () method Source

Flatten a Tensor to a 1D array.

Returns: tf.Tensor1D
asScalar () method Source

Converts a size-1 tf.Tensor to a tf.Scalar.

Returns: tf.Scalar
as1D () method Source

Converts a tf.Tensor to a tf.Tensor1D.

Returns: tf.Tensor1D
as2D (rows, columns) method Source

Converts a tf.Tensor to a tf.Tensor2D.

Parameters:
Returns: tf.Tensor2D
as3D (rows, columns, depth) method Source

Converts a tf.Tensor to a tf.Tensor3D.

Parameters:
Returns: tf.Tensor3D
as4D (rows, columns, depth, depth2) method Source

Converts a tf.Tensor to a tf.Tensor4D.

Parameters:
Returns: tf.Tensor4D
as5D (rows, columns, depth, depth2, depth3) method Source

Converts a tf.Tensor to a tf.Tensor5D.

Parameters:
  • rows (number) Number of rows in tf.Tensor5D.
  • columns (number) Number of columns in tf.Tensor5D.
  • depth (number) Depth of tf.Tensor5D.
  • depth2 (number) 4th dimension of tf.Tensor5D.
  • depth3 (number) 5th dimension of 'tf.Tensor5D'
Returns: tf.Tensor5D
asType (dtype) method Source

Casts a tf.Tensor to a specified dtype.

Parameters:
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') Data-type to cast the tensor to.
Returns: this
buffer () method Source

Returns a promise of tf.TensorBuffer that holds the underlying data.

Returns: Promise<tf.TensorBuffer>
bufferSync () method Source

Returns a tf.TensorBuffer that holds the underlying data.

Returns: tf.TensorBuffer
array () method Source

Returns the tensor data as a nested array. The transfer of data is done asynchronously.

Returns: Promise<number[]>
arraySync () method Source

Returns the tensor data as a nested array. The transfer of data is done synchronously.

Returns: number[]
data () method Source

Asynchronously downloads the values from the tf.Tensor. Returns a promise of TypedArray that resolves when the computation has finished.

Returns: Promise<DataTypeMap[NumericDataType]>
dataSync () method Source

Synchronously downloads the values from the tf.Tensor. This blocks the UI thread until the values are ready, which can cause performance issues.

Returns: DataTypeMap[NumericDataType]
dispose () method Source

Disposes tf.Tensor from memory.

Returns: void
toFloat () method Source

Casts the array to type float32

Returns: this
toInt () method Source

Casts the array to type int32

Returns: this
toBool () method Source

Casts the array to type bool

Returns: this
print (verbose?) method Source

Prints the tf.Tensor. See tf.print() for details.

Parameters:
  • verbose (boolean) Whether to print verbose information about the tensor, including dtype and size. Optional
Returns: void
reshape (newShape) method Source

Reshapes the tensor into the provided shape. See tf.reshape() for more details.

Parameters:
  • newShape (number[]) An array of integers defining the output tensor shape.
Returns: tf.Tensor
reshapeAs (x) method Source

Reshapes the tensor into the shape of the provided tensor.

Parameters:
  • x (tf.Tensor) The tensor of required shape.
Returns: tf.Tensor
expandDims (axis?) method Source

Returns a tf.Tensor that has expanded rank, by inserting a dimension into the tensor's shape. See tf.expandDims() for details.

Parameters:
  • axis (number) The dimension index at which to insert shape of 1. Defaults to 0 (the first dimension). Optional
Returns: tf.Tensor
cumsum (axis?, exclusive?, reverse?) method Source

Returns the cumulative sum of the tf.Tensor along axis.

Parameters:
  • axis (number) The axis along which to sum. Optional. Defaults to 0. Optional
  • exclusive (boolean) Whether to perform exclusive cumulative sum. Defaults to false. If set to true then the sum of each tensor entry does not include its own value, but only the values previous to it along the specified axis. Optional
  • reverse (boolean) Whether to sum in the opposite direction. Defaults to false. Optional
Returns: tf.Tensor
squeeze (axis?) method Source

Returns a tf.Tensor with dimensions of size 1 removed from the shape. See tf.squeeze() for more details.

Parameters:
  • axis (number[]) A list of numbers. If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1. Optional
Returns: tf.Tensor
clone () method Source

Returns a copy of the tensor. See tf.clone() for details.

Returns: tf.Tensor
toString (verbose?) method Source

Returns a human-readable description of the tensor. Useful for logging.

Parameters:
  • verbose (boolean) Optional
Returns: string
tf.Variable extends tf.Tensor class Source

A mutable tf.Tensor, useful for persisting state, e.g. for training.

assign (newValue) method Source

Assign a new tf.Tensor to this variable. The new tf.Tensor must have the same shape and dtype as the old tf.Tensor.

Parameters:
  • newValue (tf.Tensor) New tensor to be assigned to this variable.
Returns: void

A mutable object, similar to tf.Tensor, that allows users to set values at locations before converting to an immutable tf.Tensor.

See tf.buffer() for creating a tensor buffer.

set (value, ...locs) method Source

Sets a value in the buffer at a given location.

Parameters:
  • value (SingleValueMap[D]) The value to set.
  • ...locs (number[]) The location indices.
Returns: void
get (...locs) method Source

Returns the value in the buffer at the provided location.

Parameters:
  • ...locs (number[]) The location indices.
Returns: SingleValueMap[D]
toTensor () method Source

Creates an immutable tf.Tensor object from the buffer.

Returns: tf.Tensor

This section describes some common Tensor transformations for reshaping and type-casting.

tf.batchToSpaceND (x, blockShape, crops) function Source

This operation reshapes the "batch" dimension 0 into M + 1 dimensions of shape blockShape + [batch], interleaves these blocks back into the grid defined by the spatial dimensions [1, ..., M], to obtain a result with the same rank as the input. The spatial dimensions of this intermediate result are then optionally cropped according to crops to produce the output. This is the reverse of tf.spaceToBatchND(). See below for a precise description.

const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
const blockShape = [2, 2];
const crops = [[0, 0], [0, 0]];

x.batchToSpaceND(blockShape, crops).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) A tf.Tensor. N-D with x.shape = `[batch] + spatialShape +

    • remainingShape, where spatialShape hasM` dimensions.
  • blockShape (number[]) A 1-D array. Must have shape [M], all values must be >= 1.
  • crops (number[][]) A 2-D array. Must have shape [M, 2], all values must be >= 0. crops[i] = [cropStart, cropEnd] specifies the amount to crop from input dimension i + 1, which corresponds to spatial dimension i. It is required that cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]

    This operation is equivalent to the following steps:

    1. Reshape x to reshaped of shape: `[blockShape[0], ...,
    • blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,
    • x.shape[N-1]]`
    1. Permute dimensions of reshapedto produce permuted of shape `[batch /
    • prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],
    • blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
    1. Reshape permuted to produce reshapedPermuted of shape `[batch /
    • prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *
    • blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`
    1. Crop the start and end of dimensions [1, ..., M] of reshapedPermuted according to crops to produce the output of shape: `[batch /
    • prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],
    • ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -
    • crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`
Returns: tf.Tensor
tf.cast (x, dtype) function Source

Casts a tf.Tensor to a new dtype.

const x = tf.tensor1d([1.5, 2.5, 3]);
tf.cast(x, 'int32').print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to be casted.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The dtype to cast the input tensor to.
Returns: tf.Tensor
tf.depthToSpace (x, blockSize, dataFormat?) function Source

Rearranges data from depth into blocks of spatial data. More specifically, this op outputs a copy of the input tensor where values from the depth dimension are moved in spatial blocks to the height and width dimensions. The attr blockSize indicates the input block size and how the data is moved.

  • Chunks of data of size blockSize * blockSize from depth are rearranged into non-overlapping blocks of size blockSize x blockSize

  • The width the output tensor is inputWidth * blockSize, whereas the height is inputHeight * blockSize

  • The Y, X coordinates within each block of the output image are determined by the high order component of the input channel index

  • The depth of the input tensor must be divisible by blockSize * blockSize

The dataFormat attr specifies the layout of the input and output tensors with the following options: "NHWC": [ batch, height, width, channels ] "NCHW": [ batch, channels, height, width ]

const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
const blockSize = 2;
const dataFormat = "NHWC";

tf.depthToSpace(x, blockSize, dataFormat).print();
Parameters:
  • x (tf.Tensor4D|TypedArray|Array) The input tensor of rank 4
  • blockSize (number)
  • dataFormat ('NHWC'|'NCHW') An optional string from: "NHWC", "NCHW". Defaults to "NHWC" Optional
Returns: tf.Tensor4D
tf.expandDims (x, axis?) function Source

Returns a tf.Tensor that has expanded rank, by inserting a dimension into the tensor's shape.

const x = tf.tensor1d([1, 2, 3, 4]);
const axis = 1;
x.expandDims(axis).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor whose dimensions to be expanded.
  • axis (number) The dimension index at which to insert shape of 1. Defaults to 0 (the first dimension). Optional
Returns: tf.Tensor
tf.pad (x, paddings, constantValue?) function Source

Pads a tf.Tensor with a given value and paddings.

This operation currently only implements the CONSTANT mode.

Also available are stricter rank-specific methods with the same signature as this method that assert that paddings is of given length.

  • tf.pad1d
  • tf.pad2d
  • tf.pad3d
  • tf.pad4d
const x = tf.tensor1d([1, 2, 3, 4]);
x.pad([[1, 2]]).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The tensor to pad.
  • paddings (Array) An array of length R (the rank of the tensor), where each element is a length-2 tuple of ints [padBefore, padAfter], specifying how much to pad along each dimension of the tensor.
  • constantValue (number) The pad value to use. Defaults to 0. Optional
Returns: tf.Tensor
tf.reshape (x, shape) function Source

Reshapes a tf.Tensor to a given shape.

Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape.

If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a shape of [-1] flattens into 1-D. At most one component of shape can be -1.

If shape is 1-D or higher, then the operation returns a tensor with shape shape filled with the values of tensor. In this case, the number of elements implied by shape must be the same as the number of elements in tensor.

const x = tf.tensor1d([1, 2, 3, 4]);
x.reshape([2, 2]).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to be reshaped.
  • shape (number[]) An array of integers defining the output tensor shape.
Returns: tf.Tensor
tf.setdiff1dAsync (x, y) function Source

Computes the difference between two lists of numbers.

Given a Tensor x and a Tensor y, this operation returns a Tensor out that represents all values that are in x but not in y. The returned Tensor out is sorted in the same order that the numbers appear in x (duplicates are preserved). This operation also returns a Tensor indices that represents the position of each out element in x. In other words:

out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]

const x = [1, 2, 3, 4, 5, 6];
const y = [1, 3, 5];

const [out, indices] = await tf.setdiff1dAsync(x, y);
out.print(); // [2, 4, 6]
indices.print(); // [1, 3, 5]
Parameters:
Returns: Promise<[tf.Tensor, tf.Tensor]>
tf.spaceToBatchND (x, blockShape, paddings) function Source

This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape blockShape, and interleaves these blocks with the "batch" dimension (0) such that in the output, the spatial dimensions [1, ..., M] correspond to the position within the grid, and the batch dimension combines both the position within a spatial block and the original batch position. Prior to division into blocks, the spatial dimensions of the input are optionally zero padded according to paddings. See below for a precise description.

const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];

x.spaceToBatchND(blockShape, paddings).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) A tf.Tensor. N-D with x.shape = `[batch] + spatialShape +

    • remainingShape, where spatialShape hasM` dimensions.
  • blockShape (number[]) A 1-D array. Must have shape [M], all values must be >= 1.
  • paddings (number[][]) A 2-D array. Must have shape [M, 2], all values must be >= 0. paddings[i] = [padStart, padEnd] specifies the amount to zero-pad from input dimension i + 1, which corresponds to spatial dimension i. It is required that (inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0

    This operation is equivalent to the following steps:

    1. Zero-pad the start and end of dimensions [1, ..., M] of the input according to paddings to produce padded of shape paddedShape.

    2. Reshape padded to reshapedPadded of shape: `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,

    • paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`
    1. Permute dimensions of reshapedPadded to produce permutedReshapedPadded of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,
    • paddedShape[M] / blockShape[M-1]] + remainingShape`
    1. Reshape permutedReshapedPadded to flatten blockShape into the batch dimension, producing an output tensor of shape: `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,
    • paddedShape[M] / blockShape[M-1]] + remainingShape`
Returns: tf.Tensor
tf.squeeze (x, axis?) function Source

Removes dimensions of size 1 from the shape of a tf.Tensor.

const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);
x.squeeze().print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to be squeezed.
  • axis (number[]) An optional list of numbers. If specified, only squeezes the dimensions listed. The dimension index starts at 0. It is an error to squeeze a dimension that is not 1. Optional
Returns: tf.Tensor

TensorFlow.js provides several operations to slice or extract parts of a tensor, or join multiple tensors together.

tf.concat (tensors, axis?) function Source

Concatenates a list of tf.Tensors along a given axis.

The tensors ranks and types must match, and their sizes must match in all dimensions except axis.

Also available are stricter rank-specific methods that assert that tensors are of the given rank:

  • tf.concat1d
  • tf.concat2d
  • tf.concat3d
  • tf.concat4d

Except tf.concat1d (which does not have axis param), all methods have same signature as this method.

const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
a.concat(b).print();  // or a.concat(b)
const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
const c = tf.tensor1d([5, 6]);
tf.concat([a, b, c]).print();
const a = tf.tensor2d([[1, 2], [10, 20]]);
const b = tf.tensor2d([[3, 4], [30, 40]]);
const axis = 1;
tf.concat([a, b], axis).print();
Parameters:
  • tensors (Array) A list of tensors to concatenate.
  • axis (number) The axis to concate along. Defaults to 0 (the first dim). Optional
Returns: tf.Tensor
tf.gather (x, indices, axis?) function Source

Gather slices from tensor x's axis axis according to indices.

const x = tf.tensor1d([1, 2, 3, 4]);
const indices = tf.tensor1d([1, 3, 3], 'int32');

x.gather(indices).print();
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
const indices = tf.tensor1d([1, 1, 0], 'int32');

x.gather(indices).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor whose slices to be gathered.
  • indices (tf.Tensor|TypedArray|Array) The indices of the values to extract.
  • axis (number) The axis over which to select values. Defaults to 0. Optional
Returns: tf.Tensor
tf.reverse (x, axis?) function Source

Reverses a tf.Tensor along a specified axis.

Also available are stricter rank-specific methods that assert that x is of the given rank:

  • tf.reverse1d
  • tf.reverse2d
  • tf.reverse3d
  • tf.reverse4d

Except tf.reverse1d (which does not have axis param), all methods have same signature as this method.

const x = tf.tensor1d([1, 2, 3, 4]);

x.reverse().print();
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.reverse(axis).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to be reversed.
  • axis (number|number[]) The set of dimensions to reverse. Must be in the range [-rank(x), rank(x)). Defaults to all axes. Optional
Returns: tf.Tensor
tf.slice (x, begin, size?) function Source

Extracts a slice from a tf.Tensor starting at coordinates begin and is of size size.

Also available are stricter rank-specific methods with the same signature as this method that assert that x is of the given rank:

  • tf.slice1d
  • tf.slice2d
  • tf.slice3d
  • tf.slice4d
const x = tf.tensor1d([1, 2, 3, 4]);

x.slice([1], [2]).print();
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

x.slice([1, 0], [1, 2]).print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tf.Tensor to slice from.
  • begin (number|number[]) The coordinates to start the slice from. The length can be less than the rank of x - the rest of the axes will have implicit 0 as start. Can also be a single number, in which case it specifies the first axis.
  • size (number|number[]) The size of the slice. The length can be less than the rank of x - the rest of the axes will have implicit -1. A value of -1 requests the rest of the dimensions in the axis. Can also be a single number, in which case it specifies the size of the first axis. Optional
Returns: tf.Tensor
tf.split (x, numOrSizeSplits, axis?) function Source

Splits a tf.Tensor into sub tensors.

If numOrSizeSplits is a number, splits x along dimension axis into numOrSizeSplits smaller tensors. Requires that numOrSizeSplits evenly divides x.shape[axis].

If numOrSizeSplits is a number array, splits x into numOrSizeSplits.length pieces. The shape of the i-th piece has the same size as x except along dimension axis where the size is numOrSizeSplits[i].

const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
const [a, b] = tf.split(x, 2, 1);
a.print();
b.print();

const [c, d, e] = tf.split(x, [1, 2, 1], 1);
c.print();
d.print();
e.print();
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to split.
  • numOrSizeSplits (number[]|number) Either an integer indicating the number of splits along the axis or an array of integers containing the sizes of each output tensor along the axis. If a number then it must evenly divide x.shape[axis]; otherwise the sum of sizes must match x.shape[axis].
  • axis (number) The dimension along which to split. Defaults to 0 (the first dim). Optional
Returns: tf.Tensor[]
tf.stack (tensors, axis?) function Source

Stacks a list of rank-R tf.Tensors into one rank-(R+1) tf.Tensor.

const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
const c = tf.tensor1d([5, 6]);
tf.stack([a, b, c]).print();
Parameters:
  • tensors (Array) A list of tensor objects with the same shape and dtype.
  • axis (number) The axis to stack along. Defaults to 0 (the first dim). Optional
Returns: tf.Tensor
tf.tile (x, reps) function Source

Construct a tensor by repeating it the number of times given by reps.

This operation creates a new tensor by replicating input reps times. The output tensor's i'th dimension has input.shape[i] * reps[i] elements, and the values of input are replicated reps[i] times along the i'th dimension. For example, tiling [a, b, c, d] by [2] produces [a, b, c, d, a, b, c, d].

const a = tf.tensor1d([1, 2]);

a.tile([2]).print();    // or a.tile([2])
const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

a.tile([1, 2]).print();  // or a.tile([1, 2])
Parameters:
  • x (tf.Tensor|TypedArray|Array) The tensor to tile.
  • reps (number[]) Determines the number of replications per dimension.
Returns: tf.Tensor
tf.unstack (x, axis?) function Source

Unstacks a tf.Tensor of rank-R into a list of rank-(R-1) tf.Tensors.

const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);

tf.unstack(a).forEach(tensor => tensor.print());
Parameters:
  • x (tf.Tensor|TypedArray|Array) A tensor object.
  • axis (number) The axis to unstack along. Defaults to 0 (the first dim). Optional
Returns: tf.Tensor[]
tf.multinomial (logits, numSamples, seed?, normalized?) function Source

Creates a tf.Tensor with values drawn from a multinomial distribution.

const probs = tf.tensor([.75, .25]);
tf.multinomial(probs, 3).print();
Parameters:
  • logits (tf.Tensor1D|tf.Tensor2D|TypedArray|Array) 1D array with unnormalized log-probabilities, or 2D array of shape [batchSize, numOutcomes]. See the normalized parameter.
  • numSamples (number) Number of samples to draw for each row slice.
  • seed (number) The seed number. Optional
  • normalized (boolean) Whether the provided logits are normalized true probabilities (sum to 1). Defaults to false. Optional
tf.randomNormal (shape, mean?, stdDev?, dtype?, seed?) function Source

Creates a tf.Tensor with values sampled from a normal distribution.

tf.randomNormal([2, 2]).print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • mean (number) The mean of the normal distribution. Optional
  • stdDev (number) The standard deviation of the normal distribution. Optional
  • dtype ('float32'|'int32') The data type of the output. Optional
  • seed (number) The seed for the random number generator. Optional
Returns: tf.Tensor
tf.randomUniform (shape, minval?, maxval?, dtype?) function Source

Creates a tf.Tensor with values sampled from a uniform distribution.

The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded.

tf.randomUniform([2, 2]).print();
Parameters:
  • shape (number[]) An array of integers defining the output tensor shape.
  • minval (number) The lower bound on the range of random values to generate. Defaults to 0. Optional
  • maxval (number) The upper bound on the range of random values to generate. Defaults to 1. Optional
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data type of the output tensor. Defaults to 'float32'. Optional
Returns: tf.Tensor

Models are one of the primary abstractions used in TensorFlow.js Layers. Models can be trained, evaluated, and used for prediction. A model's state (topology, and optionally, trained weights) can be restored from various formats.

Models are a collection of Layers, see Model Creation for details about how Layers can be connected.

There are two primary ways of creating models.

