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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

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') The data type. Optional
Returns: tf.Tensor
tf.scalar (value, dtype?) function

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|[number, number]) The value of the scalar.
• dtype ('float32'|'int32'|'bool'|'complex64') The data type. Optional
Returns: tf.Scalar
tf.tensor1d (values, dtype?) function

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') The data type. Optional
Returns: tf.Tensor1D
tf.tensor2d (values, shape?, dtype?) function

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') The data type. Optional
Returns: tf.Tensor2D
tf.tensor3d (values, shape?, dtype?) function

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') The data type. Optional
Returns: tf.Tensor3D
tf.tensor4d (values, shape?, dtype?) function

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') The data type. Optional
Returns: tf.Tensor4D
tf.tensor5d (values, shape?, dtype?) function

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') The data type. Optional
Returns: tf.Tensor5D
tf.tensor6d (values, shape?, dtype?) function

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') The data type. Optional
Returns: tf.Tensor6D
tf.buffer (shape, dtype?, values?) function

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'|'int32'|'bool'|'complex64') The dtype of the buffer. Defaults to 'float32'. Optional
• values The values of the buffer as TypedArray. Defaults to zeros. Optional
Returns: tf.TensorBuffer
tf.clone (x) function

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

Converts two real numbers to a complex number.

Given a tensor tf.real() representing the real part of a complex number, and a tensor tf.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

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') Data type. Optional
Returns: tf.Tensor2D
tf.fill (shape, value, dtype?) function

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) The scalar value to fill the tensor with.
• dtype ('float32'|'int32'|'bool'|'complex64') The type of an element in the resulting tensor. Defaults to 'float'. Optional
Returns: tf.Tensor
tf.fromPixels (pixels, numChannels?) function

Creates a tf.Tensor from an image.

``````const image = new ImageData(1, 1);
image.data[0] = 100;
image.data[1] = 150;
image.data[2] = 200;
image.data[3] = 255;

tf.fromPixels(image).print();
``````
Parameters:
• pixels The input image to construct the tensor from. The supported image types are all 4-channel.
• numChannels (number) The number of channels of the output tensor. A numChannels value less than 4 allows you to ignore channels. Defaults to 3 (ignores alpha channel of input image). Optional
Returns: tf.Tensor3D
tf.imag (input) function

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

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

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).

``````tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();
``````
Parameters:
• indices (tf.Tensor1D|TypedArray|Array) tf.Tensor1D 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.Tensor2D
tf.ones (shape, dtype?) function

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') The type of an element in the resulting tensor. Defaults to 'float'. Optional
Returns: tf.Tensor
tf.onesLike (x) function

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

Prints information about the tf.Tensor including its data.

``````const verbose = true;
tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);
``````
Parameters:
• x 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

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

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

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

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 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') If set, initialValue will be converted to the given type. Optional
Returns: tf.Variable
tf.zeros (shape, dtype?) function

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') 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

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.

tf.Tensor class

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

Flatten a Tensor to a 1D array.

Returns: tf.Tensor1D
asScalar () method

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

Returns: tf.Scalar
as1D () method

Converts a tf.Tensor to a tf.Tensor1D.

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

Converts a tf.Tensor to a tf.Tensor2D.

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

Converts a tf.Tensor to a tf.Tensor3D.

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

Converts a tf.Tensor to a tf.Tensor4D.

Parameters:
Returns: tf.Tensor4D
asType (dtype) method

Casts a tf.Tensor to a specified dtype.

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

Returns a tf.TensorBuffer that holds the underlying data.

Returns: tf.TensorBuffer
data () method

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

Returns: Promise
dataSync () method

Returns: TypedArray
dispose () method

Disposes tf.Tensor from memory.

Returns: void
toFloat () method

Casts the array to type `float32`

Returns: this
toInt () method

Casts the array to type `int32`

Returns: this
toBool () method

Casts the array to type `bool`

Returns: this
print (verbose?) method

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

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

Reshapes the tensor into the shape of the provided tensor.

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

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

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

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

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

Returns: tf.Tensor
toString (verbose?) method

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

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

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

assign (newValue) method

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 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

Sets a value in the buffer at a given location.

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

Returns the value in the buffer at the provided location.

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

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

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 has `M` dimensions.
• blockShape (number[]) A 1-D array. Must be one of the following types: `int32`, `int64`. Must have shape `[M]`, all values must be >= 1.
• crops (number[][]) A 2-D array. Must be one of the following types: `int32`, `int64`. 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]]`

2. Permute dimensions of `reshaped`to 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]]`

3. 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]]`

4. 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

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') The dtype to cast the input tensor to.
Returns: tf.Tensor
tf.depthToSpace (x, blockSize, dataFormat?) function

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

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

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]);
``````
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

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

This operation divides "spatial" dimensions [1, ..., M] of the input into a grid of blocks of shape block_shape, 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], [4, 1, 1, 1]);
const blockShape = [2, 2];
const paddings = [[0, 0], [0, 0]];

``````
Parameters:
• x (tf.Tensor|TypedArray|Array) A tf.Tensor. N-D with `x.shape` = `[batch] + spatialShape + remainingShape`, where spatialShape has `M` dimensions.
• blockShape (number[]) A 1-D array. Must be one of the following types: `int32`, `int64`. Must have shape `[M]`, all values must be >= 1.
• paddings (number[][]) A 2-D array. Must be one of the following types: `int32`, `int64`. 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 padded_shape.

3. Permute dimensions of reshaped_padded to produce permuted_ reshaped_padded of shape: block_shape + [batch] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

4. Reshape permuted_reshaped_padded to flatten block_shape into the batch dimension, producing an output tensor of shape: [batch * prod(block_shape)] + [padded_shape[1] / block_shape[0], ..., padded_shape[M] / block_shape[M-1]] + remaining_shape

Returns: tf.Tensor
tf.squeeze (x, axis?) function

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

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 (tf.Tensor[]|TypedArray|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

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.Tensor1D|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

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

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

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.stack (tensors, axis?) function

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 (tf.Tensor[]|TypedArray|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

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

This operation creates a new tensor by replicating tf.input() `reps` times. The output tensor's i'th dimension has `input.shape[i] * reps[i]` elements, and the values of tf.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

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.multinomial (logits, numSamples, seed?, normalized?) function

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
Returns:
tf.randomNormal (shape, mean?, stdDev?, dtype?, seed?) function

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

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') 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

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 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.
// Afterwards, TF.js does automatic shape inference.

// 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
// Afterwards, TF.js does automatic shape inference.

// 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 `Layer`s 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. Optional
• name (string) The name of this model. Optional
Returns: tf.Sequential
tf.model (config) function

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.Model, 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();
``````

Parameters:
• config (Object)
• inputs
• outputs
• name (string) Optional
Returns: tf.Model
tf.input (config) function

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

Users should call the tf.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: tf.input() is only necessary when using tf.model(). When using tf.sequential(), specify `inputShape` for the first layer or use `inputLayer` as the first layer.

Parameters:
• config (InputConfig)
• shape (number[]) A shape, not including the batch size. For instance, `shape=[32]` indicates that the expected input will be batches of 32-dimensional vectors. Optional
• batchShape (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. Optional
• 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. Optional
• dtype (DataType) Optional
• sparse (boolean) A boolean specifying whether the placeholder to be created is sparse. Optional

Load the frozen model through url.

Example of loading the MobileNetV2 model and making a prediction with a zero input.

``````const GOOGLE_CLOUD_STORAGE_DIR =
const MODEL_URL = 'mobilenet_v2_1.0_224/tensorflowjs_model.pb';
const WEIGHTS_URL =
'mobilenet_v2_1.0_224/weights_manifest.json';
const zeros = tf.zeros([1, 224, 224, 3]);
model.predict(zeros).print();
``````
Parameters:
• modelUrl (string) url for the model file generated by scripts/convert.py script.
• weightsManifestUrl (string)
• requestOption (RequestInit) options for Request, which allows to send credentials and custom headers. Optional
Returns: Promise

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

Example 1: Save tf.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');

``````

Example 2. Saving tf.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');

``````

Example 3. 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

``````

Example 4. Load a model from an HTTP server.

``````const model = await
model.summary();
``````
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. This path will be interpreted as a relative HTTP path, to which `fetch` will be used to request the model topology and weight manifest JSON. The content of the JSON file is assumed to be a JSON object with the following fields and values:
• 'modelTopology': A JSON object that can be either of:
1. a model architecture JSON consistent with the format of the return value of `keras.Model.to_json()`
2. a full model JSON in the format of `keras.models.save_model()`.
• 'weightsManifest': A TensorFlow.js weights manifest. See the Python converter function `save_model()` for more details. It is also assumed that model weights can be accessed from relative paths described by the `paths` fields in weights manifest.
1. An `tf.io.IOHandler` object that loads model artifacts with its `load` method.
• strict (boolean) 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. Optional
Returns: Promise
tf.io.copyModel (sourceURL, destURL) function

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();
{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
tf.io.listModels () function

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();
{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
tf.io.moveModel (sourceURL, destURL) function

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();
{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
tf.io.removeModel (url) function