  • Sequential — Easiest, works if the models is a simple stack of each layer's input resting on the top of the previous layer's output.
  • Model — Offers more control if the layers need to be wired together in graph-like ways — multiple 'towers', layers that skip a layer, etc.
tf.sequential (config?) function Source

Creates a tf.Sequential model. A sequential model is any model where the outputs of one layer are the inputs to the next layer, i.e. the model topology is a simple 'stack' of layers, with no branching or skipping.

This means that the first layer passed to a tf.Sequential model should have a defined input shape. What that means is that it should have received an inputShape or batchInputShape argument, or for some type of layers (recurrent, Dense...) an inputDim argument.

The key difference between tf.model() and tf.sequential() is that tf.sequential() is less generic, supporting only a linear stack of layers. tf.model() is more generic and supports an arbitrary graph (without cycles) of layers.

Examples:

const model = tf.sequential();

// First layer must have an input shape defined.
model.add(tf.layers.dense({units: 32, inputShape: [50]}));
// Afterwards, TF.js does automatic shape inference.
model.add(tf.layers.dense({units: 4}));

// Inspect the inferred shape of the model's output, which equals
// `[null, 4]`. The 1st dimension is the undetermined batch dimension; the
// 2nd is the output size of the model's last layer.
console.log(JSON.stringify(model.outputs[0].shape));

It is also possible to specify a batch size (with potentially undetermined batch dimension, denoted by "null") for the first layer using the batchInputShape key. The following example is equivalent to the above:

const model = tf.sequential();

// First layer must have a defined input shape
model.add(tf.layers.dense({units: 32, batchInputShape: [null, 50]}));
// Afterwards, TF.js does automatic shape inference.
model.add(tf.layers.dense({units: 4}));

// Inspect the inferred shape of the model's output.
console.log(JSON.stringify(model.outputs[0].shape));

You can also use an Array of already-constructed Layers to create a tf.Sequential model:

const model = tf.sequential({
  layers: [tf.layers.dense({units: 32, inputShape: [50]}),
           tf.layers.dense({units: 4})]
});
console.log(JSON.stringify(model.outputs[0].shape));
Parameters:
  • config (Object) Optional
  • layers (tf.layers.Layer[]) Stack of layers for the model.
  • name (string) The name of this model.
Returns: tf.Sequential
tf.model (args) function Source

A model is a data structure that consists of Layers and defines inputs and outputs.

The key difference between tf.model() and tf.sequential() is that tf.model() is more generic, supporting an arbitrary graph (without cycles) of layers. tf.sequential() is less generic and supports only a linear stack of layers.

When creating a tf.LayersModel, specify its input(s) and output(s). Layers are used to wire input(s) to output(s).

For example, the following code snippet defines a model consisting of two dense layers, with 10 and 4 units, respectively.

// Define input, which has a size of 5 (not including batch dimension).
const input = tf.input({shape: [5]});

// First dense layer uses relu activation.
const denseLayer1 = tf.layers.dense({units: 10, activation: 'relu'});
// Second dense layer uses softmax activation.
const denseLayer2 = tf.layers.dense({units: 4, activation: 'softmax'});

// Obtain the output symbolic tensor by applying the layers on the input.
const output = denseLayer2.apply(denseLayer1.apply(input));

// Create the model based on the inputs.
const model = tf.model({inputs: input, outputs: output});

// The model can be used for training, evaluation and prediction.
// For example, the following line runs prediction with the model on
// some fake data.
model.predict(tf.ones([2, 5])).print();

See also: tf.sequential(), tf.loadLayersModel().

Parameters:
Returns: tf.LayersModel
tf.input (config) function Source

Used to instantiate an input to a model as a tf.SymbolicTensor.

Users should call the input factory function for consistency with other generator functions.

Example:

// Defines a simple logistic regression model with 32 dimensional input
// and 3 dimensional output.
const x = tf.input({shape: [32]});
const y = tf.layers.dense({units: 3, activation: 'softmax'}).apply(x);
const model = tf.model({inputs: x, outputs: y});
model.predict(tf.ones([2, 32])).print();

Note: input is only necessary when using model. When using sequential, specify inputShape for the first layer or use inputLayer as the first layer.

Parameters:
  • config (Object)
  • shape ((null | number)[]) A shape, not including the batch size. For instance, shape=[32] indicates that the expected input will be batches of 32-dimensional vectors.
  • batchShape ((null | number)[]) A shape tuple (integer), including the batch size. For instance, batchShape=[10, 32] indicates that the expected input will be batches of 10 32-dimensional vectors. batchShape=[null, 32] indicates batches of an arbitrary number of 32-dimensional vectors.
  • name (string) An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string')
  • sparse (boolean) A boolean specifying whether the placeholder to be created is sparse.
tf.loadGraphModel (modelUrl, options?) function Source

Load a graph model given a URL to the model definition.

Example of loading MobileNetV2 from a URL and making a prediction with a zeros input:

const modelUrl =
   'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
const model = await tf.loadGraphModel(modelUrl);
const zeros = tf.zeros([1, 224, 224, 3]);
model.predict(zeros).print();

Example of loading MobileNetV2 from a TF Hub URL and making a prediction with a zeros input:

const modelUrl =
   'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
const zeros = tf.zeros([1, 224, 224, 3]);
model.predict(zeros).print();
Parameters:
  • modelUrl (string|io.IOHandler) The url or an io.IOHandler that loads the model.
  • options (Object) Options for the HTTP request, which allows to send credentials and custom headers. Optional
  • requestInit (RequestInit) RequestInit (options) for HTTP requests.

    For detailed information on the supported fields, see https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

  • onProgress (OnProgressCallback) Progress callback.
  • fetchFunc (Function) A function used to override the window.fetch function.
  • strict (boolean) Strict loading model: whether extraneous weights or missing weights should trigger an Error.

    If true, require that the provided weights exactly match those required by the layers. false means that both extra weights and missing weights will be silently ignored.

    Default: true.

  • weightPathPrefix (string) Path prefix for weight files, by default this is calculated from the path of the model JSON file.

    For instance, if the path to the model JSON file is http://localhost/foo/model.json, then the default path prefix will be http://localhost/foo/. If a weight file has the path value group1-shard1of2 in the weight manifest, then the weight file will be loaded from http://localhost/foo/group1-shard1of2 by default. However, if you provide a weightPathPrefix value of http://localhost/foo/alt-weights, then the weight file will be loaded from the path http://localhost/foo/alt-weights/group1-shard1of2 instead.

  • fromTFHub (boolean) Whether the module or model is to be loaded from TF Hub.

    Setting this to true allows passing a TF-Hub module URL, omitting the standard model file name and the query parameters.

    Default: false.

Returns: Promise<tf.GraphModel>
tf.loadLayersModel (pathOrIOHandler, options?) function Source

Load a model composed of Layer objects, including its topology and optionally weights. See the Tutorial named "How to import a Keras Model" for usage examples.

This method is applicable to:

  1. Models created with the tf.layers.*, tf.sequential(), and tf.model() APIs of TensorFlow.js and later saved with the tf.LayersModel.save() method.
  2. Models converted from Keras or TensorFlow tf.keras using the tensorflowjs_converter

This mode is not applicable to TensorFlow SavedModels or their converted forms. For those models, use tf.loadGraphModel().

Example 1. Load a model from an HTTP server.

const model = await tf.loadLayersModel(
    'https://storage.googleapis.com/tfjs-models/tfjs/iris_v1/model.json');
model.summary();

Example 2: Save model's topology and weights to browser local storage; then load it back.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
console.log('Prediction from original model:');
model.predict(tf.ones([1, 3])).print();

const saveResults = await model.save('localstorage://my-model-1');

const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
console.log('Prediction from loaded model:');
loadedModel.predict(tf.ones([1, 3])).print();

Example 3. Saving model's topology and weights to browser IndexedDB; then load it back.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
console.log('Prediction from original model:');
model.predict(tf.ones([1, 3])).print();

const saveResults = await model.save('indexeddb://my-model-1');

const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
console.log('Prediction from loaded model:');
loadedModel.predict(tf.ones([1, 3])).print();

Example 4. Load a model from user-selected files from HTML file input elements.

// Note: this code snippet will not work without the HTML elements in the
//   page
const jsonUpload = document.getElementById('json-upload');
const weightsUpload = document.getElementById('weights-upload');

const model = await tf.loadLayersModel(
    tf.io.browserFiles([jsonUpload.files[0], weightsUpload.files[0]]));
Parameters:
  • pathOrIOHandler (string|io.IOHandler) Can be either of the two formats

    1. A string path to the ModelAndWeightsConfig JSON describing the model in the canonical TensorFlow.js format. For file:// (tfjs-node-only), http:// and https:// schemas, the path can be either absolute or relative.
    2. An tf.io.IOHandler object that loads model artifacts with its load method.
  • options (Object) Optional configuration arguments for the model loading, including:

    • strict: Require that the provided weights exactly match those required by the layers. Default true. Passing false means that both extra weights and missing weights will be silently ignored.
    • `onProgress`: A function of the signature `(fraction: number) => void',
    • that can be used as the progress callback for the model loading.
                    
    • @returns A Promise of tf.LayersModel, with the topology and weights
    • loaded.
                    
    Optional
  • requestInit (RequestInit) RequestInit (options) for HTTP requests.

    For detailed information on the supported fields, see https://developer.mozilla.org/en-US/docs/Web/API/Request/Request

  • onProgress (OnProgressCallback) Progress callback.
  • fetchFunc (Function) A function used to override the window.fetch function.
  • strict (boolean) Strict loading model: whether extraneous weights or missing weights should trigger an Error.

    If true, require that the provided weights exactly match those required by the layers. false means that both extra weights and missing weights will be silently ignored.

    Default: true.

  • weightPathPrefix (string) Path prefix for weight files, by default this is calculated from the path of the model JSON file.

    For instance, if the path to the model JSON file is http://localhost/foo/model.json, then the default path prefix will be http://localhost/foo/. If a weight file has the path value group1-shard1of2 in the weight manifest, then the weight file will be loaded from http://localhost/foo/group1-shard1of2 by default. However, if you provide a weightPathPrefix value of http://localhost/foo/alt-weights, then the weight file will be loaded from the path http://localhost/foo/alt-weights/group1-shard1of2 instead.

  • fromTFHub (boolean) Whether the module or model is to be loaded from TF Hub.

    Setting this to true allows passing a TF-Hub module URL, omitting the standard model file name and the query parameters.

    Default: false.

Returns: Promise<tf.LayersModel>
tf.io.browserDownloads (fileNamePrefix?) function Source

Creates an IOHandler that triggers file downloads from the browser.

The returned IOHandler instance can be used as model exporting methods such as tf.Model.save and supports only saving.

const model = tf.sequential();
model.add(tf.layers.dense(
    {units: 1, inputShape: [10], activation: 'sigmoid'}));
const saveResult = await model.save('downloads://mymodel'));
// This will trigger downloading of two files:
//   'mymodel.json' and 'mymodel.weights.bin'.
console.log(saveResult);
Parameters:
  • fileNamePrefix (string) Prefix name of the files to be downloaded. For use with tf.Model, fileNamePrefix should follow either of the following two formats:

    1. null or undefined, in which case the default file names will be used:
    • 'model.json' for the JSON file containing the model topology and weights manifest.
    • 'model.weights.bin' for the binary file containing the binary weight values.
    1. A single string or an Array of a single string, as the file name prefix. For example, if 'foo' is provided, the downloaded JSON file and binary weights file will be named 'foo.json' and 'foo.weights.bin', respectively.
    Optional
Returns: IOHandler
tf.io.browserFiles (files) function Source

Creates an IOHandler that loads model artifacts from user-selected files.

This method can be used for loading from files such as user-selected files in the browser. When used in conjunction with tf.loadModel, an instance of tf.Model (Keras-style) can be constructed from the loaded artifacts.

// Note: This code snippet won't run properly without the actual file input
//   elements in the HTML DOM.

// Suppose there are two HTML file input (`<input type="file" ...>`)
// elements.
const uploadJSONInput = document.getElementById('upload-json');
const uploadWeightsInput = document.getElementById('upload-weights');
const model = await tfl.loadModel(tf.io.browserFiles(
    [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));
Parameters:
  • files (File[]) Files to load from. Currently, this function supports only loading from files that contain Keras-style models (i.e., tf.Models), for which an Array of Files is expected (in that order):

    • A JSON file containing the model topology and weight manifest.
    • Optionally, One or more binary files containing the binary weights. These files must have names that match the paths in the weightsManifest contained by the aforementioned JSON file, or errors will be thrown during loading. These weights files have the same format as the ones generated by tensorflowjs_converter that comes with the tensorflowjs Python PIP package. If no weights files are provided, only the model topology will be loaded from the JSON file above.
Returns: IOHandler
tf.io.browserHTTPRequest (path, loadOptions?) function Source

Creates an IOHandler subtype that sends model artifacts to HTTP server.

An HTTP request of the multipart/form-data mime type will be sent to the path URL. The form data includes artifacts that represent the topology and/or weights of the model. In the case of Keras-style tf.Model, two blobs (files) exist in form-data:

  • A JSON file consisting of modelTopology and weightsManifest.
  • A binary weights file consisting of the concatenated weight values. These files are in the same format as the one generated by tfjs_converter.

The following code snippet exemplifies the client-side code that uses this function:

const model = tf.sequential();
model.add(
    tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));

const saveResult = await model.save(tf.io.browserHTTPRequest(
    'http://model-server:5000/upload', {method: 'PUT'}));
console.log(saveResult);

If the default POST method is to be used, without any custom parameters such as headers, you can simply pass an HTTP or HTTPS URL to model.save:

const saveResult = await model.save('http://model-server:5000/upload');

The following GitHub Gist https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 implements a server based on flask that can receive the request. Upon receiving the model artifacts via the requst, this particular server reconsistutes instances of Keras Models in memory.

Parameters:
  • path (string) A URL path to the model. Can be an absolute HTTP path (e.g., 'http://localhost:8000/model-upload)') or a relative path (e.g., './model-upload').
  • loadOptions (LoadOptions) Optional configuration for the loading. It includes the following fields:

    • weightPathPrefix Optional, this specifies the path prefix for weight files, by default this is calculated from the path param.
    • fetchFunc Optional, custom fetch function. E.g., in Node.js, the fetch from node-fetch can be used here.
    • onProgress Optional, progress callback function, fired periodically before the load is completed.
    Optional
Returns: IOHandler
tf.io.copyModel (sourceURL, destURL) function Source

Copy a model from one URL to another.

This function supports:

  1. Copying within a storage medium, e.g., tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')
  2. Copying between two storage mediums, e.g., tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')
// First create and save a model.
const model = tf.sequential();
model.add(tf.layers.dense(
    {units: 1, inputShape: [10], activation: 'sigmoid'}));
await model.save('localstorage://demo/management/model1');

// Then list existing models.
console.log(JSON.stringify(await tf.io.listModels()));

// Copy the model, from Local Storage to IndexedDB.
await tf.io.copyModel(
    'localstorage://demo/management/model1',
    'indexeddb://demo/management/model1');

// List models again.
console.log(JSON.stringify(await tf.io.listModels()));

// Remove both models.
await tf.io.removeModel('localstorage://demo/management/model1');
await tf.io.removeModel('indexeddb://demo/management/model1');
Parameters:
  • sourceURL (string) Source URL of copying.
  • destURL (string) Destination URL of copying.
Returns: Promise<ModelArtifactsInfo>

List all models stored in registered storage mediums.

For a web browser environment, the registered mediums are Local Storage and IndexedDB.

// First create and save a model.
const model = tf.sequential();
model.add(tf.layers.dense(
    {units: 1, inputShape: [10], activation: 'sigmoid'}));
await model.save('localstorage://demo/management/model1');

// Then list existing models.
console.log(JSON.stringify(await tf.io.listModels()));

// Delete the model.
await tf.io.removeModel('localstorage://demo/management/model1');

// List models again.
console.log(JSON.stringify(await tf.io.listModels()));
Returns: Promise<{[url: string]: ModelArtifactsInfo}>
tf.io.moveModel (sourceURL, destURL) function Source

Move a model from one URL to another.

This function supports:

  1. Moving within a storage medium, e.g., tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')
  2. Moving between two storage mediums, e.g., tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')
// First create and save a model.
const model = tf.sequential();
model.add(tf.layers.dense(
    {units: 1, inputShape: [10], activation: 'sigmoid'}));
await model.save('localstorage://demo/management/model1');

// Then list existing models.
console.log(JSON.stringify(await tf.io.listModels()));

// Move the model, from Local Storage to IndexedDB.
await tf.io.moveModel(
    'localstorage://demo/management/model1',
    'indexeddb://demo/management/model1');

// List models again.
console.log(JSON.stringify(await tf.io.listModels()));

// Remove the moved model.
await tf.io.removeModel('indexeddb://demo/management/model1');
Parameters:
  • sourceURL (string) Source URL of moving.
  • destURL (string) Destination URL of moving.
Returns: Promise<ModelArtifactsInfo>
tf.io.removeModel (url) function Source

Remove a model specified by URL from a reigstered storage medium.

// First create and save a model.
const model = tf.sequential();
model.add(tf.layers.dense(
    {units: 1, inputShape: [10], activation: 'sigmoid'}));
await model.save('localstorage://demo/management/model1');

// Then list existing models.
console.log(JSON.stringify(await tf.io.listModels()));

// Delete the model.
await tf.io.removeModel('localstorage://demo/management/model1');

// List models again.
console.log(JSON.stringify(await tf.io.listModels()));
Parameters:
  • url (string) A URL to a stored model, with a scheme prefix, e.g., 'localstorage://my-model-1', 'indexeddb://my/model/2'.
Returns: Promise<ModelArtifactsInfo>
tf.registerClass (cls) function Source

Register a class with the serialization map of TensorFlow.js.

This is often used for registering custom Layers, so they can be serialized and deserialized.

Example:

class MyCustomLayer extends tf.layers.Layer {
  static className = 'MyCustomLayer';

  constructor(config) {
    super(config);
  }
}
tf.serialization.registerClass(MyCustomLayer);
Parameters:
  • cls (SerializableConstructor) The class to be registered. It must have a public static member called className defined and the value must be a non-empty string.
Returns: void
tf.GraphModel extends InferenceModel class Source

A tf.GraphModel is a directed, acyclic graph of built from SavedModel GraphDef and allows inference exeuction.

A tf.GraphModel can only be created by loading from a model converted from a TensorFlow SavedModel using the command line converter tool and loaded via tf.loadGraphModel().

predict (inputs, config?) method Source

Execute the inference for the input tensors.

Parameters:
  • inputs (tf.Tensor|tf.Tensor[]|{[name: string]: tf.Tensor})
  • config (Object) Prediction configuration for specifying the batch size and output node names. Currently the batch size option is ignored for graph model. Optional
  • batchSize (number) Optional. Batch size (Integer). If unspecified, it will default to 32.
  • verbose (boolean) Optional. Verbosity mode. Defaults to false.
Returns: tf.Tensor|tf.Tensor[]|{[name: string]: tf.Tensor}
execute (inputs, outputs?) method Source

Executes inference for the model for given input tensors.

Parameters:
  • inputs (tf.Tensor|tf.Tensor[]|{[name: string]: tf.Tensor}) tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.
  • outputs (string|string[]) output node name from the Tensorflow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array. Optional
Returns: tf.Tensor|tf.Tensor[]
executeAsync (inputs, outputs?) method Source

Executes inference for the model for given input tensors in async fashion, use this method when your model contains control flow ops.

Parameters:
  • inputs (tf.Tensor|tf.Tensor[]|{[name: string]: tf.Tensor}) tensor, tensor array or tensor map of the inputs for the model, keyed by the input node names.
  • outputs (string|string[]) output node name from the Tensorflow model, if no outputs are specified, the default outputs of the model would be used. You can inspect intermediate nodes of the model by adding them to the outputs array. Optional
Returns: Promise<tf.Tensor|tf.Tensor[]>
dispose () method Source

Releases the memory used by the weight tensors.

Returns: void
tf.LayersModel extends Container|tfc.InferenceModel class Source

A tf.LayersModel is a directed, acyclic graph of tf.Layers plus methods for training, evaluation, prediction and saving.

tf.LayersModel is the basic unit of training, inference and evaluation in TensorFlow.js. To create a tf.LayersModel, use tf.LayersModel.

See also: tf.Sequential, tf.loadLayersModel().

summary (lineLength?, positions?, printFn?) method Source

Print a text summary of the model's layers.

The summary includes

  • Name and type of all layers that comprise the model.
  • Output shape(s) of the layers
  • Number of weight parameters of each layer
  • If the model has non-sequential-like topology, the inputs each layer receives
  • The total number of trainable and non-trainable parameters of the model.
const input1 = tf.input({shape: [10]});
const input2 = tf.input({shape: [20]});
const dense1 = tf.layers.dense({units: 4}).apply(input1);
const dense2 = tf.layers.dense({units: 8}).apply(input2);
const concat = tf.layers.concatenate().apply([dense1, dense2]);
const output =
    tf.layers.dense({units: 3, activation: 'softmax'}).apply(concat);

const model = tf.model({inputs: [input1, input2], outputs: output});
model.summary();
Parameters:
  • lineLength (number) Custom line length, in number of characters. Optional
  • positions (number[]) Custom widths of each of the columns, as either fractions of lineLength (e.g., [0.5, 0.75, 1]) or absolute number of characters (e.g., [30, 50, 65]). Each number corresponds to right-most (i.e., ending) position of a column. Optional
  • printFn ((message?: tf.any(), ...optionalParams: tf.any()[]) => void) Custom print function. Can be used to replace the default console.log. For example, you can use x => {} to mute the printed messages in the console. Optional
Returns: void
compile (args) method Source

Configures and prepares the model for training and evaluation. Compiling outfits the model with an optimizer, loss, and/or metrics. Calling fit or evaluate on an un-compiled model will throw an error.

Parameters:
  • args (Object) a ModelCompileArgs specifying the loss, optimizer, and metrics to be used for fitting and evaluating this model.
  • optimizer (string|tf.train.Optimizer) An instance of tf.train.Optimizer or a string name for an Optimizer.
  • loss (string|string[]|{[outputName: string]: string}|LossOrMetricFn| LossOrMetricFn[]|{[outputName: string]: LossOrMetricFn}) Object function(s) or name(s) of object function(s). If the model has multiple outputs, you can use a different loss on each output by passing a dictionary or an Array of losses. The loss value that will be minimized by the model will then be the sum of all individual losses.
  • metrics (string[]|{[outputName: string]: string}) List of metrics to be evaluated by the model during training and testing. Typically you will use metrics=['accuracy']. To specify different metrics for different outputs of a multi-output model, you could also pass a dictionary.
Returns: void
evaluate (x, y, args?) method Source

Returns the loss value & metrics values for the model in test mode.

Loss and metrics are specified during compile(), which needs to happen before calls to evaluate().

Computation is done in batches.

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
const result = model.evaluate(
    tf.ones([8, 10]), tf.ones([8, 1]), {batchSize: 4});
result.print();
Parameters:
  • x (tf.Tensor|tf.Tensor[]) tf.Tensor of test data, or an Array of tf.Tensors if the model has multiple inputs.
  • y (tf.Tensor|tf.Tensor[]) tf.Tensor of target data, or an Array of tf.Tensors if the model has multiple outputs.
  • args (Object) A ModelEvaluateArgs, containing optional fields. Optional
  • batchSize (number) Batch size (Integer). If unspecified, it will default to 32.
  • verbose (ModelLoggingVerbosity) Verbosity mode.
  • sampleWeight (tf.Tensor) Tensor of weights to weight the contribution of different samples to the loss and metrics.
  • steps (number) integer: total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of undefined.
Returns: tf.Scalar|tf.Scalar[]
evaluateDataset (dataset, args?) method Source

Evaluate model using a dataset object.

Note: Unlike evaluate(), this method is asynchronous (async);

Parameters:
  • dataset (tf.data.Dataset) A dataset object. Its iterator() method is expected to generate a dataset iterator object, the next() method of which is expected to produce data batches for evaluation. The return value of the next() call ought to contain a boolean done field and a value field. The value field is expected to be an array of two tf.Tensors or an array of two nested tf.Tensor structures. The former case is for models with exactly one input and one output (e.g.. a sequential model). The latter case is for models with multiple inputs and/or multiple outputs. Of the two items in the array, the first is the input feature(s) and the second is the output target(s).
  • args (Object) A configuration object for the dataset-based evaluation. Optional
  • batches (number) Number of batches to draw from the dataset object before ending the evaluation.
  • verbose (ModelLoggingVerbosity) Verbosity mode.
Returns: Promise<tf.Scalar|tf.Scalar[]>
predict (x, args?) method Source

Generates output predictions for the input samples.

Computation is done in batches.

Note: the "step" mode of predict() is currently not supported. This is because the TensorFlow.js core backend is imperative only.

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.predict(tf.ones([8, 10]), {batchSize: 4}).print();
Parameters:
  • x (tf.Tensor|tf.Tensor[]) The input data, as an Tensor, or an Array of tf.Tensors if the model has multiple inputs.
  • args (Object) A ModelPredictArgs object containing optional fields. Optional
  • batchSize (number) Optional. Batch size (Integer). If unspecified, it will default to 32.
  • verbose (boolean) Optional. Verbosity mode. Defaults to false.
Returns: tf.Tensor|tf.Tensor[]

Returns predictions for a single batch of samples.

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.predictOnBatch(tf.ones([8, 10])).print();
Parameters:
  • x (tf.Tensor) : Input samples, as an Tensor
Returns: tf.Tensor|tf.Tensor[]
fit (x, y, args?) method Source

Trains the model for a fixed number of epochs (iterations on a dataset).

const model = tf.sequential({
    layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
for (let i = 1; i < 5 ; ++i) {
  const h = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
      batchSize: 4,
      epochs: 3
  });
  console.log("Loss after Epoch " + i + " : " + h.history.loss[0]);
}
Parameters:
  • x (tf.Tensor|tf.Tensor[]|{[inputName: string]: tf.Tensor}) tf.Tensor of training data, or an array of tf.Tensors if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to tf.Tensors.
  • y (tf.Tensor|tf.Tensor[]|{[inputName: string]: tf.Tensor}) tf.Tensor of target (label) data, or an array of tf.Tensors if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to tf.Tensors.
  • args (Object) A ModelFitArgs, containing optional fields. Optional
  • batchSize (number) Number of samples per gradient update. If unspecified, it will default to 32.
  • epochs (number) The number of times to iterate over the training data arrays.
  • verbose (ModelLoggingVerbosity) Verbosity level.

    Expected to be 0, 1, or 2. Default: 1.

    0 - No printed message during fit() call. 1 - In Node.js (tfjs-node), prints the progress bar, together with real-time updates of loss and metric values and training speed. In the browser: no action. This is the default. 2 - Not implemented yet.

  • callbacks (BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[]) List of callbacks to be called during training. Can consist of one or more of the following fields: onTrainBegin, onTrainEnd, onEpochBegin, onEpochEnd, onBatchBegin, onBatchEnd.
  • validationSplit (number) Float between 0 and 1: fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling.
  • validationData ([ tf.Tensor|tf.Tensor[], tf.Tensor|tf.Tensor[] ]|[tf.Tensor | tf.Tensor[], tf.Tensor|tf.Tensor[], tf.Tensor|tf.Tensor[]]) Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be a tuple [xVal, yVal] or a tuple [xVal, yVal, valSampleWeights]. The model will not be trained on this data. validationData will override validationSplit.
  • shuffle (boolean) Whether to shuffle the training data before each epoch. Has no effect when stepsPerEpoch is not null.
  • classWeight ({[classIndex: string]: number}) Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
  • sampleWeight (tf.Tensor) Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequenceLength), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sampleWeightMode="temporal" in compile().
  • initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run).
  • stepsPerEpoch (number) Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with Input Tensors such as TensorFlow data tensors, the default null is equal to the number of unique samples in your dataset divided by the batch size, or 1 if that cannot be determined.
  • validationSteps (number) Only relevant if stepsPerEpoch is specified. Total number of steps (batches of samples) to validate before stopping.
  • yieldEvery (YieldEveryOptions) Configures the frequency of yielding the main thread to other tasks.

    In the browser environment, yielding the main thread can improve the responsiveness of the page during training. In the Node.js environment, it can ensure tasks queued in the event loop can be handled in a timely manner.

    • The value can be one of the following strings:
      • 'auto': automatically determine how frequently the yielding happens by measuring the duration of each batch of training (default).
      • 'batch': yield every batch.
      • 'epoch': yield every epoch.
      • 'never': never yield. (But yielding can still happen through `await
      •  nextFrame()` calls in custom callbacks.)
Returns: Promise<History>
fitDataset (dataset, args) method Source

Trains the model using a dataset object.

Parameters:
  • dataset (tf.data.Dataset) A dataset object. Its iterator() method is expected to generate a dataset iterator object, the next() method of which is expected to produce data batches for training. The return value of the next() call ought to contain a boolean done field and a value field. The value field is expected to be an array of two tf.Tensors or an array of two nested tf.Tensor structures. The former case is for models with exactly one input and one output (e.g.. a sequential model). The latter case is for models with multiple inputs and/or multiple outputs. Of the two items in the array, the first is the input feature(s) and the second is the output target(s).
  • args (Object) A ModelFitDatasetArgs, containing optional fields.
  • batchesPerEpoch (number) (Optional) Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size, so that fitDataset() call can utilize the entire dataset. If it is not provided, use done return value in iterator.next() as signal to finish an epoch.
  • epochs (number) The number of times to iterate over the training dataset.

    An integer.

  • verbose (ModelLoggingVerbosity) Verbosity level.

    Expected to be 0, 1, or 2. Default: 1.

    0 - No printed message during fit() call. 1 - In Node.js (tfjs-node), prints the progress bar, together with real-time updates of loss and metric values and training speed. In the browser: no action. This is the default. 2 - Not implemented yet.

  • callbacks (BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[]) List of callbacks to be called during training. Can consist of one or more of the following fields: onTrainBegin, onTrainEnd, onEpochBegin, onEpochEnd, onBatchBegin, onBatchEnd.
  • validationData ([ TensorOrArrayOrMap, TensorOrArrayOrMap ]|[TensorOrArrayOrMap, TensorOrArrayOrMap, TensorOrArrayOrMap]|tf.data.Dataset) Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be any of the following:

    • An array [xVal, yVal], where the two values may be tf.Tensor, an array of Tensors, or a map of string to Tensor.
    • Similarly, an array [xVal, yVal, valSampleWeights] (not implemented yet).
    • a Dataset object with elements of the form {xs: xVal, ys: yVal}, where xs and ys are the feature and label tensors, respectively.

    If validationData is an Array of Tensor objects, each tf.Tensor will be sliced into batches during validation, using the parameter validationBatchSize (which defaults to 32). The entirety of the tf.Tensor objects will be used in the validation.

    If validationData is a dataset object, and the validationBatches parameter is specified, the validation will use validationBatches batches drawn from the dataset object. If validationBatches parameter is not specified, the validation will stop when the dataset is exhausted.

    The model will not be trained on this data.

  • validationBatchSize (number) Optional batch size for validation.

    Used only if validationData is an array of tf.Tensor objects, i.e., not a dataset object.

    If not specified, its value defaults to 32.

  • validationBatches (number) (Optional) Only relevant if validationData is specified and is a dataset object.

    Total number of batches of samples to draw from validationData for validation purpose before stopping at the end of every epoch. If not specified, evaluateDataset will use iterator.next().done as signal to stop validation.

  • yieldEvery (YieldEveryOptions) Configures the frequency of yielding the main thread to other tasks.

    In the browser environment, yielding the main thread can improve the responsiveness of the page during training. In the Node.js environment, it can ensure tasks queued in the event loop can be handled in a timely manner.

    • The value can be one of the following strings:
      • 'auto': automatically determine how frequently the yielding happens by measuring the duration of each batch of training (default).
      • 'batch': yield every batch.
      • 'epoch': yield every epoch.
      • 'never': never yield. (But yielding can still happen through `await
      •  nextFrame()` calls in custom callbacks.)
  • initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run).
Returns: Promise<History>
trainOnBatch (x, y) method Source

Runs a single gradient update on a single batch of data.

This method differs from fit() and fitDataset() in the following regards:

  • It operates on exactly one batch of data.
  • It returns only the loss and matric values, instead of returning the batch-by-batch loss and metric values.
  • It doesn't support fine-grained options such as verbosity and callbacks.
Parameters:
Returns: Promise<number|number[]>
save (handlerOrURL, config?) method Source

Save the configuration and/or weights of the LayersModel.

An IOHandler is an object that has a save method of the proper signature defined. The save method manages the storing or transmission of serialized data ("artifacts") that represent the model's topology and weights onto or via a specific medium, such as file downloads, local storage, IndexedDB in the web browser and HTTP requests to a server. TensorFlow.js provides IOHandler implementations for a number of frequently used saving mediums, such as tf.io.browserDownloads() and tf.io.browserLocalStorage. See tf.io for more details.

This method also allows you to refer to certain types of IOHandlers as URL-like string shortcuts, such as 'localstorage://' and 'indexeddb://'.

Example 1: Save model's topology and weights to browser local storage; then load it back.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
console.log('Prediction from original model:');
model.predict(tf.ones([1, 3])).print();

const saveResults = await model.save('localstorage://my-model-1');

const loadedModel = await tf.loadLayersModel('localstorage://my-model-1');
console.log('Prediction from loaded model:');
loadedModel.predict(tf.ones([1, 3])).print();

Example 2. Saving model's topology and weights to browser IndexedDB; then load it back.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
console.log('Prediction from original model:');
model.predict(tf.ones([1, 3])).print();

const saveResults = await model.save('indexeddb://my-model-1');

const loadedModel = await tf.loadLayersModel('indexeddb://my-model-1');
console.log('Prediction from loaded model:');
loadedModel.predict(tf.ones([1, 3])).print();

Example 3. Saving model's topology and weights as two files (my-model-1.json and my-model-1.weights.bin) downloaded from browser.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
const saveResults = await model.save('downloads://my-model-1');

Example 4. Send model's topology and weights to an HTTP server. See the documentation of tf.io.browserHTTPRequest() for more details including specifying request parameters and implementation of the server.

const model = tf.sequential(
    {layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
const saveResults = await model.save('http://my-server/model/upload');
Parameters:
  • handlerOrURL (io.IOHandler|string) An instance of IOHandler or a URL-like, scheme-based string shortcut for IOHandler.
  • config (Object) Options for saving the model. Optional
  • trainableOnly (boolean) Whether to save only the trainable weights of the model, ignoring the untrainable ones.
Returns: Promise<io.SaveResult>
getLayer (name?, index?) method Source

Retrieves a layer based on either its name (unique) or index.

Indices are based on order of horizontal graph traversal (bottom-up).

If both name and index are specified, index takes precedence.

Parameters:
  • name (string) Name of layer. Optional
  • index (number) Index of layer. Optional
Returns: tf.layers.Layer

A model with a stack of layers, feeding linearly from one to the next.

tf.sequential() is a factory function that creates an instance of tf.Sequential.

 // Define a model for linear regression.
 const model = tf.sequential();
 model.add(tf.layers.dense({units: 1, inputShape: [1]}));

 // Prepare the model for training: Specify the loss and the optimizer.
 model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});

 // Generate some synthetic data for training.
 const xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
 const ys = tf.tensor2d([1, 3, 5, 7], [4, 1]);

 // Train the model using the data then do inference on a data point the
 // model hasn't seen:
 await model.fit(xs, ys);
 model.predict(tf.tensor2d([5], [1, 1])).print();
add (layer) method Source

Adds a layer instance on top of the layer stack.

 const model = tf.sequential();
 model.add(tf.layers.dense({units: 8, inputShape: [1]}));
 model.add(tf.layers.dense({units: 4, activation: 'relu6'}));
 model.add(tf.layers.dense({units: 1, activation: 'relu6'}));
 // Note that the untrained model is random at this point.
 model.predict(tf.randomNormal([10, 1])).print();
Parameters:
Returns: void
summary (lineLength?, positions?, printFn?) method Source

Print a text summary of the Sequential model's layers.

The summary includes

  • Name and type of all layers that comprise the model.
  • Output shape(s) of the layers
  • Number of weight parameters of each layer
  • The total number of trainable and non-trainable parameters of the model.
const model = tf.sequential();
model.add(
    tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));
model.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));

model.summary();
Parameters:
  • lineLength (number) Custom line length, in number of characters. Optional
  • positions (number[]) Custom widths of each of the columns, as either fractions of lineLength (e.g., [0.5, 0.75, 1]) or absolute number of characters (e.g., [30, 50, 65]). Each number corresponds to right-most (i.e., ending) position of a column. Optional
  • printFn ((message?: tf.any(), ...optionalParams: tf.any()[]) => void) Custom print function. Can be used to replace the default console.log. For example, you can use x => {} to mute the printed messages in the console. Optional
Returns: void
evaluate (x, y, args?) method Source

Returns the loss value & metrics values for the model in test mode.

Loss and metrics are specified during compile(), which needs to happen before calls to evaluate().

Computation is done in batches.

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
const result = model.evaluate(tf.ones([8, 10]), tf.ones([8, 1]), {
  batchSize: 4,
});
result.print();
Parameters:
  • x (tf.Tensor|tf.Tensor[]) tf.Tensor of test data, or an Array of tf.Tensors if the model has multiple inputs.
  • y (tf.Tensor|tf.Tensor[]) tf.Tensor of target data, or an Array of tf.Tensors if the model has multiple outputs.
  • args (Object) A ModelEvaluateConfig, containing optional fields. Optional
  • batchSize (number) Batch size (Integer). If unspecified, it will default to 32.
  • verbose (ModelLoggingVerbosity) Verbosity mode.
  • sampleWeight (tf.Tensor) Tensor of weights to weight the contribution of different samples to the loss and metrics.
  • steps (number) integer: total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of undefined.
Returns: tf.Scalar|tf.Scalar[]
evaluateDataset (dataset, args) method Source

Evaluate model using a dataset object.

Note: Unlike evaluate(), this method is asynchronous (async);

Parameters:
  • dataset (tf.data.Dataset) A dataset object. Its iterator() method is expected to generate a dataset iterator object, the next() method of which is expected to produce data batches for evaluation. The return value of the next() call ought to contain a boolean done field and a value field. The value field is expected to be an array of two tf.Tensors or an array of two nested tf.Tensor structures. The former case is for models with exactly one input and one output (e.g.. a sequential model). The latter case is for models with multiple inputs and/or multiple outputs. Of the two items in the array, the first is the input feature(s) and the second is the output target(s).
  • args (Object) A configuration object for the dataset-based evaluation.
  • batches (number) Number of batches to draw from the dataset object before ending the evaluation.
  • verbose (ModelLoggingVerbosity) Verbosity mode.
Returns: Promise<tf.Scalar|tf.Scalar[]>
predict (x, args?) method Source

Generates output predictions for the input samples.

Computation is done in batches.

Note: the "step" mode of predict() is currently not supported. This is because the TensorFow.js core backend is imperative only.

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.predict(tf.ones([2, 10])).print();
Parameters:
  • x (tf.Tensor|tf.Tensor[]) The input data, as an Tensor, or an Array of tf.Tensors if the model has multiple inputs.
  • args (Object) Optional
  • batchSize (number) Optional. Batch size (Integer). If unspecified, it will default to 32.
  • verbose (boolean) Optional. Verbosity mode. Defaults to false.
Returns: tf.Tensor|tf.Tensor[]
fit (x, y, args?) method Source

Trains the model for a fixed number of epochs (iterations on a dataset).

const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [10]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
const history = await model.fit(tf.ones([8, 10]), tf.ones([8, 1]), {
  batchSize: 4,
  epochs: 3
});
console.log(history.history.loss[0]);
Parameters:
  • x (tf.Tensor|tf.Tensor[]|{[inputName: string]: tf.Tensor}) tf.Tensor of training data, or an array of tf.Tensors if the model has multiple inputs. If all inputs in the model are named, you can also pass a dictionary mapping input names to tf.Tensors.
  • y (tf.Tensor|tf.Tensor[]|{[inputName: string]: tf.Tensor}) tf.Tensor of target (label) data, or an array of tf.Tensors if the model has multiple outputs. If all outputs in the model are named, you can also pass a dictionary mapping output names to tf.Tensors.
  • args (Object) A ModelFitConfig, containing optional fields. Optional
  • batchSize (number) Number of samples per gradient update. If unspecified, it will default to 32.
  • epochs (number) The number of times to iterate over the training data arrays.
  • verbose (ModelLoggingVerbosity) Verbosity level.

    Expected to be 0, 1, or 2. Default: 1.