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

``````// First create and save a model.
const model = tf.sequential();
{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
tf.registerClass (cls) function

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.FrozenModel extends tfc.InferenceModel class

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

tf.Model extends Container|tfc.InferenceModel class

A tf.Model is a directed, acyclic graph of `Layer`s plus methods for training, evaluation, prediction and saving.

tf.Model is the basic unit of training, inference and evaluation in TensorFlow.js. To create a tf.Model, use tf.model().

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

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 (config) method

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:
• config (Object) a `ModelCompileConfig` 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. Optional
Returns: void
evaluate (x, y, config?) method

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.
• config (Object) A `ModelEvaluateConfig`, containing optional fields. Optional
• batchSize (number) Batch size (Integer). If unspecified, it will default to 32. Optional
• verbose (ModelLoggingVerbosity) Verbosity mode. Optional
• sampleWeight Tensor of weights to weight the contribution of different samples to the loss and metrics. Optional
• steps (number) integer: total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `undefined`. Optional
Returns:
predict (x, config?) method

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.
• config (Object) A `ModelPredictConfig` object containing optional fields. Optional
• batchSize (number) Optional. Batch size (Integer). If unspecified, it will default to 32. Optional
• verbose (boolean) Optional. Verbosity mode. Defaults to false. Optional
Returns:
predictOnBatch (x) method

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 : Input samples, as an Tensor
Returns:
fit (x, y, config?) method

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.
• config (Object) A `ModelFitConfig`, containing optional fields. Optional
• batchSize (number) Number of samples per gradient update. If unspecified, it will default to 32. Optional
• epochs (number) The number of times to iterate over the training data arrays. Optional
• 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. Optional

• callbacks (BaseCallback[]|CustomCallbackConfig|CustomCallbackConfig[]) List of callbacks to be called during training. Can consist of one or more of the following fields: `onTrainBegin`, `onTrainEnd`, `onEpochBegin`, `onEpochEnd`, `onBatchBegin`, `onBatchEnd`. Optional
• 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. Optional
• 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`. Optional
• shuffle (boolean) Whether to shuffle the training data before each epoch. Has no effect when `stepsPerEpoch` is not `null`. Optional
• 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. Optional
• sampleWeight 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(). Optional
• initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run). Optional
• 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. Optional
• validationSteps (number) Only relevant if `stepsPerEpoch` is specified. Total number of steps (batches of samples) to validate before stopping. Optional
• 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.)
Optional
Returns: Promise
save (handlerOrURL, config?) method

Save the configuration and/or weights of the Model.

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 `IOHandler`s as URL-like string shortcuts, such as 'localstorage://' and 'indexeddb://'.

Example 1: Save tf.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');

``````

Example 2. Saving tf.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');

``````

Example 3. Saving tf.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]})]});
``````

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

``````const model = tf.sequential(
{layers: [tf.layers.dense({units: 1, inputShape: [3]})]});
``````
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
Returns: Promise
getLayer (name?, index?) method

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
tf.Sequential extends tf.Model class

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();

// 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();
``````

Adds a layer instance on top of the layer stack.

``````  const model = tf.sequential();
// Note that the untrained model is random at this point.
model.predict(tf.randomNormal([10, 1])).print();
``````
Parameters:
• layer Layer instance.
Returns: void
summary (lineLength?, positions?, printFn?) method

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();
tf.layers.dense({units: 100, inputShape: [10], activation: 'relu'}));

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, config?) method

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.
• config (Object) A `ModelEvaluateConfig`, containing optional fields. Optional
• batchSize (number) Batch size (Integer). If unspecified, it will default to 32. Optional
• verbose (ModelLoggingVerbosity) Verbosity mode. Optional
• sampleWeight Tensor of weights to weight the contribution of different samples to the loss and metrics. Optional
• steps (number) integer: total number of steps (batches of samples) before declaring the evaluation round finished. Ignored with the default value of `undefined`. Optional
Returns:
predict (x, config?) method

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.
• config (Object) Optional
• batchSize (number) Optional. Batch size (Integer). If unspecified, it will default to 32. Optional
• verbose (boolean) Optional. Verbosity mode. Defaults to false. Optional
Returns:
fit (x, y, config?) method

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.
• config (Object) A `ModelFitConfig`, containing optional fields. Optional
• batchSize (number) Number of samples per gradient update. If unspecified, it will default to 32. Optional
• epochs (number) The number of times to iterate over the training data arrays. Optional
• 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. Optional

• callbacks (BaseCallback[]|CustomCallbackConfig|CustomCallbackConfig[]) List of callbacks to be called during training. Can consist of one or more of the following fields: `onTrainBegin`, `onTrainEnd`, `onEpochBegin`, `onEpochEnd`, `onBatchBegin`, `onBatchEnd`. Optional
• 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. Optional
• 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`. Optional
• shuffle (boolean) Whether to shuffle the training data before each epoch. Has no effect when `stepsPerEpoch` is not `null`. Optional
• 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. Optional
• sampleWeight 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(). Optional
• initialEpoch (number) Epoch at which to start training (useful for resuming a previous training run). Optional
• 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. Optional
• validationSteps (number) Only relevant if `stepsPerEpoch` is specified. Total number of steps (batches of samples) to validate before stopping. Optional
• 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.)
Optional
Returns: Promise

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

They are most often encountered when building a graph of `Layer`s for a a tf.Model 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 (config?) function

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:
• config (Object) Optional
• alpha (number) Float `>= 0`. Negative slope coefficient. Defaults to `1.0`. Optional
Returns: tf.layers.Layer
tf.layers.leakyReLU (config?) function

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:
• config (Object) Optional
• alpha (number) Float `>= 0`. Negative slope coefficient. Defaults to `0.3`. Optional
Returns: tf.layers.Layer
tf.layers.reLU (config?) function

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:
• config (Object) Optional
• maxValue (number) Float, the maximum output value. Optional
Returns: tf.layers.Layer
tf.layers.softmax (config?) function

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:
• config (Object) Optional
• axis (number) Integer, axis along which the softmax normalization is applied. Defaults to `-1` (i.e., the last axis). Optional
Returns: tf.layers.Layer
tf.layers.thresholdedReLU (config?) function

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:
• config (Object) Optional
• theta (number) Float >= 0. Threshold location of activation. Optional
Returns: tf.layers.Layer
tf.layers.activation (config) function

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.model({
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:
• config (Object)
• activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh'|string) Name of the activation function to use.
Returns: tf.layers.Layer
tf.layers.dense (config) function

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

If unspecified, no activation is applied. Optional

• useBias (boolean) Whether to apply a bias. Optional
• kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the dense kernel weights matrix. Optional
• biasInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector. Optional
• inputDim (number) If specified, defines inputShape as `[inputDim]`. Optional
• kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the kernel weights. Optional
• biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the bias vector. Optional
• kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the dense kernel weights matrix. Optional
• biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector. Optional
• activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation. Optional
Returns: tf.layers.Layer
tf.layers.dropout (config) function

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:
• config (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)`. Optional

• seed (number) An integer to use as random seed. Optional
Returns: tf.layers.Layer
tf.layers.embedding (config) function

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:
• config (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'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the `embeddings` matrix. Optional
• embeddingsRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the `embeddings` matrix. Optional
• activityRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the activation. Optional
• embeddingsConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the `embeddings` matrix. Optional
• 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). Optional

• 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). Optional

Returns: tf.layers.Layer
tf.layers.flatten (config?) function

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:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.permute (config) function

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();
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:
• config (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.
Returns: tf.layers.Layer
tf.layers.repeatVector (config) function

Repeats the input n times in a new dimension.

``````  const model = tf.sequential();
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:
• config (Object)
• n (number) The integer number of times to repeat the input.
Returns: tf.layers.Layer
tf.layers.reshape (config) function

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:
• config (Object)
• targetShape (number[]) The target shape. Does not include the batch axis.
Returns: tf.layers.Layer
tf.layers.conv1d (config) function

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:
• config (Object)
• filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
Returns: tf.layers.Layer
tf.layers.conv2d (config) function

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:
• config (Object)
• filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
Returns: tf.layers.Layer
tf.layers.conv2dTranspose (config) function

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:
• config (Object)
• filters (number) The dimensionality of the output space (i.e. the number of filters in the convolution).
Returns: tf.layers.Layer
tf.layers.cropping2D (config) function

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();
inputShape: [128, 128, 3]}));
//now output shape is [batch, 124, 124, 3]
``````
Parameters:
• config (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`. Optional

Returns: tf.layers.Layer
tf.layers.depthwiseConv2d (config) function

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:
• config (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. Optional
• depthwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the depthwise kernel matrix. Default: GlorotNormal. Optional
• depthwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the depthwise kernel matrix. Optional
• depthwiseRegularizer ('l1l2'|string|Regularizer) Regulzarizer function for the depthwise kernel matrix. Optional
Returns: tf.layers.Layer
tf.layers.separableConv2d (config) function

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:
• config (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. Optional
• depthwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the depthwise kernel matrix. Optional
• pointwiseInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the pointwise kernel matrix. Optional
• depthwiseRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the depthwise kernel matrix. Optional
• pointwiseRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the pointwise kernel matrix. Optional
• depthwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the depthwise kernel matrix. Optional
• pointwiseConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the pointwise kernel matrix. Optional
Returns: tf.layers.Layer
tf.layers.upSampling2d (config) function

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

Defaults to `[2, 2]`. Optional

• 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"`. Optional

Returns: tf.layers.Layer

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]});
console.log(JSON.stringify(sum.shape));
// You get [null, 2, 2], with the first dimension as the undetermined batch
// dimension.
``````
Parameters:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.average (config?) function