    0 - No printed message during fit() call. 1 - In Node.js (tfjs-node), prints the progress bar, together with real-time updates of loss and metric values and training speed. In the browser: no action. This is the default. 2 - Not implemented yet.

  • callbacks (BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[]) List of callbacks to be called during training. Can consist of one or more of the following fields: onTrainBegin, onTrainEnd, onEpochBegin, onEpochEnd, onBatchBegin, onBatchEnd.
  • validationSplit (number) Float between 0 and 1: fraction of the training data to be used as validation data. The model will set apart this fraction of the training data, will not train on it, and will evaluate the loss and any model metrics on this data at the end of each epoch. The validation data is selected from the last samples in the x and y data provided, before shuffling.
  • validationData ([ tf.Tensor|tf.Tensor[], tf.Tensor|tf.Tensor[] ]|[tf.Tensor | tf.Tensor[], tf.Tensor|tf.Tensor[], tf.Tensor|tf.Tensor[]]) Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be a tuple [xVal, yVal] or a tuple [xVal, yVal, valSampleWeights]. The model will not be trained on this data. validationData will override validationSplit.
  • shuffle (boolean) Whether to shuffle the training data before each epoch. Has no effect when stepsPerEpoch is not null.
  • classWeight ({[classIndex: string]: number}) Optional dictionary mapping class indices (integers) to a weight (float) to apply to the model's loss for the samples from this class during training. This can be useful to tell the model to "pay more attention" to samples from an under-represented class.
  • sampleWeight (tf.Tensor) Optional array of the same length as x, containing weights to apply to the model's loss for each sample. In the case of temporal data, you can pass a 2D array with shape (samples, sequenceLength), to apply a different weight to every timestep of every sample. In this case you should make sure to specify sampleWeightMode="temporal" in compile().
  • initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run).
  • stepsPerEpoch (number) Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. When training with Input Tensors such as TensorFlow data tensors, the default null is equal to the number of unique samples in your dataset divided by the batch size, or 1 if that cannot be determined.
  • validationSteps (number) Only relevant if stepsPerEpoch is specified. Total number of steps (batches of samples) to validate before stopping.
  • yieldEvery (YieldEveryOptions) Configures the frequency of yielding the main thread to other tasks.

    In the browser environment, yielding the main thread can improve the responsiveness of the page during training. In the Node.js environment, it can ensure tasks queued in the event loop can be handled in a timely manner.

    • The value can be one of the following strings:
      • 'auto': automatically determine how frequently the yielding happens by measuring the duration of each batch of training (default).
      • 'batch': yield every batch.
      • 'epoch': yield every epoch.
      • 'never': never yield. (But yielding can still happen through `await
      •  nextFrame()` calls in custom callbacks.)
Returns: Promise<History>
fitDataset (dataset, args) method Source

Trains the model using a dataset object.

const xArray = [
  [1, 1, 1, 1, 1, 1, 1, 1, 1],
  [1, 1, 1, 1, 1, 1, 1, 1, 1],
  [1, 1, 1, 1, 1, 1, 1, 1, 1],
  [1, 1, 1, 1, 1, 1, 1, 1, 1],
];
const yArray = [1, 1, 1, 1];
// Create a dataset from the JavaScript array.
const xDataset = tf.data.array(xArray);
const yDataset = tf.data.array(yArray);
// Zip combines the `x` and `y` Datasets into a single Dataset, the
// iterator of which will return an object containing of two tensors,
// corresponding to `x` and `y`.  The call to `batch(4)` will bundle
// four such samples into a single object, with the same keys now pointing
// to tensors that hold 4 examples, organized along the batch dimension.
// The call to `shuffle(4)` causes each iteration through the dataset to
// happen in a different order.  The size of the shuffle window is 4.
const xyDataset = tf.data.zip({xs: xDataset, ys: yDataset})
    .batch(4)
    .shuffle(4);
const model = tf.sequential({
  layers: [tf.layers.dense({units: 1, inputShape: [9]})]
});
model.compile({optimizer: 'sgd', loss: 'meanSquaredError'});
const history = await model.fitDataset(xyDataset, {
  epochs: 4,
  callbacks: {onEpochEnd: (epoch, logs) => console.log(logs.loss)}
});
Parameters:
  • dataset (tf.data.Dataset) A dataset object. Its iterator() method is expected to generate a dataset iterator object, the next() method of which is expected to produce data batches for evaluation. The return value of the next() call ought to contain a boolean done field and a value field.

    The value field is expected to be an object of with fields xs and ys, which point to the feature tensor and the target tensor, respectively. This case is for models with exactly one input and one output (e.g.. a sequential model). For example:

       *   {value: {xs: xsTensor, ys: ysTensor}, done: false}
                     *   ```
                  
                  If the model has multiple inputs, the `xs` field of `value` should
                  be an object mapping input names to their respective feature tensors.
                  For example:
                  ```js
                     *   {
                     *     value: {
                     *       xs: {
                     *         input_1: xsTensor1,
                     *         input_2: xsTensor2
                     *       },
                     *       ys: ysTensor
                     *     },
                     *     done: false
                     *   }
                     *   ```
                  If the model has multiple outputs, the `ys` field of `value` should
                  be an object mapping output names to their respective target tensors.
                  For example:
                  ```js
                     *   {
                     *     value: {
                     *       xs: xsTensor,
                     *       ys: {
                     *         output_1: ysTensor1,
                     *         output_2: ysTensor2
                     *       },
                     *     },
                     *     done: false
                     *   }
                     *   ```
  • args (Object) A ModelFitDatasetArgs, containing optional fields.
  • batchesPerEpoch (number) (Optional) Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples of your dataset divided by the batch size, so that fitDataset() call can utilize the entire dataset. If it is not provided, use done return value in iterator.next() as signal to finish an epoch.
  • epochs (number) The number of times to iterate over the training dataset.

    An integer.

  • verbose (ModelLoggingVerbosity) Verbosity level.

    Expected to be 0, 1, or 2. Default: 1.

    0 - No printed message during fit() call. 1 - In Node.js (tfjs-node), prints the progress bar, together with real-time updates of loss and metric values and training speed. In the browser: no action. This is the default. 2 - Not implemented yet.

  • callbacks (BaseCallback[]|CustomCallbackArgs|CustomCallbackArgs[]) List of callbacks to be called during training. Can consist of one or more of the following fields: onTrainBegin, onTrainEnd, onEpochBegin, onEpochEnd, onBatchBegin, onBatchEnd.
  • validationData ([ TensorOrArrayOrMap, TensorOrArrayOrMap ]|[TensorOrArrayOrMap, TensorOrArrayOrMap, TensorOrArrayOrMap]|tf.data.Dataset) Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. This could be any of the following:

    • An array [xVal, yVal], where the two values may be tf.Tensor, an array of Tensors, or a map of string to Tensor.
    • Similarly, an array [xVal, yVal, valSampleWeights] (not implemented yet).
    • a Dataset object with elements of the form {xs: xVal, ys: yVal}, where xs and ys are the feature and label tensors, respectively.

    If validationData is an Array of Tensor objects, each tf.Tensor will be sliced into batches during validation, using the parameter validationBatchSize (which defaults to 32). The entirety of the tf.Tensor objects will be used in the validation.

    If validationData is a dataset object, and the validationBatches parameter is specified, the validation will use validationBatches batches drawn from the dataset object. If validationBatches parameter is not specified, the validation will stop when the dataset is exhausted.

    The model will not be trained on this data.

  • validationBatchSize (number) Optional batch size for validation.

    Used only if validationData is an array of tf.Tensor objects, i.e., not a dataset object.

    If not specified, its value defaults to 32.

  • validationBatches (number) (Optional) Only relevant if validationData is specified and is a dataset object.

    Total number of batches of samples to draw from validationData for validation purpose before stopping at the end of every epoch. If not specified, evaluateDataset will use iterator.next().done as signal to stop validation.

  • yieldEvery (YieldEveryOptions) Configures the frequency of yielding the main thread to other tasks.

    In the browser environment, yielding the main thread can improve the responsiveness of the page during training. In the Node.js environment, it can ensure tasks queued in the event loop can be handled in a timely manner.

    • The value can be one of the following strings:
      • 'auto': automatically determine how frequently the yielding happens by measuring the duration of each batch of training (default).
      • 'batch': yield every batch.
      • 'epoch': yield every epoch.
      • 'never': never yield. (But yielding can still happen through `await
      •  nextFrame()` calls in custom callbacks.)
  • initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run).
Returns: Promise<History>
trainOnBatch (x, y) method Source

Runs a single gradient update on a single batch of data.

This method differs from fit() and fitDataset() in the following regards:

  • It operates on exactly one batch of data.
  • It returns only the loss and matric values, instead of returning the batch-by-batch loss and metric values.
  • It doesn't support fine-grained options such as verbosity and callbacks.
Parameters:
Returns: Promise<number|number[]>

tf.SymbolicTensor is a placeholder for a Tensor without any concrete value.

They are most often encountered when building a graph of Layers for a a tf.LayersModel and the input data's shape, but not values are known.

Layers are the primary building block for constructing a Model. Each layer will typically perform some computation to transform its input to its output.

Layers will automatically take care of creating and initializing the various internal variables/weights they need to function.

tf.layers.elu (args?) function Source

Exponetial Linear Unit (ELU).

It follows: f(x) = alpha * (exp(x) - 1.) for x < 0, f(x) = x for x >= 0.

Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.

Output shape: Same shape as the input.

References:

Parameters:
  • args (Object) Optional
  • alpha (number) Float >= 0. Negative slope coefficient. Defaults to 1.0.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.leakyReLU (args?) function Source

Leaky version of a rectified linear unit.

It allows a small gradient when the unit is not active: f(x) = alpha * x for x < 0. f(x) = x for x >= 0.

Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.

Output shape: Same shape as the input.

Parameters:
  • args (Object) Optional
  • alpha (number) Float >= 0. Negative slope coefficient. Defaults to 0.3.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.prelu (args?) function Source

Parameterized version of a leaky rectified linear unit.

It follows f(x) = alpha * x for x < 0. f(x) = x for x >= 0. wherein alpha is a trainable weight.

Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.

Output shape: Same shape as the input.

Parameters:
  • args (Object) Optional
  • alphaInitializer (tf.initializers.Initializer|'constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string) Initializer for the learnable alpha.
  • alphaRegularizer (Regularizer) Regularizer for the learnable alpha.
  • alphaConstraint (tf.constraints.Constraint) Constraint for the learnable alpha.
  • sharedAxes (number|number[]) The axes along which to share learnable parameters for the activation function. For example, if the incoming feature maps are from a 2D convolution with output shape [numExamples, height, width, channels], and you wish to share parameters across space (height and width) so that each filter channels has only one set of parameters, set shared_axes: [1, 2].
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.reLU (args?) function Source

Rectified Linear Unit activation function.

Input shape: Arbitrary. Use the config field inputShape (Array of integers, does not include the sample axis) when using this layer as the first layer in a model.

Output shape: Same shape as the input.

Parameters:
  • args (Object) Optional
  • maxValue (number) Float, the maximum output value.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.softmax (args?) function Source

Softmax activation layer.

Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.

Output shape: Same shape as the input.

Parameters:
  • args (Object) Optional
  • axis (number) Integer, axis along which the softmax normalization is applied. Defaults to -1 (i.e., the last axis).
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Thresholded Rectified Linear Unit.

It follows: f(x) = x for x > theta, f(x) = 0 otherwise.

Input shape: Arbitrary. Use the configuration inputShape when using this layer as the first layer in a model.

Output shape: Same shape as the input.

References:

Parameters:
  • args (Object) Optional
  • theta (number) Float >= 0. Threshold location of activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.activation (args) function Source

Applies an activation function to an output.

This layer applies element-wise activation function. Other layers, notably dense can also apply activation functions. Use this isolated activation function to extract the values before and after the activation. For instance:

const input = tf.input({shape: [5]});
const denseLayer = tf.layers.dense({units: 1});
const activationLayer = tf.layers.activation({activation: 'relu6'});

// Obtain the output symbolic tensors by applying the layers in order.
const denseOutput = denseLayer.apply(input);
const activationOutput = activationLayer.apply(denseOutput);

// Create the model based on the inputs.
const model = tf.LayersModel({
    inputs: input,
    outputs: [denseOutput, activationOutput]
});

// Collect both outputs and print separately.
const [denseOut, activationOut] = model.predict(tf.randomNormal([6, 5]));
denseOut.print();
activationOut.print();
Parameters:
  • args (Object)
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Name of the activation function to use.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.dense (args) function Source

Creates a dense (fully connected) layer.

This layer implements the operation: output = activation(dot(input, kernel) + bias)

activation is the element-wise activation function passed as the activation argument.

kernel is a weights matrix created by the layer.

bias is a bias vector created by the layer (only applicable if useBias is true).

Input shape:*

nD tf.Tensor with shape: (batchSize, ..., inputDim).

The most common situation would be a 2D input with shape (batchSize, inputDim).

Output shape:*

nD tensor with shape: (batchSize, ..., units).

For instance, for a 2D input with shape (batchSize, inputDim), the output would have shape (batchSize, units).

Note: if the input to the layer has a rank greater than 2, then it is flattened prior to the initial dot product with the kernel.

Parameters:
  • args (Object)
  • units (number) Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use.

    If unspecified, no activation is applied.

  • useBias (boolean) Whether to apply a bias.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string | tf.initializers.Initializer) Initializer for the dense kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string | tf.initializers.Initializer) Initializer for the bias vector.
  • inputDim (number) If specified, defines inputShape as [inputDim].
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string | tf.constraints.Constraint) Constraint for the kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string | tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string | Regularizer) Regularizer function applied to the dense kernel weights matrix.
  • biasRegularizer ('l1l2'|string | Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string | Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.dropout (args) function Source

Applies dropout to the input.

Dropout consists in randomly setting a fraction rate of input units to 0 at each update during training time, which helps prevent overfitting.

Parameters:
  • args (Object)
  • rate (number) Float between 0 and 1. Fraction of the input units to drop.
  • noiseShape (number[]) Integer array representing the shape of the binary dropout mask that will be multiplied with the input.

    For instance, if your inputs have shape (batchSize, timesteps, features) and you want the dropout mask to be the same for all timesteps, you can use noise_shape=(batch_size, 1, features).

  • seed (number) An integer to use as random seed.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.embedding (args) function Source

Maps positive integers (indices) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]

Input shape:* 2D tensor with shape: [batchSize, sequenceLength].

Output shape:* 3D tensor with shape: [batchSize, sequenceLength, outputDim].

Parameters:
  • args (Object)
  • inputDim (number) Integer > 0. Size of the vocabulary, i.e. maximum integer index + 1.
  • outputDim (number) Integer >= 0. Dimension of the dense embedding.
  • embeddingsInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the embeddings matrix.
  • embeddingsRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the embeddings matrix.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • embeddingsConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the embeddings matrix.
  • maskZero (boolean) Whether the input value 0 is a special "padding" value that should be masked out. This is useful when using recurrent layers which may take variable length input.

    If this is True then all subsequent layers in the model need to support masking or an exception will be raised. If maskZero is set to True, as a consequence, index 0 cannot be used in the vocabulary (inputDim should equal size of vocabulary + 1).

  • inputLength (number|number[]) Length of input sequences, when it is constant.

    This argument is required if you are going to connect flatten then dense layers upstream (without it, the shape of the dense outputs cannot be computed).

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.flatten (args?) function Source

Flattens the input. Does not affect the batch size.

A Flatten layer flattens each batch in its inputs to 1D (making the output 2D).

For example:

const input = tf.input({shape: [4, 3]});
const flattenLayer = tf.layers.flatten();
// Inspect the inferred output shape of the flatten layer, which
// equals `[null, 12]`. The 2nd dimension is 4 * 3, i.e., the result of the
// flattening. (The 1st dimension is the undermined batch size.)
console.log(JSON.stringify(flattenLayer.apply(input).shape));
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.permute (args) function Source

Permutes the dimensions of the input according to a given pattern.

Useful for, e.g., connecting RNNs and convnets together.

Example:

const model = tf.Sequential();
model.add(tf.layers.permute({
  dims: [2, 1],
  inputShape: [10, 64]
}));
console.log(model.outputShape);
// Now model's output shape is [null, 64, 10], where null is the
// unpermuted sample (batch) dimension.

Input shape: Arbitrary. Use the configuration field inputShape when using this layer as othe first layer in a model.

Output shape: Same rank as the input shape, but with the dimensions re-ordered (i.e., permuted) according to the dims configuration of this layer.

Parameters:
  • args (Object)
  • dims (number[]) Array of integers. Permutation pattern. Does not include the sample (batch) dimension. Index starts at 1. For instance, [2, 1] permutes the first and second dimensions of the input.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Repeats the input n times in a new dimension.

 const model = tf.sequential();
 model.add(tf.layers.repeatVector({n: 4, inputShape: [2]}));
 const x = tf.tensor2d([[10, 20]]);
 // Use the model to do inference on a data point the model hasn't see
 model.predict(x).print();
 // output shape is now [batch, 2, 4]
Parameters:
  • args (Object)
  • n (number) The integer number of times to repeat the input.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.reshape (args) function Source

Reshapes an input to a certain shape.

const input = tf.input({shape: [4, 3]});
const reshapeLayer = tf.layers.reshape({targetShape: [2, 6]});
// Inspect the inferred output shape of the Reshape layer, which
// equals `[null, 2, 6]`. (The 1st dimension is the undermined batch size.)
console.log(JSON.stringify(reshapeLayer.apply(input).shape));

Input shape: Arbitrary: although all dimensions in the input shape must be fixed. Use the ReshapeLayerConfig field input_shape when using this layer as the first layer in a model.

Output shape: [batchSize, targetShape[0], targetShape[1], ..., targetShape[targetShape.length - 1]].

Parameters:
  • args (Object)
  • targetShape ((null | number)[]) The target shape. Does not include the batch axis.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.conv1d (args) function Source

1D convolution layer (e.g., temporal convolution).

This layer creates a convolution kernel that is convolved with the layer input over a single spatial (or temporal) dimension to produce a tensor of outputs.

If use_bias is True, a bias vector is created and added to the outputs.

If activation is not null, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide an inputShape argument Array or null.

For example, inputShape would be:

  • [10, 128] for sequences of 10 vectors of 128-dimensional vectors
  • [null, 128] for variable-length sequences of 128-dimensional vectors.
Parameters:
  • args (Object)
  • filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
  • kernelSize (number|number[]) The dimensions of the convolution window. If kernelSize is a number, the convolutional window will be square.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.conv2d (args) function Source

2D convolution layer (e.g. spatial convolution over images).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

If useBias is True, a bias vector is created and added to the outputs.

If activation is not null, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument inputShape (Array of integers, does not include the sample axis), e.g. inputShape=[128, 128, 3] for 128x128 RGB pictures in dataFormat='channelsLast'.

Parameters:
  • args (Object)
  • filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
  • kernelSize (number|number[]) The dimensions of the convolution window. If kernelSize is a number, the convolutional window will be square.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Transposed convolutional layer (sometimes called Deconvolution).

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

When using this layer as the first layer in a model, provide the configuration inputShape (Array of integers, does not include the sample axis), e.g., inputShape: [128, 128, 3] for 128x128 RGB pictures in dataFormat: 'channelsLast'.

Input shape: 4D tensor with shape: [batch, channels, rows, cols] if dataFormat is 'channelsFirst'. or 4D tensor with shape [batch, rows, cols, channels] if dataFormat is 'channelsLast.

Output shape: 4D tensor with shape: [batch, filters, newRows, newCols] if dataFormat is 'channelsFirst'. or 4D tensor with shape: [batch, newRows, newCols, filters] if dataFormat is 'channelsLast'.

References:

Parameters:
  • args (Object)
  • filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
  • kernelSize (number|number[]) The dimensions of the convolution window. If kernelSize is a number, the convolutional window will be square.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.conv3d (args) function Source

3D convolution layer (e.g. spatial convolution over volumes).

This layer creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs.

If useBias is True, a bias vector is created and added to the outputs.

If activation is not null, it is applied to the outputs as well.

When using this layer as the first layer in a model, provide the keyword argument inputShape (Array of integers, does not include the sample axis), e.g. inputShape=[128, 128, 128, 1] for 128x128x128 grayscale volumes in dataFormat='channelsLast'.

Parameters:
  • args (Object)
  • filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
  • kernelSize (number|number[]) The dimensions of the convolution window. If kernelSize is a number, the convolutional window will be square.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.cropping2D (args) function Source

Cropping layer for 2D input (e.g., image).

This layer can crop an input at the top, bottom, left and right side of an image tensor.

Input shape: 4D tensor with shape:

  • If dataFormat is "channelsLast": [batch, rows, cols, channels]
  • If data_format is "channels_first": [batch, channels, rows, cols].

Output shape: 4D with shape:

  • If dataFormat is "channelsLast": [batch, croppedRows, croppedCols, channels]
  • If dataFormat is "channelsFirst": [batch, channels, croppedRows, croppedCols].

Examples


const model = tf.sequential();
model.add(tf.layers.cropping2D({cropping:[[2, 2], [2, 2]],
                               inputShape: [128, 128, 3]}));
//now output shape is [batch, 124, 124, 3]
Parameters:
  • args (Object)
  • cropping (number|[number, number]|[[number, number], [number, number]]) Dimension of the cropping along the width and the height.

    • If integer: the same symmetric cropping is applied to width and height.
    • If list of 2 integers: interpreted as two different symmetric cropping values for height and width: [symmetric_height_crop, symmetric_width_crop].
    • If a list of 2 list of 2 integers: interpreted as [[top_crop, bottom_crop], [left_crop, right_crop]]
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Depthwise separable 2D convolution.

Depthwise Separable convolutions consists in performing just the first step in a depthwise spatial convolution (which acts on each input channel separately). The depthMultplier argument controls how many output channels are generated per input channel in the depthwise step.

Parameters:
  • args (Object)
  • kernelSize (number|[number, number]) An integer or Array of 2 integers, specifying the width and height of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
  • depthMultiplier (number) The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to filtersIn * depthMultiplier. Default: 1.
  • depthwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the depthwise kernel matrix. Default: GlorotNormal.
  • depthwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the depthwise kernel matrix.
  • depthwiseRegularizer ('l1l2'|string|Regularizer) Regulzarizer function for the depthwise kernel matrix.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Depthwise separable 2D convolution.

Separable convolution consists of first performing a depthwise spatial convolution (which acts on each input channel separately) followed by a pointwise convolution which mixes together the resulting output channels. The depthMultiplier argument controls how many output channels are generated per input channel in the depthwise step.

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

Input shape: 4D tensor with shape: [batch, channels, rows, cols] if data_format='channelsFirst' or 4D tensor with shape: [batch, rows, cols, channels] if data_format='channelsLast'.

Output shape: 4D tensor with shape: [batch, filters, newRows, newCols] if data_format='channelsFirst' or 4D tensor with shape: [batch, newRows, newCols, filters] if data_format='channelsLast'. rows and cols values might have changed due to padding.

Parameters:
  • args (Object)
  • depthMultiplier (number) The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to filtersIn * depthMultiplier. Default: 1.
  • depthwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the depthwise kernel matrix.
  • pointwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the pointwise kernel matrix.
  • depthwiseRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the depthwise kernel matrix.
  • pointwiseRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the pointwise kernel matrix.
  • depthwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the depthwise kernel matrix.
  • pointwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the pointwise kernel matrix.
  • filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
  • kernelSize (number|number[]) The dimensions of the convolution window. If kernelSize is a number, the convolutional window will be square.
  • strides (number|number[]) The strides of the convolution in each dimension. If strides is a number, strides in both dimensions are equal.

    Specifying any stride value != 1 is incompatible with specifying any dilationRate value != 1.

  • padding ('valid'|'same'|'causal') Padding mode.
  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    channels_last corresponds to inputs with shape (batch, ..., channels)

    channels_first corresponds to inputs with shape `(batch, channels,

    • ...)`.

    Defaults to channels_last.

  • dilationRate (number|[number]|[number, number]|[number, number, number]) The dilation rate to use for the dilated convolution in each dimension. Should be an integer or array of two or three integers.

    Currently, specifying any dilationRate value != 1 is incompatible with specifying any strides value != 1.

  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function of the layer.

    If you don't specify the activation, none is applied.

  • useBias (boolean) Whether the layer uses a bias vector. Defaults to true.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the convolutional kernel weights matrix.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the convolutional kernel weights.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Upsampling layer for 2D inputs.

Repeats the rows and columns of the data by size[0] and size[1] respectively.

Input shape: 4D tensor with shape: - If dataFormat is "channelsLast": [batch, rows, cols, channels] - If dataFormat is "channelsFirst": [batch, channels, rows, cols]

Output shape: 4D tensor with shape: - If dataFormat is "channelsLast": [batch, upsampledRows, upsampledCols, channels] - If dataFormat is "channelsFirst": [batch, channels, upsampledRows, upsampledCols]

Parameters:
  • args (Object)
  • size (number[]) The upsampling factors for rows and columns.

    Defaults to [2, 2].

  • dataFormat ('channelsFirst'|'channelsLast') Format of the data, which determines the ordering of the dimensions in the inputs.

    "channelsLast" corresponds to inputs with shape [batch, ..., channels]

    "channelsFirst" corresponds to inputs with shape `[batch, channels,

    • ...]`.

    Defaults to "channelsLast".

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.add (args?) function Source

Layer that performs element-wise addition on an Array of inputs.

It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape). The inputs are specified as an Array when the apply method of the Add layer instance is called. For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const addLayer = tf.layers.add();
const sum = addLayer.apply([input1, input2]);
console.log(JSON.stringify(sum.shape));
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.average (args?) function Source

Layer that performs element-wise averaging on an Array of inputs.

It takes as input a list of tensors, all of the same shape, and returns a single tensor (also of the same shape). For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const averageLayer = tf.layers.average();
const average = averageLayer.apply([input1, input2]);
console.log(JSON.stringify(average.shape));
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.concatenate (args?) function Source

Layer that concatenates an Array of inputs.

It takes a list of tensors, all of the same shape except for the concatenation axis, and returns a single tensor, the concatenation of all inputs. For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 3]});
const concatLayer = tf.layers.concatenate();
const output = concatLayer.apply([input1, input2]);
console.log(JSON.stringify(output.shape));
// You get [null, 2, 5], with the first dimension as the undetermined batch
// dimension. The last dimension (5) is the result of concatenating the
// last dimensions of the inputs (2 and 3).
Parameters:
  • args (Object) Optional
  • axis (number) Axis along which to concatenate.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.dot (args) function Source

Layer that computes a dot product between samples in two tensors.

E.g., if applied to a list of two tensors a and b both of shape [batchSize, n], the output will be a tensor of shape [batchSize, 1], where each entry at index [i, 0] will be the dot product between a[i, :] and b[i, :].

Example:

const dotLayer = tf.layers.dot({axes: -1});
const x1 = tf.tensor2d([[10, 20], [30, 40]]);
const x2 = tf.tensor2d([[-1, -2], [-3, -4]]);

// Invoke the layer's apply() method in eager (imperative) mode.
const y = dotLayer.apply([x1, x2]);
y.print();
Parameters:
  • args (Object)
  • axes (number|[number, number]) Axis or axes along which the dot product will be taken.

    Integer or an Array of integers.

  • normalize (boolean) Whether to L2-normalize samples along the dot product axis before taking the dot product.

    If set to true, the output of the dot product isthe cosine proximity between the two samples.

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.maximum (args?) function Source

Layer that computes the element-wise maximum an Array of inputs.

It takes as input a list of tensors, all of the same shape and returns a single tensor (also of the same shape). For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const maxLayer = tf.layers.maximum();
const max = maxLayer.apply([input1, input2]);
console.log(JSON.stringify(max.shape));
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.minimum (args?) function Source

Layer that computes the element-wise minimum of an Array of inputs.

It takes as input a list of tensors, all of the same shape and returns a single tensor (also of the same shape). For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const minLayer = tf.layers.minimum();
const min = minLayer.apply([input1, input2]);
console.log(JSON.stringify(min.shape));
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.multiply (args?) function Source

Layer that multiplies (element-wise) an Array of inputs.

It takes as input an Array of tensors, all of the same shape, and returns a single tensor (also of the same shape). For example:

const input1 = tf.input({shape: [2, 2]});
const input2 = tf.input({shape: [2, 2]});
const input3 = tf.input({shape: [2, 2]});
const multiplyLayer = tf.layers.multiply();
const product = multiplyLayer.apply([input1, input2, input3]);
console.log(product.shape);
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Batch normalization layer (Ioffe and Szegedy, 2014).

Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

Input shape: Arbitrary. Use the keyword argument inputShape (Array of integers, does not include the sample axis) when calling the constructor of this class, if this layer is used as a first layer in a model.

Output shape: Same shape as input.

References:

Parameters:
  • args (Object) Optional
  • axis (number) The integer axis that should be normalized (typically the features axis). Defaults to -1.

    For instance, after a Conv2D layer with data_format="channels_first", set axis=1 in batchNormalization.

  • momentum (number) Momentum of the moving average. Defaults to 0.99.
  • epsilon (number) Small float added to the variance to avoid dividing by zero. Defaults to 1e-3.
  • center (boolean) If true, add offset of beta to normalized tensor. If false, beta is ignored. Defaults to true.
  • scale (boolean) If true, multiply by gamma. If false, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling will be done by the next layer. Defaults to true.
  • betaInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the beta weight. Defaults to 'zeros'.
  • gammaInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the gamma weight. Defaults to ones.
  • movingMeanInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the moving mean. Defaults to zeros
  • movingVarianceInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the moving variance. Defaults to 'Ones'.
  • betaConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the beta weight.
  • gammaConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for gamma weight.
  • betaRegularizer ('l1l2'|string|Regularizer) Regularizer for the beta weight.
  • gammaRegularizer ('l1l2'|string|Regularizer) Regularizer for the gamma weight.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Average pooling operation for spatial data.

Input shape: [batchSize, inLength, channels]

Output shape: [batchSize, pooledLength, channels]

tf.avgPool1d is an alias.

Parameters:
  • args (Object)
  • poolSize (number|[number]) Size of the window to pool over, should be an integer.
  • strides (number|[number]) Period at which to sample the pooled values.

    If null, defaults to poolSize.

  • padding ('valid'|'same'|'causal') How to fill in data that's not an integer multiple of poolSize.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Average pooling operation for spatial data.

Input shape:

  • If dataFormat === CHANNEL_LAST: 4D tensor with shape: [batchSize, rows, cols, channels]
  • If dataFormat === CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, rows, cols]

Output shape

  • If dataFormat === CHANNEL_LAST: 4D tensor with shape: [batchSize, pooleRows, pooledCols, channels]
  • If dataFormat === CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, pooleRows, pooledCols]

tf.avgPool2d is an alias.

Parameters:
  • args (Object)
  • poolSize (number|[number, number]) Factors by which to downscale in each dimension [vertical, horizontal]. Expects an integer or an array of 2 integers.

    For example, [2, 2] will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions.

  • strides (number|[number, number]) The size of the stride in each dimension of the pooling window. Expects an integer or an array of 2 integers. Integer, tuple of 2 integers, or None.

    If null, defaults to poolSize.

  • padding ('valid'|'same'|'causal') The padding type to use for the pooling layer.
  • dataFormat ('channelsFirst'|'channelsLast') The data format to use for the pooling layer.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Global average pooling operation for temporal data.

Input Shape: 3D tensor with shape: [batchSize, steps, features].

Output Shape:2D tensor with shape: [batchSize, features].

Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Global average pooling operation for spatial data.

Input shape:

  • If dataFormat is CHANNEL_LAST: 4D tensor with shape: [batchSize, rows, cols, channels].
  • If dataFormat is CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, rows, cols].

Output shape: 2D tensor with shape: [batchSize, channels].

Parameters:
  • args (Object)
  • dataFormat ('channelsFirst'|'channelsLast') One of CHANNEL_LAST (default) or CHANNEL_FIRST.

    The ordering of the dimensions in the inputs. CHANNEL_LAST corresponds to inputs with shape [batch, height, width, channels[ while CHANNEL_FIRST corresponds to inputs with shape [batch, channels, height, width].

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Global max pooling operation for temporal data.

Input Shape: 3D tensor with shape: [batchSize, steps, features].

Output Shape:2D tensor with shape: [batchSize, features].

Parameters:
  • args (Object) Optional
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Global max pooling operation for spatial data.

Input shape:

  • If dataFormat is CHANNEL_LAST: 4D tensor with shape: [batchSize, rows, cols, channels].
  • If dataFormat is CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, rows, cols].

Output shape: 2D tensor with shape: [batchSize, channels].

Parameters:
  • args (Object)
  • dataFormat ('channelsFirst'|'channelsLast') One of CHANNEL_LAST (default) or CHANNEL_FIRST.

    The ordering of the dimensions in the inputs. CHANNEL_LAST corresponds to inputs with shape [batch, height, width, channels[ while CHANNEL_FIRST corresponds to inputs with shape [batch, channels, height, width].

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Max pooling operation for temporal data.

Input shape: [batchSize, inLength, channels]

Output shape: [batchSize, pooledLength, channels]

Parameters:
  • args (Object)
  • poolSize (number|[number]) Size of the window to pool over, should be an integer.
  • strides (number|[number]) Period at which to sample the pooled values.

    If null, defaults to poolSize.

  • padding ('valid'|'same'|'causal') How to fill in data that's not an integer multiple of poolSize.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Max pooling operation for spatial data.

Input shape

  • If dataFormat === CHANNEL_LAST: 4D tensor with shape: [batchSize, rows, cols, channels]
  • If dataFormat === CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, rows, cols]

Output shape

  • If dataFormat=CHANNEL_LAST: 4D tensor with shape: [batchSize, pooleRows, pooledCols, channels]
  • If dataFormat=CHANNEL_FIRST: 4D tensor with shape: [batchSize, channels, pooleRows, pooledCols]
Parameters:
  • args (Object)
  • poolSize (number|[number, number]) Factors by which to downscale in each dimension [vertical, horizontal]. Expects an integer or an array of 2 integers.

    For example, [2, 2] will halve the input in both spatial dimension. If only one integer is specified, the same window length will be used for both dimensions.

  • strides (number|[number, number]) The size of the stride in each dimension of the pooling window. Expects an integer or an array of 2 integers. Integer, tuple of 2 integers, or None.

    If null, defaults to poolSize.

  • padding ('valid'|'same'|'causal') The padding type to use for the pooling layer.
  • dataFormat ('channelsFirst'|'channelsLast') The data format to use for the pooling layer.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.gru (args) function Source

Gated Recurrent Unit - Cho et al. 2014.

This is an RNN layer consisting of one GRUCell. However, unlike the underlying GRUCell, the apply method of SimpleRNN operates on a sequence of inputs. The shape of the input (not including the first, batch dimension) needs to be at least 2-D, with the first dimension being time steps. For example:

const rnn = tf.layers.gru({units: 8, returnSequences: true});

// Create an input with 10 time steps.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the `GRUCell`'s number of units.
Parameters:
  • args (Object)
  • recurrentActivation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use for the recurrent step.

    Defaults to hard sigmoid (hardSigmoid).

    If null, no activation is applied.

  • implementation (number) Implementation mode, either 1 or 2.

    Mode 1 will structure its operations as a larger number of smaller dot products and additions.

    Mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

    Note: For superior performance, TensorFlow.js always uses implementation 2, regardless of the actual value of this configuration field.

  • units (number) Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use.

    Defaults to hyperbolic tangent (tanh)

    If you pass null, no activation will be applied.

  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrentKernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the bias vector.
  • dropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • cell (tf.RNNCell|tf.RNNCell[]) A RNN cell instance. A RNN cell is a class that has:

    • a call() method, which takes [Tensor, Tensor] as the first input argument. The first item is the input at time t, and second item is the cell state at time t. The call() method returns [outputAtT, statesAtTPlus1]. The call() method of the cell can also take the argument constants, see section "Note on passing external constants" below. Porting Node: PyKeras overrides the call() signature of RNN cells, which are Layer subtypes, to accept two arguments. tfjs-layers does not do such overriding. Instead we preseve the call() signature, which due to its Tensor|Tensor[] argument and return value, is flexible enough to handle the inputs and states.
    • a stateSize attribute. This can be a single integer (single state) in which case it is the size of the recurrent state (which should be the same as the size of the cell output). This can also be an Array of integers (one size per state). In this case, the first entry (stateSize[0]) should be the same as the size of the cell output. It is also possible for cell to be a list of RNN cell instances, in which case the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN.
  • returnSequences (boolean) Whether to return the last output in the output sequence, or the full sequence.
  • returnState (boolean) Whether to return the last state in addition to the output.
  • goBackwards (boolean) If true, process the input sequence backwards and return the reversed sequence (default: false).
  • stateful (boolean) If true, the last state for each sample at index i in a batch will be used as initial state of the sample of index i in the following batch (default: false).

    You can set RNN layers to be "stateful", which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

    To enable "statefulness":

    • specify stateful: true in the layer constructor.
    • specify a fixed batch size for your model, by passing
      • if sequential model: batchInputShape: [...] to the first layer in your model.
      • else for functional model with 1 or more Input layers: batchShape: [...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g., [32, 10, 100].
    • specify shuffle: false when calling LayersModel.fit().

    To reset the state of your model, call resetStates() on either the specific layer or on the entire model.

  • unroll (boolean) If true, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory- intensive. Unrolling is only suitable for short sequences (default: false). Porting Note: tfjs-layers has an imperative backend. RNNs are executed with normal TypeScript control flow. Hence this property is inapplicable and ignored in tfjs-layers.
  • inputDim (number) Dimensionality of the input (integer). This option (or alternatively, the option inputShape) is required when this layer is used as the first layer in a model.
  • inputLength (number) Length of the input sequences, to be specified when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g., via the inputShape option).
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.gruCell (args) function Source

Cell class for GRU.

GRUCell is distinct from the RNN subclass GRU in that its apply method takes the input data of only a single time step and returns the cell's output at the time step, while GRU takes the input data over a number of time steps. For example:

const cell = tf.layers.gruCell({units: 2});
const input = tf.input({shape: [10]});
const output = cell.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10]: This is the cell's output at a single time step. The 1st
// dimension is the unknown batch size.

Instance(s) of GRUCell can be used to construct RNN layers. The most typical use of this workflow is to combine a number of cells into a stacked RNN cell (i.e., StackedRNNCell internally) and use it to create an RNN. For example:

const cells = [
  tf.layers.gruCell({units: 4}),
  tf.layers.gruCell({units: 8}),
];
const rnn = tf.layers.rnn({cell: cells, returnSequences: true});

// Create an input with 10 time steps and a length-20 vector at each step.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the last `gruCell`'s number of units.

To create an RNN consisting of only one GRUCell, use the tf.layers.gru().

Parameters:
  • args (Object)
  • recurrentActivation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use for the recurrent step.

    Defaults to hard sigmoid (hardSigmoid).

    If null, no activation is applied.

  • implementation (number) Implementation mode, either 1 or 2.

    Mode 1 will structure its operations as a larger number of smaller dot products and additions.

    Mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

    Note: For superior performance, TensorFlow.js always uses implementation 2, regardless of the actual value of this configuration field.

  • units (number) units: Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use. Default: hyperbolic tangent ('tanh'). If you pass null, 'linear' activation will be applied.
  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrent_kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraintfunction applied to the bias vector.
  • dropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.RNNCell
tf.layers.lstm (args) function Source

Long-Short Term Memory layer - Hochreiter 1997.

This is an RNN layer consisting of one LSTMCell. However, unlike the underlying LSTMCell, the apply method of LSTM operates on a sequence of inputs. The shape of the input (not including the first, batch dimension) needs to be at least 2-D, with the first dimension being time steps. For example:

const lstm = tf.layers.lstm({units: 8, returnSequences: true});

// Create an input with 10 time steps.
const input = tf.input({shape: [10, 20]});
const output = lstm.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the `LSTMCell`'s number of units.
Parameters:
  • args (Object)
  • recurrentActivation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use for the recurrent step.

    Defaults to hard sigmoid (hardSigmoid).

    If null, no activation is applied.

  • unitForgetBias (boolean) If true, add 1 to the bias of the forget gate at initialization. Setting it to true will also force biasInitializer = 'zeros'. This is recommended in Jozefowicz et al..
  • implementation (number) Implementation mode, either 1 or 2. Mode 1 will structure its operations as a larger number of smaller dot products and additions, whereas mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

    Note: For superior performance, TensorFlow.js always uses implementation 2, regardless of the actual value of this config field.

  • units (number) Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use.