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:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.concatenate (config?) function

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:
• config (Object) Optional
• axis (number) Axis along which to concatenate. Optional
Returns: tf.layers.Layer
tf.layers.maximum (config?) function

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:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.minimum (config?) function

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:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.multiply (config?) function

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:
• config (Object) Optional
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
Returns: tf.layers.Layer
tf.layers.batchNormalization (config?) function

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:
• config (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 tf.batchNormalization(). Optional

• momentum (number) Momentum of the moving average. Defaults to 0.99. Optional
• epsilon (number) Small float added to the variance to avoid dividing by zero. Defaults to 1e-3. Optional
• center (boolean) If `true`, add offset of `beta` to normalized tensor. If `false`, `beta` is ignored. Defaults to `true`. Optional
• 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`. Optional
• betaInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the beta weight. Defaults to 'zeros'. Optional
• gammaInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the gamma weight. Defaults to tf.ones(). Optional
• movingMeanInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the moving mean. Defaults to tf.zeros() Optional
• movingVarianceInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the moving variance. Defaults to 'Ones'. Optional
• betaConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for the beta weight. Optional
• gammaConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint for gamma weight. Optional
• betaRegularizer ('l1l2'|string|Regularizer) Regularizer for the beta weight. Optional
• gammaRegularizer ('l1l2'|string|Regularizer) Regularizer for the gamma weight. Optional
Returns: tf.layers.Layer
tf.layers.averagePooling1d (config) function

Average pooling operation for spatial data.

Input shape: `[batchSize, inLength, channels]`

Output shape: `[batchSize, pooledLength, channels]`

`tf.avgPool1d` is an alias.

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

If `null`, defaults to `poolSize`. Optional

• padding ('valid'|'same'|'causal') How to fill in data that's not an integer multiple of poolSize. Optional
Returns: tf.layers.Layer
tf.layers.averagePooling2d (config) function

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:
• config (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. Optional

• 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`. Optional

• padding ('valid'|'same'|'causal') The padding type to use for the pooling layer. Optional
• dataFormat ('channelsFirst'|'channelsLast') The data format to use for the pooling layer. Optional
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:
• config (Object)
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
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:
• config (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]`. Optional

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:
• config (Object)
• inputShape (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). Optional
• batchInputShape (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). Optional
• batchSize (number) If `inputShape` is specified and `batchInputShape` is not specifiedd, `batchSize` is used to construct the `batchInputShape`: `[batchSize, ...inputShape]` Optional
• dtype (DataType) The data-type for this layer. Defaults to 'float32'. This argument is only applicable to input layers (the first layer of a model). Optional
• name (string) Name for this layer. Optional
• trainable (boolean) Whether this layer is trainable. Defaults to true. Optional
• updatable (boolean) Whether the weights of this layer are updatable by `fit`. Optional
• weights (tf.Tensor[]) Initial weight values of the layer. Optional
• inputDType (DataType) Legacy support. Do not use for new code. Optional
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:
• config (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]`. Optional

Returns: tf.layers.Layer
tf.layers.maxPooling1d (config) function

Max pooling operation for temporal data.

Input shape: `[batchSize, inLength, channels]`

Output shape: `[batchSize, pooledLength, channels]`

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

If `null`, defaults to `poolSize`. Optional

• padding ('valid'|'same'|'causal') How to fill in data that's not an integer multiple of poolSize. Optional
Returns: tf.layers.Layer
tf.layers.maxPooling2d (config) function

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:
• config (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. Optional

• 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`. Optional

• padding ('valid'|'same'|'causal') The padding type to use for the pooling layer. Optional
• dataFormat ('channelsFirst'|'channelsLast') The data format to use for the pooling layer. Optional
Returns: tf.layers.Layer
tf.layers.gru (config) function

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 [tf.input()](#input), due to `returnSequences`: `true`;
// 3rd dimension is the `GRUCell`'s number of units.``````
Parameters:
• config (Object)
• recurrentActivation (string) Activation function to use for the recurrent step.

Defaults to hard sigmoid (`hardSigomid`).

If `null`, no activation is applied. Optional

• 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. Optional

Returns: tf.layers.Layer
tf.layers.gruCell (config) function

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 [tf.input()](#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:
• config (Object)
• recurrentActivation (string) Activation function to use for the recurrent step.

Defaults to hard sigmoid (`hardSigomid`).

If `null`, no activation is applied. Optional

• 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. Optional

Returns: tf.RNNCell
tf.layers.lstm (config) function

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 [tf.input()](#input), due to `returnSequences`: `true`;
// 3rd dimension is the `LSTMCell`'s number of units.``````
Parameters:
• config (Object)
• recurrentActivation (string) Activation function to use for the recurrent step.

Defaults to hard sigmoid (`hardSigomid`).

If `null`, no activation is applied. Optional

• 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.. Optional
• implementation (1|2) 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. Optional
Returns: tf.layers.Layer
tf.layers.lstmCell (config) function

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 [tf.input()](#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:
• config (Object)
• recurrentActivation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh'|string) Activation function to use for the recurrent step.

Defaults to hard sigmoid (`hardSigomid`).

If `null`, no activation is applied. Optional

• 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.. Optional
• implementation (1|2) 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. Optional

Returns: tf.RNNCell
tf.layers.rnn (config) function

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:
Returns: tf.layers.Layer
tf.layers.simpleRNN (config) function

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 [tf.input()](#input), due to `returnSequences`: `true`;
// 3rd dimension is the `SimpleRNNCell`'s number of units.
``````
Parameters:
• config (Object)
• units (number) Positive integer, dimensionality of the output space.
• activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh'|string) Activation function to use.

Defaults to hyperbolic tangent (tf.tanh())

If you pass `null`, no activation will be applied. Optional

• useBias (boolean) Whether the layer uses a bias vector. Optional
• kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'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. Optional
• recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'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. Optional
• biasInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector. Optional
• kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the kernel weights matrix. Optional
• recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the recurrentKernel weights matrix. Optional
• biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector. Optional
• kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the kernel weights matrix. Optional
• recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the recurrentKernel weights matrix. Optional
• biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the bias vector. Optional
• dropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Optional
• recurrentDropout (number) Number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Optional
Returns: tf.layers.Layer
tf.layers.simpleRNNCell (config) function

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 [tf.input()](#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:
• config (Object)
• units (number) units: Positive integer, dimensionality of the output space.
• activation ('elu'|'hardSigmoid'|'linear'|'relu'|'relu6'| 'selu'|'sigmoid'|'softmax'|'softplus'|'softsign'|'tanh'|string) Activation function to use. Default: hyperbolic tangent ('tanh'). If you pass `null`, 'linear' activation will be applied. Optional
• useBias (boolean) Whether the layer uses a bias vector. Optional
• kernelInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'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. Optional
• recurrentInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'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. Optional
• biasInitializer ('constant'|'glorotNormal'|'glorotUniform'| 'heNormal'|'identity'|'leCunNormal'|'ones'|'orthogonal'|'randomNormal'| 'randomUniform'|'truncatedNormal'|'varianceScaling'|'zeros'|string|tf.initializers.Initializer) Initializer for the bias vector. Optional
• kernelRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the `kernel` weights matrix. Optional
• recurrentRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the `recurrent_kernel` weights matrix. Optional
• biasRegularizer ('l1l2'|string|Regularizer) Regularizer function applied to the bias vector. Optional
• kernelConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the `kernel` weights matrix. Optional
• recurrentConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraint function applied to the `recurrentKernel` weights matrix. Optional
• biasConstraint ('maxNorm'|'minMaxNorm'|'nonNeg'|'unitNorm'|string|tf.constraints.Constraint) Constraintfunction applied to the bias vector. Optional
• dropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Optional
• recurrentDropout (number) Float number between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Optional
Returns: tf.RNNCell
tf.layers.stackedRNNCells (config) function

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:
Returns: tf.RNNCell
tf.layers.bidirectional (config) function
Parameters:
• config (Object)
• layer (RNN) The instance of an `RNN` layer to be wrapped.
• mergeMode (BidirectionalMergeMode) Mode by which outputs of the forward and backward RNNs are combinied. If `null` or `undefined`, the output will not be combined, they will be returned as an `Array`. Optional
Returns: Wrapper
tf.layers.timeDistributed (config) function

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();
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`:
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();
layer: tf.layers.conv2d({filters: 64, kernelSize: [3, 3]}),
inputShape: [10, 299, 299, 3],
}));
console.log(JSON.stringify(model.outputs[0].shape));
``````
Parameters:
• config (Object)
• layer The layer to be wrapped.
Returns: tf.layers.Layer
tf.layers.Layer extends serialization.Serializable class

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

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

apply (inputs, kwargs?) method

Builds or executes a `Layer's logic.