    Defaults to hyperbolic tangent (tanh)

    If you pass null, no activation will be applied.

  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrentKernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the bias vector.
  • dropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • cell (tf.RNNCell|tf.RNNCell[]) A RNN cell instance. A RNN cell is a class that has:

    • a call() method, which takes [Tensor, Tensor] as the first input argument. The first item is the input at time t, and second item is the cell state at time t. The call() method returns [outputAtT, statesAtTPlus1]. The call() method of the cell can also take the argument constants, see section "Note on passing external constants" below. Porting Node: PyKeras overrides the call() signature of RNN cells, which are Layer subtypes, to accept two arguments. tfjs-layers does not do such overriding. Instead we preseve the call() signature, which due to its Tensor|Tensor[] argument and return value, is flexible enough to handle the inputs and states.
    • a stateSize attribute. This can be a single integer (single state) in which case it is the size of the recurrent state (which should be the same as the size of the cell output). This can also be an Array of integers (one size per state). In this case, the first entry (stateSize[0]) should be the same as the size of the cell output. It is also possible for cell to be a list of RNN cell instances, in which case the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN.
  • returnSequences (boolean) Whether to return the last output in the output sequence, or the full sequence.
  • returnState (boolean) Whether to return the last state in addition to the output.
  • goBackwards (boolean) If true, process the input sequence backwards and return the reversed sequence (default: false).
  • stateful (boolean) If true, the last state for each sample at index i in a batch will be used as initial state of the sample of index i in the following batch (default: false).

    You can set RNN layers to be "stateful", which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

    To enable "statefulness":

    • specify stateful: true in the layer constructor.
    • specify a fixed batch size for your model, by passing
      • if sequential model: batchInputShape: [...] to the first layer in your model.
      • else for functional model with 1 or more Input layers: batchShape: [...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g., [32, 10, 100].
    • specify shuffle: false when calling LayersModel.fit().

    To reset the state of your model, call resetStates() on either the specific layer or on the entire model.

  • unroll (boolean) If true, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory- intensive. Unrolling is only suitable for short sequences (default: false). Porting Note: tfjs-layers has an imperative backend. RNNs are executed with normal TypeScript control flow. Hence this property is inapplicable and ignored in tfjs-layers.
  • inputDim (number) Dimensionality of the input (integer). This option (or alternatively, the option inputShape) is required when this layer is used as the first layer in a model.
  • inputLength (number) Length of the input sequences, to be specified when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g., via the inputShape option).
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.lstmCell (args) function Source

Cell class for LSTM.

LSTMCell is distinct from the RNN subclass LSTM in that its apply method takes the input data of only a single time step and returns the cell's output at the time step, while LSTM takes the input data over a number of time steps. For example:

const cell = tf.layers.lstmCell({units: 2});
const input = tf.input({shape: [10]});
const output = cell.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10]: This is the cell's output at a single time step. The 1st
// dimension is the unknown batch size.

Instance(s) of LSTMCell can be used to construct RNN layers. The most typical use of this workflow is to combine a number of cells into a stacked RNN cell (i.e., StackedRNNCell internally) and use it to create an RNN. For example:

const cells = [
  tf.layers.lstmCell({units: 4}),
  tf.layers.lstmCell({units: 8}),
];
const rnn = tf.layers.rnn({cell: cells, returnSequences: true});

// Create an input with 10 time steps and a length-20 vector at each step.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the last `lstmCell`'s number of units.

To create an RNN consisting of only one LSTMCell, use the tf.layers.lstm().

Parameters:
  • args (Object)
  • recurrentActivation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use for the recurrent step.

    Defaults to hard sigmoid (hardSigmoid).

    If null, no activation is applied.

  • unitForgetBias (boolean) If true, add 1 to the bias of the forget gate at initialization. Setting it to true will also force biasInitializer = 'zeros'. This is recommended in Jozefowicz et al..
  • implementation (number) Implementation mode, either 1 or 2.

    Mode 1 will structure its operations as a larger number of smaller dot products and additions.

    Mode 2 will batch them into fewer, larger operations. These modes will have different performance profiles on different hardware and for different applications.

    Note: For superior performance, TensorFlow.js always uses implementation 2, regardless of the actual value of this configuration field.

  • units (number) units: Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use. Default: hyperbolic tangent ('tanh'). If you pass null, 'linear' activation will be applied.
  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrent_kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraintfunction applied to the bias vector.
  • dropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.RNNCell
tf.layers.rnn (args) function Source

Base class for recurrent layers.

Input shape: 3D tensor with shape [batchSize, timeSteps, inputDim].

Output shape:

  • if returnState, an Array of tensors (i.e., tf.Tensors). The first tensor is the output. The remaining tensors are the states at the last time step, each with shape [batchSize, units].
  • if returnSequences, the output will have shape [batchSize, timeSteps, units].
  • else, the output will have shape [batchSize, units].

Masking: This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an embedding layer with the mask_zero parameter set to True.

Notes on using statefulness in RNNs: You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

To enable statefulness: - specify stateful: true in the layer constructor. - specify a fixed batch size for your model, by passing if sequential model: batchInputShape=[...] to the first layer in your model. else for functional model with 1 or more Input layers: batchShape=[...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g. (32, 10, 100). - specify shuffle=False when calling fit().

To reset the states of your model, call .resetStates() on either a specific layer, or on your entire model.

Note on specifying the initial state of RNNs You can specify the initial state of RNN layers symbolically by calling them with the option initialState. The value of initialState should be a tensor or list of tensors representing the initial state of the RNN layer.

You can specify the initial state of RNN layers numerically by calling resetStates with the keyword argument states. The value of states should be a numpy array or list of numpy arrays representing the initial state of the RNN layer.

Note on passing external constants to RNNs You can pass "external" constants to the cell using the constants keyword argument of RNN.call method. This requires that the cell.call method accepts the same keyword argument constants. Such constants can be used to conditon the cell transformation on additional static inputs (not changing over time), a.k.a an attention mechanism.

Parameters:
  • args (Object)
  • cell (tf.RNNCell|tf.RNNCell[]) A RNN cell instance. A RNN cell is a class that has:

    • a call() method, which takes [Tensor, Tensor] as the first input argument. The first item is the input at time t, and second item is the cell state at time t. The call() method returns [outputAtT, statesAtTPlus1]. The call() method of the cell can also take the argument constants, see section "Note on passing external constants" below. Porting Node: PyKeras overrides the call() signature of RNN cells, which are Layer subtypes, to accept two arguments. tfjs-layers does not do such overriding. Instead we preseve the call() signature, which due to its Tensor|Tensor[] argument and return value, is flexible enough to handle the inputs and states.
    • a stateSize attribute. This can be a single integer (single state) in which case it is the size of the recurrent state (which should be the same as the size of the cell output). This can also be an Array of integers (one size per state). In this case, the first entry (stateSize[0]) should be the same as the size of the cell output. It is also possible for cell to be a list of RNN cell instances, in which case the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN.
  • returnSequences (boolean) Whether to return the last output in the output sequence, or the full sequence.
  • returnState (boolean) Whether to return the last state in addition to the output.
  • goBackwards (boolean) If true, process the input sequence backwards and return the reversed sequence (default: false).
  • stateful (boolean) If true, the last state for each sample at index i in a batch will be used as initial state of the sample of index i in the following batch (default: false).

    You can set RNN layers to be "stateful", which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

    To enable "statefulness":

    • specify stateful: true in the layer constructor.
    • specify a fixed batch size for your model, by passing
      • if sequential model: batchInputShape: [...] to the first layer in your model.
      • else for functional model with 1 or more Input layers: batchShape: [...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g., [32, 10, 100].
    • specify shuffle: false when calling LayersModel.fit().

    To reset the state of your model, call resetStates() on either the specific layer or on the entire model.

  • unroll (boolean) If true, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory- intensive. Unrolling is only suitable for short sequences (default: false). Porting Note: tfjs-layers has an imperative backend. RNNs are executed with normal TypeScript control flow. Hence this property is inapplicable and ignored in tfjs-layers.
  • inputDim (number) Dimensionality of the input (integer). This option (or alternatively, the option inputShape) is required when this layer is used as the first layer in a model.
  • inputLength (number) Length of the input sequences, to be specified when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g., via the inputShape option).
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.simpleRNN (args) function Source

Fully-connected RNN where the output is to be fed back to input.

This is an RNN layer consisting of one SimpleRNNCell. However, unlike the underlying SimpleRNNCell, the apply method of SimpleRNN operates on a sequence of inputs. The shape of the input (not including the first, batch dimension) needs to be at least 2-D, with the first dimension being time steps. For example:

const rnn = tf.layers.simpleRNN({units: 8, returnSequences: true});

// Create an input with 10 time steps.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the `SimpleRNNCell`'s number of units.
Parameters:
  • args (Object)
  • units (number) Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use.

    Defaults to hyperbolic tangent (tanh)

    If you pass null, no activation will be applied.

  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrentKernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the bias vector.
  • dropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • cell (tf.RNNCell|tf.RNNCell[]) A RNN cell instance. A RNN cell is a class that has:

    • a call() method, which takes [Tensor, Tensor] as the first input argument. The first item is the input at time t, and second item is the cell state at time t. The call() method returns [outputAtT, statesAtTPlus1]. The call() method of the cell can also take the argument constants, see section "Note on passing external constants" below. Porting Node: PyKeras overrides the call() signature of RNN cells, which are Layer subtypes, to accept two arguments. tfjs-layers does not do such overriding. Instead we preseve the call() signature, which due to its Tensor|Tensor[] argument and return value, is flexible enough to handle the inputs and states.
    • a stateSize attribute. This can be a single integer (single state) in which case it is the size of the recurrent state (which should be the same as the size of the cell output). This can also be an Array of integers (one size per state). In this case, the first entry (stateSize[0]) should be the same as the size of the cell output. It is also possible for cell to be a list of RNN cell instances, in which case the cells get stacked on after the other in the RNN, implementing an efficient stacked RNN.
  • returnSequences (boolean) Whether to return the last output in the output sequence, or the full sequence.
  • returnState (boolean) Whether to return the last state in addition to the output.
  • goBackwards (boolean) If true, process the input sequence backwards and return the reversed sequence (default: false).
  • stateful (boolean) If true, the last state for each sample at index i in a batch will be used as initial state of the sample of index i in the following batch (default: false).

    You can set RNN layers to be "stateful", which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

    To enable "statefulness":

    • specify stateful: true in the layer constructor.
    • specify a fixed batch size for your model, by passing
      • if sequential model: batchInputShape: [...] to the first layer in your model.
      • else for functional model with 1 or more Input layers: batchShape: [...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g., [32, 10, 100].
    • specify shuffle: false when calling LayersModel.fit().

    To reset the state of your model, call resetStates() on either the specific layer or on the entire model.

  • unroll (boolean) If true, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory- intensive. Unrolling is only suitable for short sequences (default: false). Porting Note: tfjs-layers has an imperative backend. RNNs are executed with normal TypeScript control flow. Hence this property is inapplicable and ignored in tfjs-layers.
  • inputDim (number) Dimensionality of the input (integer). This option (or alternatively, the option inputShape) is required when this layer is used as the first layer in a model.
  • inputLength (number) Length of the input sequences, to be specified when it is constant. This argument is required if you are going to connect Flatten then Dense layers upstream (without it, the shape of the dense outputs cannot be computed). Note that if the recurrent layer is not the first layer in your model, you would need to specify the input length at the level of the first layer (e.g., via the inputShape option).
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Cell class for SimpleRNN.

SimpleRNNCell is distinct from the RNN subclass SimpleRNN in that its apply method takes the input data of only a single time step and returns the cell's output at the time step, while SimpleRNN takes the input data over a number of time steps. For example:

const cell = tf.layers.simpleRNNCell({units: 2});
const input = tf.input({shape: [10]});
const output = cell.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10]: This is the cell's output at a single time step. The 1st
// dimension is the unknown batch size.

Instance(s) of SimpleRNNCell can be used to construct RNN layers. The most typical use of this workflow is to combine a number of cells into a stacked RNN cell (i.e., StackedRNNCell internally) and use it to create an RNN. For example:

const cells = [
  tf.layers.simpleRNNCell({units: 4}),
  tf.layers.simpleRNNCell({units: 8}),
];
const rnn = tf.layers.rnn({cell: cells, returnSequences: true});

// Create an input with 10 time steps and a length-20 vector at each step.
const input = tf.input({shape: [10, 20]});
const output = rnn.apply(input);

console.log(JSON.stringify(output.shape));
// [null, 10, 8]: 1st dimension is unknown batch size; 2nd dimension is the
// same as the sequence length of `input`, due to `returnSequences`: `true`;
// 3rd dimension is the last `SimpleRNNCell`'s number of units.

To create an RNN consisting of only one SimpleRNNCell, use the tf.layers.simpleRNN().

Parameters:
  • args (Object)
  • units (number) units: Positive integer, dimensionality of the output space.
  • activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh') Activation function to use. Default: hyperbolic tangent ('tanh'). If you pass null, 'linear' activation will be applied.
  • useBias (boolean) Whether the layer uses a bias vector.
  • kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the kernel weights matrix, used for the linear transformation of the inputs.
  • recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the recurrentKernel weights matrix, used for linear transformation of the recurrent state.
  • biasInitializer ('constant'|'glorotNormal'|'glorotUniform'|'heNormal'|'heUniform'|'identity'| 'leCunNormal'|'leCunUniform'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector.
  • kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix.
  • recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrent_kernel weights matrix.
  • biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector.
  • kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix.
  • recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix.
  • biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraintfunction applied to the bias vector.
  • dropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
  • recurrentDropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.RNNCell

Base class for recurrent layers.

Input shape: 3D tensor with shape [batchSize, timeSteps, inputDim].

Output shape:

  • if returnState, an Array of tensors (i.e., tf.Tensors). The first tensor is the output. The remaining tensors are the states at the last time step, each with shape [batchSize, units].
  • if returnSequences, the output will have shape [batchSize, timeSteps, units].
  • else, the output will have shape [batchSize, units].

Masking: This layer supports masking for input data with a variable number of timesteps. To introduce masks to your data, use an embedding layer with the mask_zero parameter set to True.

Notes on using statefulness in RNNs: You can set RNN layers to be 'stateful', which means that the states computed for the samples in one batch will be reused as initial states for the samples in the next batch. This assumes a one-to-one mapping between samples in different successive batches.

To enable statefulness: - specify stateful: true in the layer constructor. - specify a fixed batch size for your model, by passing if sequential model: batchInputShape=[...] to the first layer in your model. else for functional model with 1 or more Input layers: batchShape=[...] to all the first layers in your model. This is the expected shape of your inputs including the batch size. It should be a tuple of integers, e.g. (32, 10, 100). - specify shuffle=False when calling fit().

To reset the states of your model, call .resetStates() on either a specific layer, or on your entire model.

Note on specifying the initial state of RNNs You can specify the initial state of RNN layers symbolically by calling them with the option initialState. The value of initialState should be a tensor or list of tensors representing the initial state of the RNN layer.

You can specify the initial state of RNN layers numerically by calling resetStates with the keyword argument states. The value of states should be a numpy array or list of numpy arrays representing the initial state of the RNN layer.

Note on passing external constants to RNNs You can pass "external" constants to the cell using the constants keyword argument of RNN.call method. This requires that the cell.call method accepts the same keyword argument constants. Such constants can be used to conditon the cell transformation on additional static inputs (not changing over time), a.k.a an attention mechanism.

Parameters:
  • args (Object)
  • cells (tf.RNNCell[]) A Array of RNNCell instances.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.RNNCell
Parameters:
  • args (Object)
  • layer (RNN) The instance of an RNN layer to be wrapped.
  • mergeMode ('sum'|'mul'|'concat'|'ave') Mode by which outputs of the forward and backward RNNs are combined. If null or undefined, the output will not be combined, they will be returned as an Array.

    If undefined (i.e., not provided), defaults to 'concat'.

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: Bidirectional

This wrapper applies a layer to every temporal slice of an input.

The input should be at least 3D, and the dimension of the index 1 will be considered to be the temporal dimension.

Consider a batch of 32 samples, where each sample is a sequence of 10 vectors of 16 dimensions. The batch input shape of the layer is then [32, 10, 16], and the inputShape, not including the sample dimension, is [10, 16].

You can then use TimeDistributed to apply a Dense layer to each of the 10 timesteps, independently:

const model = tf.sequential();
model.add(tf.layers.timeDistributed({
  layer: tf.layers.dense({units: 8}),
  inputShape: [10, 16],
}));

// Now model.outputShape = [null, 10, 8].
// The output will then have shape `[32, 10, 8]`.

// In subsequent layers, there is no need for `inputShape`:
model.add(tf.layers.timeDistributed({layer: tf.layers.dense({units: 32})}));
console.log(JSON.stringify(model.outputs[0].shape));
// Now model.outputShape = [null, 10, 32].

The output will then have shape [32, 10, 32].

TimeDistributed can be used with arbitrary layers, not just Dense, for instance a Conv2D layer.

const model = tf.sequential();
model.add(tf.layers.timeDistributed({
  layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),
  inputShape: [10, 299, 299, 3],
}));
console.log(JSON.stringify(model.outputs[0].shape));
Parameters:
  • args (Object)
  • layer (tf.layers.Layer) The layer to be wrapped.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer
tf.layers.Layer extends serialization.Serializable class Source

A layer is a grouping of operations and weights that can be composed to create a tf.LayersModel.

Layers are constructed by using the functions under the tf.layers namespace.

apply (inputs, kwargs?) method Source

Builds or executes a `Layer's logic.

When called with tf.Tensor(s), execute the Layers computation and return Tensor(s). For example:

const denseLayer = tf.layers.dense({
   units: 1,
   kernelInitializer: 'zeros',
   useBias: false
});

// Invoke the layer's apply() method with a [tf.Tensor](#class:Tensor) (with concrete
// numeric values).
const input = tf.ones([2, 2]);
const output = denseLayer.apply(input);

// The output's value is expected to be [[0], [0]], due to the fact that
// the dense layer has a kernel initialized to all-zeros and does not have
// a bias.
output.print();

When called with tf.SymbolicTensor(s), this will prepare the layer for future execution. This entails internal book-keeping on shapes of expected Tensors, wiring layers together, and initializing weights.

Calling apply with tf.SymbolicTensors are typically used during the building of non-tf.Sequential models. For example:

const flattenLayer = tf.layers.flatten();
const denseLayer = tf.layers.dense({units: 1});

// Use tf.layers.input() to obtain a SymbolicTensor as input to apply().
const input = tf.input({shape: [2, 2]});
const output1 = flattenLayer.apply(input);

// output1.shape is [null, 4]. The first dimension is the undetermined
// batch size. The second dimension comes from flattening the [2, 2]
// shape.
console.log(JSON.stringify(output1.shape));

// The output SymbolicTensor of the flatten layer can be used to call
// the apply() of the dense layer:
const output2 = denseLayer.apply(output1);

// output2.shape is [null, 1]. The first dimension is the undetermined
// batch size. The second dimension matches the number of units of the
// dense layer.
console.log(JSON.stringify(output2.shape));

// The input and output and be used to construct a model that consists
// of the flatten and dense layers.
const model = tf.model({inputs: input, outputs: output2});

@param inputs a tf.Tensor or tf.SymbolicTensor or an Array of them. @param kwargs Additional keyword arguments to be passed to call().

@return Output of the layer's call method.

@exception ValueError error in case the layer is missing shape information for its build call.

Parameters:
countParams () method Source

Counts the total number of numbers (e.g., float32, int32) in the weights.

Returns: number
build (inputShape) method Source

Creates the layer weights.

Must be implemented on all layers that have weights.

Called when apply() is called to construct the weights.

Parameters:
  • inputShape ((null | number)[]|(null | number)[][]) A Shape or array of Shape (unused).
Returns: void
getWeights (trainableOnly?) method Source

Returns the current values of the weights of the layer.

Parameters:
  • trainableOnly (boolean) Whether to get the values of only trainable weights. Optional
Returns: tf.Tensor[]
setWeights (weights) method Source

Sets the weights of the layer, from Tensors.

Parameters:
  • weights (tf.Tensor[]) a list of Tensors. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of getWeights).
Returns: void
addWeight (name, shape, dtype?, initializer?, regularizer?, trainable?, constraint?) method Source

Adds a weight variable to the layer.