When called with tf.Tensor(s), execute the `Layer`s 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});
``````
Parameters:
• inputs a tf.Tensor or tf.SymbolicTensor or an Array of them.
• kwargs (Kwargs) Additional keyword arguments to be passed to `call()`. Optional
Returns:
tf.RNNCell extends Layer class

An RNNCell layer.

tf.layers.inputLayer (config) function

An input layer is an entry point into a tf.Model.

`InputLayer` is generated automatically for tf.Sequential models by specifying the `inputshape` or `batchInputShape` for the first layer. It should not be specified explicitly.

``````// Define a model which simply adds two inputs.
const inputA = tf.input({shape: [3]});
const inputB = tf.input({shape: [3]});
const model = tf.model({inputs: [inputA, inputB], outputs: sum});
const batchSize = 2;
model.predict([tf.ones([batchSize, 3]), tf.ones([batchSize, 3])]).print();
``````
Parameters:
• config (Object)
• inputShape (number[]) Input shape, not including the batch axis. Optional
• batchSize (number) Optional input batch size (integer or null). Optional
• batchInputShape (number[]) Batch input shape, including the batch axis. Optional
• dtype (DataType) Datatype of the input. Optional
• sparse (boolean) Whether the placeholder created is meant to be sparse. Optional
• name (string) Name of the layer. Optional
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:
• config (Object) Optional
• padding (number|[number, number]|[[number, number], [number, number]]) Integer, or `Array` of 2 integers, or `Array` of 2 `Array`s, 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 `Array`s, interpreted as: `[[topPad, bottomPad], [leftPad, rightPad]]`.
Optional
• 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]`. Optional

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.

We also expose `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]);

``````
``````// Broadcast add a with b.
const a = tf.scalar(5);
const b = tf.tensor1d([10, 20, 30, 40]);

``````
Parameters:
Returns: tf.Tensor
tf.sub (a, b) function

Subtracts two tf.Tensors element-wise, A - B. Supports broadcasting.

We also expose `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

Multiplies two tf.Tensors element-wise, A * B. Supports broadcasting.

We also expose `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

Divides two tf.Tensors element-wise, A / B. Supports broadcasting.

We also expose `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

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]);

``````
Parameters:
• tensors (Array) A list of tensors with the same shape and dtype.
Returns: tf.Tensor
tf.floorDiv (a, b) function

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

Returns the max of a and b (`a > b ? a : b`) element-wise. Supports broadcasting.

We also expose `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

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

Returns the mod of a and b element-wise. `floor(x / y) * y + mod(x, y) = x` Supports broadcasting.

We also expose `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

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 tf.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 tf.exp() are the same shape (does not broadcast).

Parameters:
Returns: tf.Tensor
tf.squaredDifference (a, b) function

Returns (a - b) * (a - b) element-wise. Supports broadcasting.

We also expose `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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.leakyRelu (x, alpha?) function

Computes leaky rectified linear element-wise.

``````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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.norm (x, ord?, axis?, keepDims?) function

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

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

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

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, a tuple `[filterHeight, filterWidth]`.
• strides ([number, number]|number) The strides of the pooling: `[strideHeight, strideWidth]`.

• 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
Returns:
tf.conv1d (x, filter, stride, pad, dataFormat?, dilation?, dimRoundingMode?) function

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.

• 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
Returns:
tf.conv2d (x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?) function

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]`.

• 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
Returns:
tf.conv2dTranspose (x, filter, outputShape, strides, pad, dimRoundingMode?) function

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
Returns:
tf.depthwiseConv2d (x, filter, strides, pad, dataFormat?, dilations?, dimRoundingMode?) function

Depthwise 2D convolution.

Given a 4D tf.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, then `strideHeight == strideWidth`.

• 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
Returns:
tf.maxPool (x, filterSize, strides, pad, dimRoundingMode?) function

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, a tuple `[filterHeight, filterWidth]`.
• strides ([number, number]|number) The strides of the pooling: `[strideHeight, strideWidth]`.

• 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
Returns:
tf.separableConv2d (x, depthwiseFilter, pointwiseFilter, strides, pad, dilation?, dataFormat?) function

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, then `strideHeight == strideWidth`.

• 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
Returns:
tf.all (x, axis?, keepDims?) function

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]);

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

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]);

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

Returns the indices of the maximum values along an `axis`.

The result has the same shape as tf.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

Returns the indices of the minimum values along an `axis`.

The result has the same shape as tf.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

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

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

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

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.sum (x, axis?, keepDims?) function

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.batchNormalization (x, mean, variance, varianceEpsilon?, scale?, offset?) function

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.batchNormalization2d`
• `tf.batchNormalization3d`
• `tf.batchNormalization4d`
Parameters:
Returns: tf.Tensor
tf.localResponseNormalization (x, depthRadius?, bias?, alpha?, beta?) function

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 window. Optional
• bias (number) A constant bias term for the basis. Optional
• alpha (number) A scale factor, usually positive. Optional
• beta (number) An exponent. Optional
Returns:
tf.moments (x, axis?, keepDims?) function

Calculates the mean and variance of `x`. The mean and variance are calculated by aggregating the contents of `x` across `axes`. If `x` is 1-D and `axes = [0]` this is just the mean and variance of a vector.

Parameters:
• x (tf.Tensor|TypedArray|Array) The input tensor.
• axis (number|number[]) The dimension(s) along with to compute mean and variance. By default it reduces all dimensions. Optional
• keepDims (boolean) If true, the moments have the same dimensionality as the input. Optional
Returns: {mean: tf.Tensor, variance: tf.Tensor}
tf.softmax (logits, dim?) function

Computes the softmax normalized vector given the logits.

``````const a = tf.tensor1d([1, 2, 3]);

a.softmax().print();  // or tf.softmax(a)
``````
``````const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);

a.softmax().print();  // or tf.softmax(a)
``````
Parameters:
• logits (tf.Tensor|TypedArray|Array) The logits array.
• dim (number) The dimension softmax would be performed on. Defaults to `-1` which indicates the last dimension. Optional
Returns: tf.Tensor
tf.image.cropAndResize (image, boxes, boxInd, cropSize, method?, extrapolationValue?) function

Extracts crops from the input image tensor and resizes them using bilinear sampling or nearest neighbor sampling (possibly with aspect ratio change) to a common output size specified by crop_size.

Parameters:
• image (tf.Tensor4D|TypedArray|Array) 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`, where imageHeight and imageWidth must be positive, specifying the batch of images from which to take crops
• boxes (tf.Tensor2D|TypedArray|Array) 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized coordinates of the box in the boxInd[i]'th image in the batch
• boxInd (tf.Tensor1D|TypedArray|Array) 1d int32 tensor of shape `[numBoxes]` with values in range `[0, batch)` that specifies the image that the `i`-th box refers to.
• cropSize ([number, number]) 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]` specifying the size to which all crops are resized to.
• method ('bilinear'|'nearest') Optional string from `'bilinear' | 'nearest'`, defaults to bilinear, which specifies the sampling method for resizing Optional
• extrapolationValue (number) A threshold for deciding when to remove boxes based on score. Defaults to 0. Optional
Returns: tf.Tensor4D
tf.image.nonMaxSuppression (boxes, scores, maxOutputSize, iouThreshold?, scoreThreshold?) function

Performs non maximum suppression of bounding boxes based on iou (intersection over union)

Parameters:
• boxes (tf.Tensor2D|TypedArray|Array) a 2d tensor of shape `[numBoxes, 4]`. Each entry is `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of the bounding box.
• scores (tf.Tensor1D|TypedArray|Array) a 1d tensor providing the box scores of shape `[numBoxes]`.
• maxOutputSize (number) The maximum number of boxes to be selected.
• iouThreshold (number) A float representing the threshold for deciding whether boxes overlap too much with respect to IOU. Must be between [0, 1]. Defaults to 0.5 (50% box overlap). Optional
• scoreThreshold (tf.any()) A threshold for deciding when to remove boxes based on score. Defaults to -inf, which means any score is accepted. Optional
Returns: tf.Tensor1D
tf.image.resizeBilinear (images, size, alignCorners?) function

Bilinear resize a batch of 3D images to a new shape.

Parameters:
• images (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The images, of rank 4 or rank 3, of shape `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
• size ([number, number]) The new shape `[newHeight, newWidth]` to resize the images to. Each channel is resized individually.
• alignCorners (boolean) Defaults to False. If true, rescale input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 corners of images and resized images. If false, rescale by `new_height / height`. Treat similarly the width dimension. Optional
Returns:
tf.image.resizeNearestNeighbor (images, size, alignCorners?) function

NearestNeighbor resize a batch of 3D images to a new shape.

Parameters:
• images (tf.Tensor3D|tf.Tensor4D|TypedArray|Array) The images, of rank 4 or rank 3, of shape `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
• size ([number, number]) The new shape `[newHeight, newWidth]` to resize the images to. Each channel is resized individually.
• alignCorners (boolean) Defaults to False. If true, rescale input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 corners of images and resized images. If false, rescale by `new_height / height`. Treat similarly the width dimension. Optional
Returns:
tf.basicLSTMCell (forgetBias, lstmKernel, lstmBias, data, c, h) function

Computes the next state and output of a BasicLSTMCell.

Returns `[newC, newH]`.

Derived from tf.contrib.rnn.BasicLSTMCell.

Parameters:
Returns:
tf.multiRNNCell (lstmCells, data, c, h) function

Computes the next states and outputs of a stack of LSTMCells.

Each cell output is used as input to the next cell.

Returns `[cellState, cellOutput]`.

Derived from tf.contrib.rn.MultiRNNCell.

Parameters:
Returns: [tf.Tensor2D[], tf.Tensor2D[]]
tf.equal (a, b) function

Returns the truth value of (a == b) element-wise. Supports broadcasting.

We also expose `equalStrict` 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]);
const b = tf.tensor1d([2, 2, 2]);

a.equal(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.greater (a, b) function

Returns the truth value of (a > b) element-wise. Supports broadcasting.

We also expose `greaterStrict` 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]);
const b = tf.tensor1d([2, 2, 2]);

a.greater(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.greaterEqual (a, b) function

Returns the truth value of (a >= b) element-wise. Supports broadcasting.

We also expose `greaterEqualStrict` 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]);
const b = tf.tensor1d([2, 2, 2]);

a.greaterEqual(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.less (a, b) function

Returns the truth value of (a < b) element-wise. Supports broadcasting.

We also expose `lessStrict` 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]);
const b = tf.tensor1d([2, 2, 2]);

a.less(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.lessEqual (a, b) function

Returns the truth value of (a <= b) element-wise. Supports broadcasting.

We also expose `lessEqualStrict` 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]);
const b = tf.tensor1d([2, 2, 2]);

a.lessEqual(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.logicalAnd (a, b) function

Returns the truth value of a AND b element-wise. Supports broadcasting.

``````const a = tf.tensor1d([false, false, true, true], 'bool');
const b = tf.tensor1d([false, true, false, true], 'bool');

a.logicalAnd(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.logicalNot (x) function

Returns the truth value of `NOT x` element-wise.

``````const a = tf.tensor1d([false, true], 'bool');

a.logicalNot().print();
``````
Parameters:
Returns: tf.Tensor
tf.logicalOr (a, b) function

Returns the truth value of `a OR b` element-wise. Supports broadcasting.

``````const a = tf.tensor1d([false, false, true, true], 'bool');
const b = tf.tensor1d([false, true, false, true], 'bool');

a.logicalOr(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.logicalXor (a, b) function

Returns the truth value of `a XOR b` element-wise. Supports broadcasting.

``````const a = tf.tensor1d([false, false, true, true], 'bool');
const b = tf.tensor1d([false, true, false, true], 'bool');

a.logicalXor(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.notEqual (a, b) function

Returns the truth value of (a != b) element-wise. Supports broadcasting.

We also expose `notEqualStrict` 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]);
const b = tf.tensor1d([0, 2, 3]);

a.notEqual(b).print();
``````
Parameters:
Returns: tf.Tensor
tf.where (condition, a, b) function

Returns the elements, either `a` or `b` depending on the `condition`.

If the condition is true, select from `a`, otherwise select from `b`.

``````const cond = tf.tensor1d([false, false, true], 'bool');
const a = tf.tensor1d([1 , 2, 3]);
const b = tf.tensor1d([-1, -2, -3]);

a.where(cond, b).print();
``````
Parameters:
• condition (tf.Tensor|TypedArray|Array) The input condition. Must be of dtype bool.
• a (tf.Tensor|TypedArray|Array) If `condition` is rank 1, `a` may have a higher rank but its first dimension must match the size of `condition`.
• b (tf.Tensor|TypedArray|Array) A tensor with the same shape and type as `a`.
Returns: tf.Tensor
tf.cumsum (x, axis?, exclusive?, reverse?) function

Computes the cumulative sum of a tf.Tensor along `axis`.

``````const x = tf.tensor([1, 2, 3, 4]);
x.cumsum().print();
``````
``````const x = tf.tensor([[1, 2], [3, 4]]);
x.cumsum().print();
``````
Parameters:
• x (tf.Tensor|TypedArray|Array) The input tensor to be summed.
• axis (number) The axis along which to sum. Optional. Defaults to 0. Optional
• exclusive (boolean) Whether to perform exclusive cumulative sum. Optional. 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. Optional. Defaults to false. Optional
Returns: tf.Tensor
tf.unsortedSegmentSum (x, segmentIds, numSegments) function

Computes the sum along segments of a tf.Tensor.

``````const x = tf.tensor1d([1, 2, 3, 4]);
const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');
const numSegments = 3;

x.unsortedSegmentSum(segmentIds, numSegments).print()
//or tf.unsortedSegmentSum(x, segmentIds, numSegments)
``````
Parameters:
• x (tf.Tensor|TypedArray|Array) The tf.Tensor that will be summed along its segments.
• segmentIds (tf.Tensor1D|TypedArray|Array) A tf.Tensor1D whose rank is equal to the rank of `x`'s dimension along the `axis`. Maps each element of `x` to a segment.
• numSegments (number) The number of distinct `segmentIds`.
Returns: tf.Tensor
tf.movingAverage (v, x, decay, step?, zeroDebias?) function

Compute the moving average of a variable.

Without zeroDebias, the moving average operation is defined by: `v += delta` where `delta = (1 - decay) * (x - v)`

With zeroDebias (default), the `delta` term is scaled to debias the effect of the (assumed) zero-initialization of `v`. `delta /= (1 - decay ^ step)`

For more details on the zero-debiasing algorithm, see: https://arxiv.org/abs/1412.6980

Note that this function is completely stateless and does not keep track of step count. The step count needs to be maintained by the caller and passed in as tf.step().

Parameters:
• v (tf.Tensor|TypedArray|Array) The current moving average value.
• x (tf.Tensor|TypedArray|Array) New input value, must have the same shape and dtype as `v`.
• decay (number|tf.Scalar) The decay factor. Typical values are 0.95 and 0.99.
• step (number|tf.Scalar) Step count. Optional
• zeroDebias (boolean) : Whether zeroDebias is to be performed (default: `true`). Optional
Returns: tf.Tensor

Extracts a strided slice of a tensor.

Roughly speaking, this op extracts a slice of size (end-begin)/stride from the given input_ tensor. Starting at the location specified by begin the slice continues by adding stride to the index until all dimensions are not less than end. Note that a stride can be negative, which causes a reverse slice.

``````t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
[3, 2, 3]);
t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]
t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],
// [4, 4, 4]]]
t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],
// [3, 3, 3]]]
``````
Parameters:
• x (tf.Tensor|TypedArray|Array) The tensor to stride slice.
• begin (number[]) The coordinates to start the slice from.
• end (number[]) : The coordinates to end the slice at.
• strides (number[]) : The size of the slice.
• beginMask (number) : If the ith bit of begin_mask is set, begin[i] is ignored and the fullest possible range in that dimension is used instead. Optional
• endMask (number) : If the ith bit of end_mask is set, end[i] is ignored and the fullest possible range in that dimension is used instead. Optional
• shrinkAxisMask (number) : a bitmask where bit i implies that the ith specification should shrink the dimensionality. begin and end must imply a slice of size 1 in the dimension. Optional
Returns: tf.Tensor
tf.topk (x, k?, sorted?) function

Finds the values and indices of the `k` largest entries along the last dimension.

If the input is a vector (rank=1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j]. For higher rank inputs, computes the top k entries along the last dimension.

If two elements are equal, the lower-index element appears first.

``````const a = tf.tensor2d([[1, 5], [4, 3]]);
const {values, indices} = tf.topk(a);
values.print();
indices.print();
``````
Parameters:
• x (tf.Tensor|TypedArray|Array) 1-D or higher tf.Tensor with last dimension being at least `k`.
• k (number) Number of top elements to look for along the last dimension. Optional
• sorted (boolean) If true, the resulting `k` elements will be sorted by the values in descending order. Optional
Returns: {values: tf.Tensor, indices: tf.Tensor}
tf.linalg.gramSchmidt (xs) function

Gram-Schmidt orthogonalization.

``````const x = tf.tensor2d([[1, 2], [3, 4]]);
let y = tf.linalg.gramSchmidt(x);
y.print();
console.log('Othogonalized:');
y.dot(y.transpose()).print();  // should be nearly the identity matrix.
console.log('First row direction maintained:');
console.log(y.get(0, 1) / y.get(0, 0));  // should be nearly 2.
``````
Parameters:
• xs The vectors to be orthogonalized, in one of the two following formats:

• An Array of tf.Tensor1D.
• A tf.Tensor2D, i.e., a matrix, in which case the vectors are the rows of `xs`. In each case, all the vectors must have the same length and the length must be greater than or equal to the number of vectors.
Returns:
tf.linalg.qr (x, fullMatrices?) function

Compute QR decomposition of m-by-n matrix using Householder transformation.