Parameters:
  • name (string) Name of the new weight variable.
  • shape ((null | number)[]) The shape of the weight.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The dtype of the weight. Optional
  • initializer (tf.initializers.Initializer) An initializer instance. Optional
  • regularizer (Regularizer) A regularizer instance. Optional
  • trainable (boolean) Whether the weight should be trained via backprop or not (assuming that the layer itself is also trainable). Optional
  • constraint (tf.constraints.Constraint) An optional trainable. Optional
Returns: LayerVariable
addLoss (losses) method Source

Add losses to the layer.

The loss may potentionally be conditional on some inputs tensors, for instance activity losses are conditional on the layer's inputs.

Parameters:
  • losses (RegularizerFn|RegularizerFn[])
Returns: void
computeOutputShape (inputShape) method Source

Computes the output shape of the layer.

Assumes that the layer will be built to match that input shape provided.

Parameters:
  • inputShape ((null | number)[]|(null | number)[][]) A shape (tuple of integers) or a list of shape tuples (one per output tensor of the layer). Shape tuples can include null for free dimensions, instead of an integer.
Returns: (null | number)[]|(null | number)[][]
getConfig () method Source

Returns the config of the layer.

A layer config is a TS dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by 'Container' (one layer of abstraction above).

Porting Note: The TS dictionary follows TS naming standrds for keys, and uses tfjs-layers type-safe Enums. Serialization methods should use a helper function to convert to the pythonic storage standard. (see serialization_utils.convertTsToPythonic)

Returns: serialization.ConfigDict
dispose () method Source

Attempt to dispose layer's weights.

This method decrease the reference count of the Layer object by 1.

A Layer is reference-counted. Its reference count is incremented by 1 the first item its apply() method is called and when it becomes a part of a new Node (through calling the apply()) method on a tf.SymbolicTensor).

If the reference count of a Layer becomes 0, all the weights will be disposed and the underlying memory (e.g., the textures allocated in WebGL) will be freed.

Note: If the reference count is greater than 0 after the decrement, the weights of the Layer will not be disposed.

After a Layer is disposed, it cannot be used in calls such as apply(), getWeights() or setWeights() anymore.

Returns: DisposeResult

An RNNCell layer.

tf.layers.inputLayer (args) function Source

An input layer is an entry point into a tf.LayersModel.

InputLayer is generated automatically for tf.Sequentialmodels by specifying theinputshapeorbatchInputShape` for the first layer. It should not be specified explicitly. However, it can be useful sometimes, e.g., when constructing a sequential model from a subset of another sequential model's layers. Like the code snippet below shows.

// Define a model which simply adds two inputs.
const model1 = tf.sequential();
model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));
model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
model1.summary();
model1.predict(tf.zeros([1, 4])).print();

// Construct another model, reusing the second layer of `model1` while
// not using the first layer of `model1`. Note that you cannot add the second
// layer of `model` directly as the first layer of the new sequential model,
// because doing so will lead to an error related to the fact that the layer
// is not an input layer. Instead, you need to create an `inputLayer` and add
// it to the new sequential model before adding the reused layer.
const model2 = tf.sequential();
// Use an inputShape that matches the input shape of `model1`'s second
// layer.
model2.add(tf.layers.inputLayer({inputShape: [3]}));
model2.add(model1.layers[1]);
model2.summary();
model2.predict(tf.zeros([1, 3])).print();
Parameters:
  • args (Object)
  • inputShape ((null | number)[]) Input shape, not including the batch axis.
  • batchSize (number) Optional input batch size (integer or null).
  • batchInputShape ((null | number)[]) Batch input shape, including the batch axis.
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') Datatype of the input.
  • sparse (boolean) Whether the placeholder created is meant to be sparse.
  • name (string) Name of the layer.
Returns: tf.layers.Layer

Zero-padding layer for 2D input (e.g., image).

This layer can add rows and columns of zeros at the top, bottom, left and right side of an image tensor.

Input shape: 4D tensor with shape:

  • If dataFormat is "channelsLast": [batch, rows, cols, channels]
  • If data_format is "channels_first": [batch, channels, rows, cols].

Output shape: 4D with shape:

  • If dataFormat is "channelsLast": [batch, paddedRows, paddedCols, channels]
  • If dataFormat is "channelsFirst": [batch, channels, paddedRows, paddedCols].
Parameters:
  • args (Object) Optional
  • padding (number|[number, number]|[[number, number], [number, number]]) Integer, or Array of 2 integers, or Array of 2 Arrays, each of which is an Array of 2 integers.

    • If integer, the same symmetric padding is applied to width and height.
    • If Array` of 2 integers, interpreted as two different symmetric values
      • for height and width:
      • [symmetricHeightPad, symmetricWidthPad].
        • If Array of 2 Arrays, interpreted as:
      • [[topPad, bottomPad], [leftPad, rightPad]].
  • dataFormat ('channelsFirst'|'channelsLast') One of 'channelsLast' (default) and 'channelsFirst'.

    The ordering of the dimensions in the inputs. channelsLast corresponds to inputs with shape [batch, height, width, channels] while channelsFirst corresponds to inputs with shape [batch, channels, height, width].

  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

Applies Alpha Dropout to the input.

As it is a regularization layer, it is only active at training time.

Alpha Dropout is a Dropout that keeps mean and variance of inputs to their original values, in order to ensure the self-normalizing property even after this dropout. Alpha Dropout fits well to Scaled Exponential Linear Units by randomly setting activations to the negative saturation value.

Arguments

rate: float, drop probability (as with `Dropout`).
    The multiplicative noise will have
    standard deviation `sqrt(rate / (1 - rate))`.
noise_shape: A 1-D `Tensor` of type `int32`, representing the
     shape for randomly generated keep/drop flags.

Input shape

     Arbitrary. Use the keyword argument `input_shape`
     (tuple of integers, does not include the samples axis)
     when using this layer as the first layer in a model.

Output shape

     Same shape as input.

References

 - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
Parameters:
  • args (Object)
  • rate (number) drop probability.
  • noiseShape ((null | number)[]) A 1-D Tensor of type int32, representing the shape for randomly generated keep/drop flags.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: AlphaDropout

Apply multiplicative 1-centered Gaussian noise.

As it is a regularization layer, it is only active at training time.

Arguments

 rate: float, drop probability (as with `Dropout`).
    The multiplicative noise will have
    standard deviation `sqrt(rate / (1 - rate))`.

Input shape

 Arbitrary. Use the keyword argument `input_shape`
 (tuple of integers, does not include the samples axis)
 when using this layer as the first layer in a model.

Output shape

 Same shape as input.

References

 - [Dropout: A Simple Way to Prevent Neural Networks from Overfitting](
    http://www.cs.toronto.edu/~rsalakhu/papers/srivastava14a.pdf)
Parameters:
  • args (Object)
  • rate (number) drop probability.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: GaussianDropout

Apply additive zero-centered Gaussian noise.

As it is a regularization layer, it is only active at training time.

This is useful to mitigate overfitting (you could see it as a form of random data augmentation). Gaussian Noise (GS) is a natural choice as corruption process for real valued inputs.

Arguments

 stddev: float, standard deviation of the noise distribution.

Input shape

     Arbitrary. Use the keyword argument `input_shape`
     (tuple of integers, does not include the samples axis)
     when using this layer as the first layer in a model.

Output shape

     Same shape as input.
Parameters:
  • args (Object)
  • stddev (number) Standard Deviation.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: GaussianNoise
tf.layers.masking (args?) function Source

Masks a sequence by using a mask value to skip timesteps.

If all features for a given sample timestep are equal to mask_value, then the sample timestep will be masked (skipped) in all downstream layers (as long as they support masking).

If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.

Arguments

 maskValue: Either None or mask value to skip.

Input shape

 Arbitrary. Use the keyword argument `input_shape`
 (tuple of integers, does not include the samples axis)
 when using this layer as the first layer in a model.

Output shape

 Same shape as input.
Parameters:
  • args (Object) Optional
  • maskValue (number) Masking Value. Defaults to 0.0.
  • inputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchInputShape ((null | number)[]) If defined, will be used to create an input layer to insert before this layer. If both inputShape and batchInputShape are defined, batchInputShape will be used. This argument is only applicable to input layers (the first layer of a model).
  • batchSize (number) If inputShape is specified and batchInputShape is not specified, batchSize is used to construct the batchInputShape: `[batchSize,

    • ...inputShape]`
  • dtype ('float32'|'int32'|'bool'|'complex64'|'string') The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model).
  • name (string) Name for this layer.
  • trainable (boolean) Whether this layer is trainable. Defaults to true.
  • updatable (boolean) Whether the weights of this layer are updatable by fit.
  • weights (tf.Tensor[]) Initial weight values of the layer.
  • inputDType ('float32'|'int32'|'bool'|'complex64'|'string') Legacy support. Do not use for new code.
Returns: tf.layers.Layer

To perform mathematical computation on Tensors, we use operations. Tensors are immutable, so all operations always return new Tensors and never modify input Tensors.

tf.add (a, b) function Source

Adds two tf.Tensors element-wise, A + B. Supports broadcasting.

We also expose tf.addStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 2, 3, 4]);
const b = tf.tensor1d([10, 20, 30, 40]);

a.add(b).print();  // or tf.add(a, b)
// Broadcast add a with b.
const a = tf.scalar(5);
const b = tf.tensor1d([10, 20, 30, 40]);

a.add(b).print();  // or tf.add(a, b)
Parameters:
Returns: tf.Tensor
tf.sub (a, b) function Source

Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

We also expose tf.subStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([10, 20, 30, 40]);
const b = tf.tensor1d([1, 2, 3, 4]);

a.sub(b).print();  // or tf.sub(a, b)
// Broadcast subtract a with b.
const a = tf.tensor1d([10, 20, 30, 40]);
const b = tf.scalar(5);

a.sub(b).print();  // or tf.sub(a, b)
Parameters:
Returns: tf.Tensor
tf.mul (a, b) function Source

Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

We also expose tf.mulStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 2, 3, 4]);
const b = tf.tensor1d([2, 3, 4, 5]);

a.mul(b).print();  // or tf.mul(a, b)
// Broadcast mul a with b.
const a = tf.tensor1d([1, 2, 3, 4]);
const b = tf.scalar(5);

a.mul(b).print();  // or tf.mul(a, b)
Parameters:
Returns: tf.Tensor
tf.div (a, b) function Source

Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

We also expose tf.divStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 4, 9, 16]);
const b = tf.tensor1d([1, 2, 3, 4]);

a.div(b).print();  // or tf.div(a, b)
// Broadcast div a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(2);

a.div(b).print();  // or tf.div(a, b)
Parameters:
Returns: tf.Tensor
tf.addN (tensors) function Source

Adds a list of tf.Tensors element-wise, each with the same shape and dtype.

const a = tf.tensor1d([1, 2]);
const b = tf.tensor1d([3, 4]);
const c = tf.tensor1d([5, 6]);

tf.addN([a, b, c]).print();
Parameters:
  • tensors (Array) A list of tensors with the same shape and dtype.
Returns: tf.Tensor
tf.floorDiv (a, b) function Source

Divides two tf.Tensors element-wise, A / B. Supports broadcasting. The result is rounded with floor function.

const a = tf.tensor1d([1, 4, 9, 16]);
const b = tf.tensor1d([1, 2, 3, 4]);

a.floorDiv(b).print();  // or tf.div(a, b)
// Broadcast div a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(2);

a.floorDiv(b).print();  // or tf.floorDiv(a, b)
Parameters:
Returns: tf.Tensor
tf.maximum (a, b) function Source

Returns the max of a and b (a > b ? a : b) element-wise. Supports broadcasting.

We also expose tf.maximumStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 4, 3, 16]);
const b = tf.tensor1d([1, 2, 9, 4]);

a.maximum(b).print();  // or tf.maximum(a, b)
// Broadcast maximum a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(5);

a.maximum(b).print();  // or tf.maximum(a, b)
Parameters:
Returns: tf.Tensor
tf.minimum (a, b) function Source

Returns the min of a and b (a < b ? a : b) element-wise. Supports broadcasting.

We also expose minimumStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 4, 3, 16]);
const b = tf.tensor1d([1, 2, 9, 4]);

a.minimum(b).print();  // or tf.minimum(a, b)
// Broadcast minimum a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(5);

a.minimum(b).print();  // or tf.minimum(a, b)
Parameters:
Returns: tf.Tensor
tf.mod (a, b) function Source

Returns the mod of a and b element-wise. floor(x / y) * y + mod(x, y) = x Supports broadcasting.

We also expose tf.modStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 4, 3, 16]);
const b = tf.tensor1d([1, 2, 9, 4]);

a.mod(b).print();  // or tf.mod(a, b)
// Broadcast a mod b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(5);

a.mod(b).print();  // or tf.mod(a, b)
Parameters:
Returns: tf.Tensor
tf.pow (base, exp) function Source

Computes the power of one tf.Tensor to another. Supports broadcasting.

Given a tf.Tensor x and a tf.Tensor y, this operation computes x^y for corresponding elements in x and y. The result's dtype will be the upcasted type of the base and exp dtypes.

const a = tf.tensor([[2, 3], [4, 5]])
const b = tf.tensor([[1, 2], [3, 0]]).toInt();

a.pow(b).print();  // or tf.pow(a, b)
const a = tf.tensor([[1, 2], [3, 4]])
const b = tf.tensor(2).toInt();

a.pow(b).print();  // or tf.pow(a, b)

We also expose powStrict which has the same signature as this op and asserts that base and exp are the same shape (does not broadcast).

Parameters:
Returns: tf.Tensor
tf.squaredDifference (a, b) function Source

Returns (a - b) * (a - b) element-wise. Supports broadcasting.

We also expose tf.squaredDifferenceStrict which has the same signature as this op and asserts that a and b are the same shape (does not broadcast).

const a = tf.tensor1d([1, 4, 3, 16]);
const b = tf.tensor1d([1, 2, 9, 4]);

a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
// Broadcast squared difference  a with b.
const a = tf.tensor1d([2, 4, 6, 8]);
const b = tf.scalar(5);

a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)
Parameters:
Returns: tf.Tensor
tf.abs (x) function Source

Computes absolute value element-wise: abs(x)

const x = tf.tensor1d([-1, 2, -3, 4]);

x.abs().print();  // or tf.abs(x)
Parameters:
Returns: tf.Tensor
tf.acos (x) function Source

Computes acos of the input tf.Tensor element-wise: acos(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.acos().print();  // or tf.acos(x)
Parameters:
Returns: tf.Tensor
tf.acosh (x) function Source

Computes the inverse hyperbolic cos of the input tf.Tensor element-wise: acosh(x)

const x = tf.tensor1d([10, 1, 3, 5.7]);

x.acosh().print();  // or tf.acosh(x)
Parameters:
Returns: tf.Tensor
tf.asin (x) function Source

Computes asin of the input tf.Tensor element-wise: asin(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.asin().print();  // or tf.asin(x)
Parameters:
Returns: tf.Tensor
tf.asinh (x) function Source

Computes inverse hyperbolic sin of the input tf.Tensor element-wise: asinh(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.asinh().print();  // or tf.asinh(x)
Parameters:
Returns: tf.Tensor
tf.atan (x) function Source

Computes atan of the input tf.Tensor element-wise: atan(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.atan().print();  // or tf.atan(x)
Parameters:
Returns: tf.Tensor
tf.atan2 (a, b) function Source

Computes arctangent of tf.Tensors a / b element-wise: atan2(a, b). Supports broadcasting.

const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);

tf.atan2(a, b).print()
Parameters:
Returns: tf.Tensor
tf.atanh (x) function Source

Computes inverse hyperbolic tan of the input tf.Tensor element-wise: atanh(x)

const x = tf.tensor1d([0, .1, -.1, .7]);

x.atanh().print();  // or tf.atanh(x)
Parameters:
Returns: tf.Tensor
tf.ceil (x) function Source

Computes ceiling of input tf.Tensor element-wise: ceil(x)

const x = tf.tensor1d([.6, 1.1, -3.3]);

x.ceil().print();  // or tf.ceil(x)
Parameters:
Returns: tf.Tensor
tf.clipByValue (x, clipValueMin, clipValueMax) function Source

Clips values element-wise. max(min(x, clipValueMax), clipValueMin)

const x = tf.tensor1d([-1, 2, -3, 4]);

x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • clipValueMin (number) Lower-bound of range to be clipped to.
  • clipValueMax (number) Upper-bound of range to be clipped to.
Returns: tf.Tensor
tf.cos (x) function Source

Computes cos of the input tf.Tensor element-wise: cos(x)

const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);

x.cos().print();  // or tf.cos(x)
Parameters:
Returns: tf.Tensor
tf.cosh (x) function Source

Computes hyperbolic cos of the input tf.Tensor element-wise: cosh(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.cosh().print();  // or tf.cosh(x)
Parameters:
Returns: tf.Tensor
tf.elu (x) function Source

Computes exponential linear element-wise: x > 0 ? e ^ x - 1 : 0.

const x = tf.tensor1d([-1, 1, -3, 2]);

x.elu().print();  // or tf.elu(x)
Parameters:
Returns: tf.Tensor
tf.erf (x) function Source

Computes gause error function of the input tf.Tensor element-wise: erf(x)

const x = tf.tensor1d([0, .1, -.1, .7]);

x.erf().print(); // or tf.erf(x);
Parameters:
Returns: tf.Tensor
tf.exp (x) function Source

Computes exponential of the input tf.Tensor element-wise. e ^ x

const x = tf.tensor1d([1, 2, -3]);

x.exp().print();  // or tf.exp(x)
Parameters:
Returns: tf.Tensor
tf.expm1 (x) function Source

Computes exponential of the input tf.Tensor minus one element-wise. e ^ x - 1

const x = tf.tensor1d([1, 2, -3]);

x.expm1().print();  // or tf.expm1(x)
Parameters:
Returns: tf.Tensor
tf.floor (x) function Source

Computes floor of input tf.Tensor element-wise: floor(x).

const x = tf.tensor1d([.6, 1.1, -3.3]);

x.floor().print();  // or tf.floor(x)
Parameters:
Returns: tf.Tensor
tf.isFinite (x) function Source

Returns which elements of x are finite.

const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);

x.isFinite().print();  // or tf.isNaN(x)
Parameters:
Returns: tf.Tensor
tf.isInf (x) function Source

Returns which elements of x are Infinity or -Infinity.

const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);

x.isInf().print();  // or tf.isNaN(x)
Parameters:
Returns: tf.Tensor
tf.isNaN (x) function Source

RReturns which elements of x are NaN.

const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]);

x.isNaN().print();  // or tf.isNaN(x)
Parameters:
Returns: tf.Tensor
tf.leakyRelu (x, alpha?) function Source

Computes leaky rectified linear element-wise.