Implementation based on [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf] (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)

``````const a = tf.tensor2d([[1, 2], [3, 4]]);
let [q, r] = tf.linalg.qr(a);
console.log('Q');
q.print();
console.log('R');
r.print();
console.log('Orthogonalized');
q.dot(q.transpose()).print()  // should be nearly the identity matrix.
console.log('Reconstructed');
q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];
``````
Parameters:
• x The tf.Tensor to be QR-decomposed. Must have rank >= 2. Suppose it has the shape `[..., M, N]`.
• fullMatrices (boolean) An optional boolean parameter. Defaults to `false`. If `true`, compute full-sized `Q`. If `false` (the default), compute only the leading N columns of `Q` and `R`. Optional
Returns:

We also provide an API to do perform training, and compute gradients. We compute gradients eagerly, users provide a function that is a combination of operations and we automatically differentiate that function's output with respect to its inputs.

For those familiar with TensorFlow, the API we expose exactly mirrors the TensorFlow Eager API.

Provided `f(x)`, returns another function `g(x, dy?)`, which gives the gradient of `f(x)` with respect to `x`.

If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to `x` is computed instead. `f(x)` must take a single tensor `x` and return a single tensor `y`. If `f()` takes multiple inputs, use tf.grads() instead.

``````// f(x) = x ^ 2
const f = x => x.square();
// f'(x) = 2x

const x = tf.tensor1d([2, 3]);
g(x).print();
``````
``````// f(x) = x ^ 3
const f = x => x.pow(tf.scalar(3, 'int32'));
// f'(x) = 3x ^ 2
// f''(x) = 6x

const x = tf.tensor1d([2, 3]);
gg(x).print();
``````
Parameters:
Returns: ( x: tf.Tensor, dy?: tf.Tensor) => tf.Tensor

Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`, which gives an array of gradients of `f()` with respect to each input [`x1`,`x2`,...].

If `dy` is passed when calling `g()`, the gradient of `f(x1,...).mul(dy).sum()` with respect to each input is computed instead. The provided `f` must take one or more tensors and return a single tensor `y`. If `f()` takes a single input, we recommend using tf.grad() instead.

``````// f(a, b) = a * b
const f = (a, b) => a.mul(b);
// df / da = b, df / db = a

const a = tf.tensor1d([2, 3]);
const b = tf.tensor1d([-2, -3]);
const [da, db] = g([a, b]);
console.log('da');
da.print();
console.log('db');
db.print();
``````
Parameters:
• f ((...args: tf.Tensor[]) => tf.Tensor) The function `f(x1, x2,...)` to compute gradients for.
Returns: ( args: tf.Tensor[], dy?: tf.Tensor) => tf.Tensor[]

Overrides the gradient computation of a function `f`.

Takes a function `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns another function `g(...inputs)` which takes the same inputs as `f`. When called, `g` returns `f().value`. In backward mode, custom gradients with respect to each input of `f` are computed using `f().gradFunc`.

``````const customOp = tf.customGrad(x => {
// Override gradient of our custom x ^ 2 op to be dy * abs(x);
return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};
});

const x = tf.tensor1d([-1, -2, 3]);
const dx = tf.grad(x => customOp(x));

console.log(`f(x):`);
customOp(x).print();
console.log(`f'(x):`);
dx(x).print();
``````
Parameters:
• f ((a: tf.Tensor, b: tf.Tensor,...) => { value: tf.Tensor, * gradFunc: (dy: tf.Tensor) => tf.Tensor | tf.Tensor[] * }) The function to evaluate in forward mode, which should return `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns the custom gradients of `f` with respect to its inputs.
Returns: (...args: tf.Tensor[]) => tf.Tensor

Like tf.grad(), but also returns the value of `f()`. Useful when `f()` returns a metric you want to show.

The result is a rich object with the following properties:

• grad: The gradient of `f(x)` w.r.t `x` (result of tf.grad()).
• value: The value returned by `f(x)`.
``````// f(x) = x ^ 2
const f = x => x.square();
// f'(x) = 2x

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

console.log('value');
value.print();
``````
Parameters:
Returns: ( x: tf.Tensor, dy?: tf.Tensor) => { value: tf.Tensor; grad: tf.Tensor; }

Like tf.grads(), but returns also the value of `f()`. Useful when `f()` returns a metric you want to show.

The result is a rich object with the following properties:

• grads: The gradients of `f()` w.r.t each input (result of tf.grads()).
• value: The value returned by `f(x)`.
``````// f(a, b) = a * b
const f = (a, b) => a.mul(b);
// df/da = b, df/db = a

const a = tf.tensor1d([2, 3]);
const b = tf.tensor1d([-2, -3]);
const {value, grads} = g([a, b]);

console.log('value');
value.print();

console.log('da');
da.print();
console.log('db');
db.print();
``````
Parameters:
Returns: ( args: tf.Tensor[], dy?: tf.Tensor) => { grads: tf.Tensor[]; value: tf.Tensor; }

Computes and returns the gradient of f(x) with respect to the list of trainable variables provided by `varList`. If no list is provided, it defaults to all trainable variables.

``````const a = tf.variable(tf.tensor1d([3, 4]));
const b = tf.variable(tf.tensor1d([5, 6]));
const x = tf.tensor1d([1, 2]);

// f(a, b) = a * x ^ 2 + b * x
const f = () => a.mul(x.square()).add(b.mul(x)).sum();
// df/da = x ^ 2, df/db = x

``````
Parameters:
• f (() => tf.Scalar) The function to execute. f() should return a scalar.
• varList (tf.Variable[]) The list of trainable variables. Defaults to all variables. Optional
Returns: {value: tf.Scalar, grads: {[name: string]: tf.Tensor}}
tf.train.sgd (learningRate) function

Constructs a tf.SGDOptimizer that uses stochastic gradient descent.

``````// Fit a quadratic function by learning the coefficients a, b, c.
const xs = tf.tensor1d([0, 1, 2, 3]);
const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);

const a = tf.scalar(Math.random()).variable();
const b = tf.scalar(Math.random()).variable();
const c = tf.scalar(Math.random()).variable();

// y = a * x^2 + b * x + c.
const loss = (pred, label) => pred.sub(label).square().mean();

const learningRate = 0.01;
const optimizer = tf.train.sgd(learningRate);

// Train the model.
for (let i = 0; i < 10; i++) {
optimizer.minimize(() => loss(f(xs), ys));
}

// Make predictions.
console.log(
`a: \${a.dataSync()}, b: \${b.dataSync()}, c: \${c.dataSync()}`);
const preds = f(xs).dataSync();
preds.forEach((pred, i) => {
console.log(`x: \${i}, pred: \${pred}`);
});
``````
Parameters:
• learningRate (number) The learning rate to use for the SGD algorithm.
Returns: tf.SGDOptimizer
tf.train.momentum (learningRate, momentum, useNesterov?) function

Constructs a tf.MomentumOptimizer that uses momentum gradient descent.

Parameters:
• learningRate (number) The learning rate to use for the Momentum gradient descent algorithm.
• momentum (number) The momentum to use for the momentum gradient descent algorithm.
• useNesterov (boolean) Optional
Parameters:
• initialAccumulatorValue (number) Starting value for the accumulators, must be positive. Optional

Parameters:
• rho (number) The learning rate decay over each update. Optional
• epsilon (number) A constant epsilon used to better condition the grad update. Optional
tf.train.adam (learningRate?, beta1?, beta2?, epsilon?) function

Constructs a `AdamOptimizer` that uses the Adam algorithm. See https://arxiv.org/abs/1412.6980

Parameters:
• learningRate (number) The learning rate to use for the Adam gradient descent algorithm. Optional
• beta1 (number) The exponential decay rate for the 1st moment estimates. Optional
• beta2 (number) The exponential decay rate for the 2nd moment estimates. Optional
• epsilon (number) A small constant for numerical stability. Optional
tf.train.adamax (learningRate?, beta1?, beta2?, epsilon?, decay?) function

Constructs a `AdamaxOptimizer` that uses the Adamax algorithm. See https://arxiv.org/abs/1412.6980

Parameters:
• learningRate (number) The learning rate to use for the Adamax gradient descent algorithm. Optional
• beta1 (number) The exponential decay rate for the 1st moment estimates. Optional
• beta2 (number) The exponential decay rate for the 2nd moment estimates. Optional
• epsilon (number) A small constant for numerical stability. Optional
• decay (number) The learning rate decay over each update. Optional
tf.train.rmsprop (learningRate, decay?, momentum?, epsilon?, centered?) function

Constructs a tf.RMSPropOptimizer that uses RMSProp gradient descent. This implementation uses plain momentum and is not centered version of RMSProp.

Parameters:
• learningRate (number) The learning rate to use for the RMSProp gradient descent algorithm.
• decay (number) The discounting factor for the history/coming gradient. Optional
• momentum (number) The momentum to use for the RMSProp gradient descent algorithm. Optional
• epsilon (number) Small value to avoid zero denominator. Optional
• centered (boolean) If true, gradients are normalized by the estimated variance of the gradient. Optional
tf.losses.absoluteDifference (labels, predictions, weights?, reduction?) function

Computes the absolute difference loss between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.computeWeightedLoss (losses, weights?, reduction?) function

Computes the weighted loss between two tensors.

Parameters:
• losses (tf.Tensor|TypedArray|Array) Tensor of shape `[batch_size, d1, ... dN]`.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `losses`, and must be broadcastable to `losses` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• reduction (Reduction) Optional
Returns: tf.Tensor
tf.losses.cosineDistance (labels, predictions, axis, weights?, reduction?) function

Computes the cosine distance loss between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• axis (number) The dimension along which the cosine distance is computed.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.hingeLoss (labels, predictions, weights?, reduction?) function

Computes the Hinge loss between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.huberLoss (labels, predictions, weights?, delta?, reduction?) function

Computes the huber loss between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• delta (number) Point where huber loss changes from quadratic to linear. Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction`. Optional
Returns: tf.Tensor
tf.losses.logLoss (labels, predictions, weights?, epsilon?, reduction?) function

Computes the log loss between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• epsilon (number) A small increment to avoid taking log of zero Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.meanSquaredError (labels, predictions, weights?, reduction?) function

Computes the mean squared error between two tensors.

Parameters:
• labels (tf.Tensor|TypedArray|Array) The ground truth output tensor, same dimensions as 'predictions'.
• predictions (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.sigmoidCrossEntropy (multiClassLabels, logits, weights?, labelSmoothing?, reduction?) function

Computes the sigmoid cross entropy loss between two tensors.

If labelSmoothing is nonzero, smooth the labels towards 1/2:

newMulticlassLabels = multiclassLabels * (1 - labelSmoothing) + 0.5 * labelSmoothing

Parameters:
• multiClassLabels (tf.Tensor|TypedArray|Array) The ground truth output tensor of shape [batch_size, num_classes], same dimensions as 'predictions'.
• logits (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or the same rank as `labels`, and must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `losses` dimension). Optional
• labelSmoothing (number) If greater than 0, then smooth the labels. Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.losses.softmaxCrossEntropy (onehotLabels, logits, weights?, labelSmoothing?, reduction?) function

Computes the softmax cross entropy loss between two tensors.

If labelSmoothing is nonzero, smooth the labels towards 1/2:

newOnehotLabels = onehotLabels * (1 - labelSmoothing) + labelSmoothing / numClasses

Parameters:
• onehotLabels (tf.Tensor|TypedArray|Array) One hot encoded labels [batch_size, num_classes], same dimensions as 'predictions'.
• logits (tf.Tensor|TypedArray|Array) The predicted outputs.
• weights (tf.Tensor|TypedArray|Array) Tensor whose rank is either 0, or 1, and must be broadcastable to `loss` of shape [batch_size] Optional
• labelSmoothing (number) If greater than 0, then smooth the labels. Optional
• reduction (Reduction) Type of reduction to apply to loss. Should be of type `Reduction` Optional
Returns: tf.Tensor
tf.train.Optimizer extends Serializable class
minimize (f, returnCost?, varList?) method

Executes `f()` and minimizes the scalar output of `f()` by computing gradients of y with respect to the list of trainable variables provided by `varList`. If no list is provided, it defaults to all trainable variables.

Parameters:
• f (() => tf.Scalar) The function to execute and whose output to minimize.
• returnCost (boolean) Whether to return the scalar cost value produced by executing `f()`. Optional
• varList (tf.Variable[]) An optional list of variables to update. If specified, only the trainable variables in varList will be updated by minimize. Defaults to all trainable variables. Optional
Returns: tf.Scalar |null

Executes the provided function `fn` and after it is executed, cleans up all intermediate tensors allocated by `fn` except those returned by `fn`. `f` must not return a Promise (async functions not allowed). The returned result can be a complex object, however tidy only walks the top-level properties (depth 1) of that object to search for tensors, or lists of tensors that need to be tracked in the parent scope.

Using this method helps avoid memory leaks. In general, wrap calls to operations in tf.tidy() for automatic memory cleanup.

When in safe mode, you must enclose all tf.Tensor creation and ops inside a tf.tidy() to prevent memory leaks.

``````// y = 2 ^ 2 + 1
const y = tf.tidy(() => {
// a, b, and one will be cleaned up when the tidy ends.
const one = tf.scalar(1);
const a = tf.scalar(2);
const b = a.square();

console.log('numTensors (in tidy): ' + tf.memory().numTensors);

// The value returned inside the tidy function will return
// through the tidy, in this case to the variable y.
});

console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
y.print();
``````
Parameters:
• nameOrFn (string|Function) The name of the closure, or the function to execute. If a name is provided, the 2nd argument should be the function. If debug mode is on, the timing and the memory usage of the function will be tracked and displayed on the console using the provided name.
• fn (Function) The function to execute. Optional
Returns: void|number|string|tf.Tensor|tf.Tensor[]|{[key: string]:tf.Tensor|number|string}
tf.dispose (container) function

Disposes any tf.Tensors found within the provided object.

Parameters:
• container (void|number|string|tf.Tensor|tf.Tensor[]|{[key: string]:tf.Tensor|number|string}) an object that may be a tf.Tensor or may directly contain tf.Tensors, such as a `Tensor[]` or `{key: Tensor, ...}`. If the object is not a tf.Tensor or does not contain `Tensors`, nothing happens. In general it is safe to pass any object here, except that `Promise`s are not supported.
Returns: void
tf.keep (result) function

Keeps a tf.Tensor generated inside a tf.tidy() from being disposed automatically.

``````let b;
const y = tf.tidy(() => {
const one = tf.scalar(1);
const a = tf.scalar(2);

// b will not be cleaned up by the tidy. a and one will be cleaned up
// when the tidy ends.
b = tf.keep(a.square());

console.log('numTensors (in tidy): ' + tf.memory().numTensors);

// The value returned inside the tidy function will return
// through the tidy, in this case to the variable y.
});

console.log('numTensors (outside tidy): ' + tf.memory().numTensors);
console.log('y:');
y.print();
console.log('b:');
b.print();
``````
Parameters:
• result The tensor to keep from being disposed.
Returns: tf.Tensor
tf.memory () function

Returns memory info at the current time in the program. The result is an object with the following properties:

• `numBytes`: Number of bytes allocated (undisposed) at this time.
• `numTensors`: Number of unique tensors allocated.
• `numDataBuffers`: Number of unique data buffers allocated (undisposed) at this time, which is â‰¤ the number of tensors (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same data buffer with `a`).
• `unreliable`: `Optional` `boolean`:
• On WebGL, not present (always reliable).
• On CPU, true. Due to automatic garbage collection, these numbers represent undisposed tensors, i.e. not wrapped in `tidy()`, or lacking a call to `tensor.dispose()`.
Returns: MemoryInfo
tf.time (f) function

Executes `f()` and returns a promise that resolves with timing information.

The result is an object with the following properties:

• `wallMs`: Wall execution time.
• `kernelMs`: Kernel execution time, ignoring data transfer.
• On `WebGL` The following additional properties exist:
• `uploadWaitMs`: CPU blocking time on texture uploads.
• `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).
``````const x = tf.randomNormal([20, 20]);
const time = await tf.time(() => x.matMul(x));

console.log(`kernelMs: \${time.kernelMs}, wallTimeMs: \${time.wallMs}`);
``````
Parameters:
• f (() => void) The function to execute and time.
Returns: Promise
tf.nextFrame () function

Returns a promise that resolve when a requestAnimationFrame has completed.

On Node.js this uses setImmediate instead of requestAnimationFrame.

This is simply a sugar method so that users can do the following: `await tf.nextFrame();`

Returns: Promise
tf.profile (f) function

Executes the provided function `f()` and returns a promise that resolves with information about the function's memory use:

• `newBytes`: tne number of new bytes allocated
• `newTensors`: the number of new tensors created
• `peakBytes`: the peak number of bytes allocated
• `kernels`: an array of objects for each kernel involved that reports their input and output shapes, number of bytes used, and number of new tensors created.
``````const profile = await tf.profile(() => {
const x = tf.tensor1d([1, 2, 3]);
let x2 = x.square();
x2.dispose();
x2 = x.square();
x2.dispose();
return x;
});

console.log(`newBytes: \${profile.newBytes}`);
console.log(`newTensors: \${profile.newTensors}`);
console.log(`byte usage over all kernels: \${profile.kernels.map(k =>
k.totalBytesSnapshot)}`);
``````
Parameters:
Returns: Promise

TensorFlow.js can run mathematical operations on different backends. Currently, we support WebGL and JavaScript CPU. By default, we choose the 'best' backend available, but allow users to customize their backend.

Dispose all variables kept in backend engine.

Returns: void
tf.getBackend () function

Returns the current backend name (cpu, webgl, etc). The backend is responsible for creating tensors and executing operations on those tensors.

Returns: string
tf.setBackend (backendName, safeMode?) function

Sets the backend (cpu, webgl, etc) responsible for creating tensors and executing operations on those tensors.

Note this disposes the current backend, if any, as well as any tensors associated with it. A new backend is initialized, even if it is of the same type as the previous one.