See http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf

const x = tf.tensor1d([-1, 2, -3, 4]);

x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • alpha (number) The scaling factor for negative values, defaults to 0.2. Optional
Returns: tf.Tensor
tf.log (x) function Source

Computes natural logarithm of the input tf.Tensor element-wise: ln(x)

const x = tf.tensor1d([1, 2, Math.E]);

x.log().print();  // or tf.log(x)
Parameters:
Returns: tf.Tensor
tf.log1p (x) function Source

Computes natural logarithm of the input tf.Tensor plus one element-wise: ln(1 + x)

const x = tf.tensor1d([1, 2, Math.E - 1]);

x.log1p().print();  // or tf.log1p(x)
Parameters:
Returns: tf.Tensor
tf.logSigmoid (x) function Source

Computes log sigmoid of the input tf.Tensor element-wise: logSigmoid(x). For numerical stability, we use -tf.softplus(-x).

const x = tf.tensor1d([0, 1, -1, .7]);

x.logSigmoid().print();  // or tf.logSigmoid(x)
Parameters:
Returns: tf.Tensor
tf.neg (x) function Source

Computes -1 * x element-wise.

const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);

x.neg().print();  // or tf.neg(x)
Parameters:
Returns: tf.Tensor
tf.prelu (x, alpha) function Source

Computes leaky rectified linear element-wise with parametric alphas.

x < 0 ? alpha * x : f(x) = x

const x = tf.tensor1d([-1, 2, -3, 4]);
const alpha = tf.scalar(0.1);

x.prelu(alpha).print();  // or tf.prelu(x, alpha)
Parameters:
Returns: tf.Tensor
tf.reciprocal (x) function Source

Computes reciprocal of x element-wise: 1 / x

const x = tf.tensor1d([0, 1, 2]);

x.reciprocal().print();  // or tf.reciprocal(x)
Parameters:
Returns: tf.Tensor
tf.relu (x) function Source

Computes rectified linear element-wise: max(x, 0).

const x = tf.tensor1d([-1, 2, -3, 4]);

x.relu().print();  // or tf.relu(x)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor. If the dtype is bool, the output dtype will be `int32'. *
Returns: tf.Tensor
tf.round (x) function Source

Computes round of input tf.Tensor element-wise: round(x). It implements banker's rounding.

const x = tf.tensor1d([.6, 1.1, -3.3]);

x.round().print();  // or tf.round(x)
Parameters:
Returns: tf.Tensor
tf.rsqrt (x) function Source

Computes reciprocal of square root of the input tf.Tensor element-wise: y = 1 / sqrt(x)

const x = tf.tensor1d([1, 2, 4, -1]);

x.rsqrt().print();  // or tf.rsqrt(x)
Parameters:
Returns: tf.Tensor
tf.selu (x) function Source

Computes scaled exponential linear element-wise.

x < 0 ? scale * alpha * (exp(x) - 1) : x

const x = tf.tensor1d([-1, 2, -3, 4]);

x.selu().print();  // or tf.selu(x)
Parameters:
Returns: tf.Tensor
tf.sigmoid (x) function Source

Computes sigmoid element-wise, 1 / (1 + exp(-x))

const x = tf.tensor1d([0, -1, 2, -3]);

x.sigmoid().print();  // or tf.sigmoid(x)
Parameters:
Returns: tf.Tensor
tf.sign (x) function Source

Returns an element-wise indication of the sign of a number.

const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);

x.sign().print();  // or tf.sign(x)
Parameters:
Returns: tf.Tensor
tf.sin (x) function Source

Computes sin of the input Tensor element-wise: sin(x)

const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);

x.sin().print();  // or tf.sin(x)
Parameters:
Returns: tf.Tensor
tf.sinh (x) function Source

Computes hyperbolic sin of the input tf.Tensor element-wise: sinh(x)

const x = tf.tensor1d([0, 1, -1, .7]);

x.sinh().print();  // or tf.sinh(x)
Parameters:
Returns: tf.Tensor
tf.softplus (x) function Source

Computes softplus of the input tf.Tensor element-wise: log(exp(x) + 1)

const x = tf.tensor1d([0, 1, -1, .7]);

x.softplus().print();  // or tf.softplus(x)
Parameters:
Returns: tf.Tensor
tf.sqrt (x) function Source

Computes square root of the input tf.Tensor element-wise: y = sqrt(x)

const x = tf.tensor1d([1, 2, 4, -1]);

x.sqrt().print();  // or tf.sqrt(x)
Parameters:
Returns: tf.Tensor
tf.square (x) function Source

Computes square of x element-wise: x ^ 2

const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);

x.square().print();  // or tf.square(x)
Parameters:
Returns: tf.Tensor
tf.step (x, alpha?) function Source

Computes step of the input tf.Tensor element-wise: x > 0 ? 1 : alpha * x

const x = tf.tensor1d([0, 2, -1, -3]);

x.step(.5).print();  // or tf.step(x, .5)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • alpha (number) The gradient when input is negative. Optional
Returns: tf.Tensor
tf.tan (x) function Source

Computes tan of the input tf.Tensor element-wise, tan(x)

const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);

x.tan().print();  // or tf.tan(x)
Parameters:
Returns: tf.Tensor
tf.tanh (x) function Source

Computes hyperbolic tangent of the input tf.Tensor element-wise: tanh(x)

const x = tf.tensor1d([0, 1, -1, 70]);

x.tanh().print();  // or tf.tanh(x)
Parameters:
Returns: tf.Tensor
tf.dot (t1, t2) function Source

Computes the dot product of two matrices and/or vectors, t1 and t2.

const a = tf.tensor1d([1, 2]);
const b = tf.tensor2d([[1, 2], [3, 4]]);
const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);

a.dot(b).print();  // or tf.dot(a, b)
b.dot(a).print();
b.dot(c).print();
Parameters:
Returns: tf.Tensor
tf.matMul (a, b, transposeA?, transposeB?) function Source

Computes the dot product of two matrices, A * B. These must be matrices.

const a = tf.tensor2d([1, 2], [1, 2]);
const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);

a.matMul(b).print();  // or tf.matMul(a, b)
Parameters:
  • a (tf.Tensor|TypedArray|Array) First matrix in dot product operation.
  • b (tf.Tensor|TypedArray|Array) Second matrix in dot product operation.
  • transposeA (boolean) If true, a is transposed before multiplication. Optional
  • transposeB (boolean) If true, b is transposed before multiplication. Optional
Returns: tf.Tensor
tf.fused.matMul (a, b, transposeA?, transposeB?, bias?, activation?) function Source

Computes the dot product of two matrices with optional activation and bias.

const a = tf.tensor2d([-1, -2], [1, 2]);
const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);
const c = tf.tensor2d([1, 2], [1, 2]);

tf.fused.matMul(a, b, false, false, 'relu', c);
Parameters:
  • a (tf.Tensor|TypedArray|Array) First matrix in dot product operation.
  • b (tf.Tensor|TypedArray|Array) Second matrix in dot product operation.
  • transposeA (boolean) If true, a is transposed before multiplication. Optional
  • transposeB (boolean) If true, b is transposed before multiplication. Optional
  • bias (tf.Tensor|TypedArray|Array) Matrix to be added to the result. Optional
  • activation (Activation) Name of activation kernel (defaults to linear). Optional
Returns: tf.Tensor
tf.norm (x, ord?, axis?, keepDims?) function Source

Computes the norm of scalar, vectors, and matrices. This function can compute several different vector norms (the 1-norm, the Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) and matrix norms (Frobenius, 1-norm, and inf-norm).

const x = tf.tensor1d([1, 2, 3, 4]);

x.norm().print();  // or tf.norm(x)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input array.
  • ord (number|'euclidean'|'fro') Optional. Order of the norm. Supported norm types are following:

    ord norm for matrices norm for vectors
    'euclidean' Frobenius norm 2-norm
    'fro' Frobenius norm
    Infinity max(sum(abs(x), axis=1)) max(abs(x))
    -Infinity min(sum(abs(x), axis=1)) min(abs(x))
    1 max(sum(abs(x), axis=0)) sum(abs(x))
    2 sum(abs(x)^2)^1/2*
    Optional
  • axis (number|number[]) Optional. If axis is null (the default), the input is considered a vector and a single vector norm is computed over the entire set of values in the Tensor, i.e. norm(x, ord) is equivalent to norm(x.reshape([-1]), ord). If axis is a integer, the input is considered a batch of vectors, and axis determines the axis in x over which to compute vector norms. If axis is a 2-tuple of integer it is considered a batch of matrices and axis determines the axes in NDArray over which to compute a matrix norm. Optional
  • keepDims (boolean) Optional. If true, the norm have the same dimensionality as the input. Optional
Returns: tf.Tensor
tf.outerProduct (v1, v2) function Source

Computes the outer product of two vectors, v1 and v2.

const a = tf.tensor1d([1, 2, 3]);
const b = tf.tensor1d([3, 4, 5]);

tf.outerProduct(a, b).print();
Parameters:
Returns: tf.Tensor2D
tf.transpose (x, perm?) function Source

Transposes the tf.Tensor. Permutes the dimensions according to perm.

The returned tf.Tensor's dimension i will correspond to the input dimension perm[i]. If perm is not given, it is set to [n-1...0], where n is the rank of the input tf.Tensor. Hence by default, this operation performs a regular matrix transpose on 2-D input tf.Tensors.

const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);

a.transpose().print();  // or tf.transpose(a)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The tensor to transpose.
  • perm (number[]) The permutation of the dimensions of a. Optional
Returns: tf.Tensor
tf.avgPool (x, filterSize, strides, pad, dimRoundingMode?) function Source

Computes the 2D average pooling of an image.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3 of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • filterSize ([number, number]|number) The filter size: [filterHeight, filterWidth]. If filterSize is a single number, then filterHeight == filterWidth.
  • strides ([number, number]|number) The strides of the pooling: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.
  • pad ('valid'|'same'|number) The type of padding algorithm:

  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.conv1d (x, filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?) function Source

Computes a 1D convolution over the input x.

Parameters:
  • x (tf.Tensor2D|tf.Tensor3D|TypedArray|Array) The input tensor, of rank 3 or rank 2, of shape [batch, width, inChannels]. If rank 2, batch of 1 is assumed.
  • filter (tf.Tensor3D|TypedArray|Array) The filter, rank 3, of shape [filterWidth, inDepth, outDepth].
  • stride (number) The number of entries by which the filter is moved right at each step.
  • pad ('valid'|'same'|number) The type of padding algorithm.

  • dataFormat ('NWC'|'NCW') An optional string from "NWC", "NCW". Defaults to "NWC", the data is stored in the order of [batch, in_width, in_channels]. Only "NWC" is currently supported. Optional
  • dilation (number) The dilation rate in which we sample input values in atrous convolution. Defaults to 1. If it is greater than 1, then stride must be 1. Optional
  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.conv2d (x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?) function Source

Computes a 2D convolution over the input x.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • filter (tf.Tensor4D|TypedArray|Array) The filter, rank 4, of shape [filterHeight, filterWidth, inDepth, outDepth].
  • strides ([number, number]|number) The strides of the convolution: `[strideHeight,

    • strideWidth]`.
  • pad ('valid'|'same'|number) The type of padding algorithm.

  • dataFormat ('NHWC'|'NCHW') : An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Only "NHWC" is currently supported. Optional
  • dilations ([number, number]|number) The dilation rates: [dilationHeight, dilationWidth] in which we sample input values across the height and width dimensions in atrous convolution. Defaults to [1, 1]. If dilations is a single number, then dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1. Optional
  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.conv2dTranspose (x, filter, outputShape, strides, pad, dimRoundingMode?) function Source

Computes the transposed 2D convolution of an image, also known as a deconvolution.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input image, of rank 4 or rank 3, of shape [batch, height, width, inDepth]. If rank 3, batch of 1 is assumed.
  • filter (tf.Tensor4D|TypedArray|Array) The filter, rank 4, of shape [filterHeight, filterWidth, outDepth, inDepth]. inDepth must match inDepth in x.
  • outputShape ([number, number, number, number]|[number, number, number]) Output shape, of rank 4 or rank 3: [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
  • strides ([number, number]|number) The strides of the original convolution: [strideHeight, strideWidth].
  • pad ('valid'|'same'|number) The type of padding algorithm used in the non-transpose version of the op.
  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.conv3d (x, filter, strides, pad, dataFormat?, dilations?) function Source

Computes a 3D convolution over the input x.

Parameters:
  • x (tf.Tensor4D|tf.Tensor5D|TypedArray|Array) The input tensor, of rank 5 or rank 4, of shape [batch, depth, height, width, channels]. If rank 4, batch of 1 is assumed.
  • filter (tf.Tensor5D|TypedArray|Array) The filter, rank 5, of shape [filterDepth, filterHeight, filterWidth, inChannels, outChannels]. inChannels must match between input and filter.
  • strides ([number, number, number]|number) The strides of the convolution: `[strideDepth, strideHeight,

    • strideWidth]`.
  • pad ('valid'|'same') The type of padding algorithm.

  • dataFormat ('NHWC'|'NCHW') : An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, depth, height, width, channels]. Only "NHWC" is currently supported. Optional
  • dilations ([number, number, number]|number) The dilation rates: `[dilationDepth, dilationHeight,

    • dilationWidth]` in which we sample input values across the height
                    

    and width dimensions in atrous convolution. Defaults to [1, 1, 1]. If dilations is a single number, then dilationDepth == dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1. Optional

tf.depthwiseConv2d (x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?) function Source

Depthwise 2D convolution.

Given a 4D input array and a filter array of shape [filterHeight, filterWidth, inChannels, channelMultiplier] containing inChannels convolutional filters of depth 1, this op applies a different filter to each input channel (expanding from 1 channel to channelMultiplier channels for each), then concatenates the results together. The output has inChannels * channelMultiplier channels.

See https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d for more details.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • filter (tf.Tensor4D|TypedArray|Array) The filter tensor, rank 4, of shape [filterHeight, filterWidth, inChannels, channelMultiplier].
  • strides ([number, number]|number) The strides of the convolution: `[strideHeight,

    • strideWidth]. If strides is a single number, thenstrideHeight ==
    • strideWidth`.
  • pad ('valid'|'same'|number) The type of padding algorithm.

  • dataFormat ('NHWC'|'NCHW') : An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Only "NHWC" is currently supported. Optional
  • dilations ([number, number]|number) The dilation rates: [dilationHeight, dilationWidth] in which we sample input values across the height and width dimensions in atrous convolution. Defaults to [1, 1]. If rate is a single number, then dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1. Optional
  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.maxPool (x, filterSize, strides, pad, dimRoundingMode?) function Source

Computes the 2D max pooling of an image.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3 of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • filterSize ([number, number]|number) The filter size: [filterHeight, filterWidth]. If filterSize is a single number, then filterHeight == filterWidth.
  • strides ([number, number]|number) The strides of the pooling: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth.
  • pad ('valid'|'same'|number) The type of padding algorithm.

  • dimRoundingMode ('floor'|'round'|'ceil') The rounding mode used when computing output dimensions if pad is a number. If none is provided, it will not round and error if the output is of fractional size. Optional
tf.pool (input, windowShape, poolingType, pad, dilations?, strides?) function Source

Performs an N-D pooling operation

Parameters:
  • input (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3 of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • windowShape ([number, number]|number) The filter size: [filterHeight, filterWidth]. If filterSize is a single number, then filterHeight == filterWidth.
  • poolingType ('avg'|'max') The type of pooling, either 'max' or 'avg'.
  • pad ('valid'|'same'|number) The type of padding algorithm:

  • dilations ([number, number]|number) The dilation rates: [dilationHeight, dilationWidth] in which we sample input values across the height and width dimensions in dilated pooling. Defaults to [1, 1]. If dilationRate is a single number, then dilationHeight == dilationWidth. If it is greater than 1, then all values of strides must be 1. Optional
  • strides ([number, number]|number) The strides of the pooling: [strideHeight, strideWidth]. If strides is a single number, then strideHeight == strideWidth. Optional
tf.separableConv2d (x, depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?) function Source

2-D convolution with separable filters.

Performs a depthwise convolution that acts separately on channels followed by a pointwise convolution that mixes channels. Note that this is separability between dimensions [1, 2] and 3, not spatial separability between dimensions 1 and 2.

See https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d for more details.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor, of rank 4 or rank 3, of shape [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
  • depthwiseFilter (tf.Tensor4D|TypedArray|Array) The depthwise filter tensor, rank 4, of shape [filterHeight, filterWidth, inChannels, channelMultiplier]. This is the filter used in the first step.
  • pointwiseFilter (tf.Tensor4D|TypedArray|Array) The pointwise filter tensor, rank 4, of shape [1, 1, inChannels * channelMultiplier, outChannels]. This is the filter used in the second step.
  • strides ([number, number]|number) The strides of the convolution: `[strideHeight,

    • strideWidth]. If strides is a single number, thenstrideHeight ==
    • strideWidth`.
  • pad ('valid'|'same') The type of padding algorithm.

  • dilation ([number, number]|number) Optional
  • dataFormat ('NHWC'|'NCHW') : An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Only "NHWC" is currently supported. Optional
tf.all (x, axis?, keepDims?) function Source

Computes the logical and of elements across dimensions of a tf.Tensor.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and an tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 1, 1], 'bool');

x.all().print();  // or tf.all(x)
const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');

const axis = 1;
x.all(axis).print();  // or tf.all(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor. Must be of dtype bool.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.any (x, axis?, keepDims?) function Source

Computes the logical or of elements across dimensions of a tf.Tensor.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and an tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 1, 1], 'bool');

x.any().print();  // or tf.any(x)
const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');

const axis = 1;
x.any(axis).print();  // or tf.any(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor. Must be of dtype bool.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.argMax (x, axis?) function Source

Returns the indices of the maximum values along an axis.

The result has the same shape as input with the dimension along axis removed.

const x = tf.tensor1d([1, 2, 3]);

x.argMax().print();  // or tf.argMax(x)
const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);

const axis = 1;
x.argMax(axis).print();  // or tf.argMax(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • axis (number) The dimension to reduce. Defaults to 0 (outer-most dimension). Optional
Returns: tf.Tensor
tf.argMin (x, axis?) function Source

Returns the indices of the minimum values along an axis.

The result has the same shape as input with the dimension along axis removed.

const x = tf.tensor1d([1, 2, 3]);

x.argMin().print();  // or tf.argMin(x)
const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);

const axis = 1;
x.argMin(axis).print();  // or tf.argMin(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • axis (number) The dimension to reduce. Defaults to 0 (outer-most dimension). Optional
Returns: tf.Tensor
tf.logSumExp (x, axis?, keepDims?) function Source

Computes the log(sum(exp(elements across the reduction dimensions)).

Reduces the input along the dimensions given in axis. Unless keepDims is true, the rank of the array is reduced by 1 for each entry in axis. If keepDims is true, the reduced dimensions are retained with length 1. If axis has no entries, all dimensions are reduced, and an array with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.logSumExp().print();  // or tf.logSumExp(x)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • axis (number|number[]) The dimension(s) to reduce. If null (the default), reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with length of 1. Defaults to false. Optional
Returns: tf.Tensor
tf.max (x, axis?, keepDims?) function Source

Computes the maximum of elements across dimensions of a tf.Tensor.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and an tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.max().print();  // or tf.max(x)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.max(axis).print();  // or tf.max(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.mean (x, axis?, keepDims?) function Source

Computes the mean of elements across dimensions of a tf.Tensor.

Reduces x along the dimensions given in axis. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axis. If keepDims is true, the reduced dimensions are retained with length 1. If axis has no entries, all dimensions are reduced, and a tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.mean().print();  // or tf.mean(a)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.mean(axis).print();  // or tf.mean(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.min (x, axis?, keepDims?) function Source

Computes the minimum value from the input.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the array is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and an array with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.min().print();  // or tf.min(x)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.min(axis).print();  // or tf.min(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input Tensor.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.prod (x, axis?, keepDims?) function Source

Computes the product of elements across dimensions of a tf.Tensor.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and a tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.prod().print();  // or tf.prod(x)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.prod(axis).print();  // or tf.prod(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to compute the product over. If the dtype is bool it will be converted to int32 and the output dtype will be int32.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.sum (x, axis?, keepDims?) function Source

Computes the sum of elements across dimensions of a tf.Tensor.

Reduces the input along the dimensions given in axes. Unless keepDims is true, the rank of the tf.Tensor is reduced by 1 for each entry in axes. If keepDims is true, the reduced dimensions are retained with length 1. If axes has no entries, all dimensions are reduced, and a tf.Tensor with a single element is returned.

const x = tf.tensor1d([1, 2, 3]);

x.sum().print();  // or tf.sum(x)
const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);

const axis = 1;
x.sum(axis).print();  // or tf.sum(x, axis)
Parameters:
  • x (tf.Tensor|TypedArray|Array) The input tensor to compute the sum over. If the dtype is bool it will be converted to int32 and the output dtype will be int32.
  • axis (number|number[]) The dimension(s) to reduce. By default it reduces all dimensions. Optional
  • keepDims (boolean) If true, retains reduced dimensions with size 1. Optional
Returns: tf.Tensor
tf.batchNorm (x, mean, variance, offset?, scale?, varianceEpsilon?) function Source

Batch normalization.

As described in http://arxiv.org/abs/1502.03167.

Mean, variance, scale, and offset can be of two shapes:

  • The same shape as the input.
  • In the common case, the depth dimension is the last dimension of x, so the values would be an tf.Tensor1D of shape [depth].

Also available are stricter rank-specific methods with the same signature as this method that assert that parameters passed are of given rank

  • tf.batchNorm2d
  • tf.batchNorm3d
  • tf.batchNorm4d
Parameters:
Returns: tf.Tensor
tf.localResponseNormalization (x, depthRadius?, bias?, alpha?, beta?) function Source

Normalizes the activation of a local neighborhood across or within channels.

Parameters:
  • x (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The input tensor. The 4-D input tensor is treated as a 3-D array of 1D vectors (along the last dimension), and each vector is normalized independently.
  • depthRadius (number) The number of adjacent channels in the 1D normalization