Parameters:
• backendName (string) The name of the backend. Currently supports `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js (requires tfjs-node).
• safeMode (boolean) Defaults to false. In safe mode, you are forced to construct tensors and call math operations inside a `tidy()` which will automatically clean up intermediate tensors. Optional
Returns: void

Constraints are added to attributes of a Layer (such as weights, kernels, or biases) at construction time to clamp, or otherwise enforce an allowed range, of values for different components of the Layer.

tf.constraints.Constraint extends serialization.Serializable class

Base class for functions that impose constraints on weight values

tf.constraints.maxNorm (config) function

MaxNorm weight constraint.

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Parameters:
• config (Object)
• maxValue (number) Maximum norm for incoming weights Optional
• axis (number) Axis along which to calculate norms.

For instance, in a `Dense` layer the weight matrix has shape `[inputDim, outputDim]`, set `axis` to `0` to constrain each weight vector of length `[inputDim,]`. In a `Conv2D` layer with `dataFormat="channels_last"`, the weight tensor has shape `[rows, cols, inputDepth, outputDepth]`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `[rows, cols, inputDepth]`. Optional

tf.constraints.minMaxNorm (config) function
Parameters:
• config (Object)
• minValue (number) Minimum norm for incoming weights Optional
• maxValue (number) Maximum norm for incoming weights Optional
• axis (number) Axis along which to calculate norms. For instance, in a `Dense` layer the weight matrix has shape `[inputDim, outputDim]`, set `axis` to `0` to constrain each weight vector of length `[inputDim,]`. In a `Conv2D` layer with `dataFormat="channels_last"`, the weight tensor has shape `[rows, cols, inputDepth, outputDepth]`, set `axis` to `[0, 1, 2]` to constrain the weights of each filter tensor of size `[rows, cols, inputDepth]`. Optional
• rate (number) Rate for enforcing the constraint: weights will be rescaled to yield: `(1 - rate) * norm + rate * norm.clip(minValue, maxValue)`. Effectively, this means that rate=1.0 stands for strict enforcement of the constraint, while rate<1.0 means that weights will be rescaled at each step to slowly move towards a value inside the desired interval. Optional

Constains the weight to be non-negative.

tf.constraints.unitNorm (config) function

Constrains the weights incident to each hidden unit to have unit norm.

Parameters:
• config (Object)
• axis (number) Axis along which to calculate norms.

For instance, in a `Dense` layer the weight matrix has shape `[inputDim, outputDim]`, set `axis` to `0` to constrain each weight vector of length `[inputDim,]`. In a `Conv2D` layer with `dataFormat="channels_last"`, the weight tensor has shape [rows, cols, inputDepth, outputDepth]`, set`axis`to`[0, 1, 2]`to constrain the weights of each filter tensor of size`[rows, cols, inputDepth]`. Optional

Initializers are used in Layers to establish the starting the values of weights, biases, kernels, etc.

tf.initializers.Initializer extends serialization.Serializable class

Initializer base class.

tf.initializers.constant (config) function

Initializer that generates values initialized to some constant.

Parameters:
• config (Object)
• value (number) The value for each element in the variable.

Glorot normal initializer, also called Xavier normal initializer. It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / (fan_in + fan_out))` where `fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units in the weight tensor.

Reference: Glorot & Bengio, AISTATS 2010 http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf

Parameters:
• config (Object)
• seed (number) Random number generator seed. Optional

Glorot uniform initializer, also called Xavier uniform initializer. It draws samples from a uniform distribution within [-limit, limit] where `limit` is `sqrt(6 / (fan_in + fan_out))` where `fan_in` is the number of input units in the weight tensor and `fan_out` is the number of output units in the weight tensor

Reference: Glorot & Bengio, AISTATS 2010 http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf.

Parameters:
• config (Object)
• seed (number) Random number generator seed. Optional
tf.initializers.heNormal (config) function

He normal initializer.

It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(2 / fanIn)` where `fanIn` is the number of input units in the weight tensor.

Reference: He et al., http://arxiv.org/abs/1502.01852

Parameters:
• config (Object)
• seed (number) Random number generator seed. Optional
tf.initializers.identity (config) function

Initializer that generates the identity matrix. Only use for square 2D matrices.

Parameters:
• config (Object)
• gain (number) Multiplicative factor to apply to the identity matrix. Optional
tf.initializers.leCunNormal (config) function

LeCun normal initializer.

It draws samples from a truncated normal distribution centered on 0 with `stddev = sqrt(1 / fanIn)` where `fanIn` is the number of input units in the weight tensor.

Parameters:
• config (Object)
• seed (number) Random number generator seed. Optional

Initializer that generates tensors initialized to 1.

tf.initializers.orthogonal (config) function

Initializer that generates a random orthogonal matrix.

Parameters:
• config (Object)
• gain (number) Multiplicative factor to apply to the orthogonal matrix. Defaults to 1. Optional

Initializer that generates random values initialized to a normal distribution.

Parameters:
• config (Object)
• mean (number) Mean of the random values to generate. Optional
• stddev (number) Standard deviation of the random values to generate. Optional
• seed (number) Used to seed the random generator. Optional

Initializer that generates random values initialized to a uniform distribution.

Values will be distributed uniformly between the configured minval and maxval.

Parameters:
• config (Object)
• minval (number) Lower bound of the range of random values to generate. Optional
• maxval (number) Upper bound of the range of random values to generate. Optional
• seed (number) Used to seed the random generator. Optional

Initializer that generates random values initialized to a truncated normal. distribution.

These values are similar to values from a `RandomNormal` except that values more than two standard deviations from the mean are discarded and re-drawn. This is the recommended initializer for neural network weights and filters.

Parameters:
• config (Object)
• mean (number) Mean of the random values to generate. Optional
• stddev (number) Standard deviation of the random values to generate. Optional
• seed (number) Used to seed the random generator. Optional

Initializer capable of adapting its scale to the shape of weights. With distribution=NORMAL, samples are drawn from a truncated normal distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:

• number of input units in the weight tensor, if mode = FAN_IN.
• number of output units, if mode = FAN_OUT.
• average of the numbers of input and output units, if mode = FAN_AVG. With distribution=UNIFORM, samples are drawn from a uniform distribution within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
Parameters:
• config (Object)
• scale (number) Scaling factor (positive float).
• mode ('fanIn'|'fanOut'|'fanAvg') Fanning mode for inputs and outputs.
• distribution ('normal'|'uniform') Probabilistic distribution of the values.
• seed (number) Random number generator seed. Optional

Initializer that generates tensors initialized to 0.

Returns: Zeros

Regularizers can be attached to various components of a Layer to add a 'scoring' function to help drive weights, or other trainable values, away from excessively large values. They're typically used to promote a notion that a 'simpler' model is better than a complicated model, assuming equal performance.

tf.regularizers.L1L2 extends Regularizer class

Regularizer for L1 and L2 regularization.

Adds a term to the loss to penalize large weights: loss += sum(l1 * abs(x)) + sum(l2 * x^2)

tf.regularizers.l1 (config?) function

Regularizer for L1 and L2 regularization.

Adds a term to the loss to penalize large weights: loss += sum(l1 * abs(x)) + sum(l2 * x^2)

Parameters:
• config (Object) Optional
• l1 (number) L1 regularization rate. Defaults to 0.01.
Returns: Regularizer
tf.regularizers.l1l2 (config?) function

Regularizer for L1 and L2 regularization.

Adds a term to the loss to penalize large weights: loss += sum(l1 * abs(x)) + sum(l2 * x^2)

Parameters:
• config (Object) Optional
• l1 (number) L1 regularization rate. Defaults to 0.01. Optional
• l2 (number) L2 regularization rate. Defaults to 0.01. Optional
Returns: Regularizer
tf.regularizers.l2 (config?) function

Regularizer for L1 and L2 regularization.

Adds a term to the loss to penalize large weights: loss += sum(l1 * abs(x)) + sum(l2 * x^2)

Parameters:
• config (Object) Optional
• l2 (number) L2 regularization rate. Defaults to 0.01.
Returns: Regularizer
tf.toPixels (img, canvas?) function

Draws a tf.Tensor of pixel values to a byte array or optionally a canvas.

When the dtype of the input is 'float32', we assume values in the range [0-1]. Otherwise, when input is 'int32', we assume values in the range [0-255].

Returns a promise that resolves when the canvas has been drawn to.

Parameters:
• img (tf.Tensor2D|tf.Tensor3D|TypedArray|Array) A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If rank-3, must have depth of 1, 3 or 4. When depth of 1, draws grayscale. When depth of 3, we draw with the first three components of the depth dimension corresponding to r, g, b and alpha = 1. When depth of 4, all four components of the depth dimension correspond to r, g, b, a.
• canvas The canvas to draw to. Optional
Returns: Promise
tf.metrics.binaryAccuracy (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.binaryCrossentropy (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.categoricalAccuracy (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.categoricalCrossentropy (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.cosineProximity (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.meanAbsoluteError (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.meanSquaredError (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.precision (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor
tf.metrics.recall (yTrue, yPred) function
Parameters:
• yTrue
• yPred
Returns: tf.Tensor