Functions for visualizing training in TensorBoard
Create a summary file writer for TensorBoard.
Example:
const tf = require('@tensorflow/tfjs-node');
const summaryWriter = tf.node.summaryFileWriter('/tmp/tfjs_tb_logdir');
for (let step = 0; step < 100; ++step) {
summaryWriter.scalar('dummyValue', Math.sin(2 * Math.PI * step / 8), step);
}
Parameters:
- logdir (string) Log directory in which the summary data will be written.
-
maxQueue
(number)
Maximum queue length (default:
10). Optional -
flushMillis
(number)
Flush every __ milliseconds (default:
120e3, i.e,120seconds). Optional -
filenameSuffix
(string)
Suffix of the protocol buffer file names to be
written in the
logdir(default:.v2). Optional
Returns:
SummaryFileWriter
Callback for logging to TensorBoard durnig training.
Writes the loss and metric values (if any) to the specified log directory
(logdir) which can be ingested and visualized by TensorBoard.
This callback is usually passed as a callback to tf.Model.fit() or
tf.Model.fitDataset() calls during model training. The frequency at which
the values are logged can be controlled with the updateFreq field of the
configuration object (2nd argument).
Usage example:
// Constructor a toy multilayer-perceptron regressor for demo purpose.
const model = tf.sequential();
model.add(
tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
model.add(tf.layers.dense({units: 1}));
model.compile({
loss: 'meanSquaredError',
optimizer: 'sgd',
metrics: ['MAE']
});
// Generate some random fake data for demo purpose.
const xs = tf.randomUniform([10000, 200]);
const ys = tf.randomUniform([10000, 1]);
const valXs = tf.randomUniform([1000, 200]);
const valYs = tf.randomUniform([1000, 1]);
// Start model training process.
await model.fit(xs, ys, {
epochs: 100,
validationData: [valXs, valYs],
// Add the tensorBoard callback here.
callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
});
Then you can use the following commands to point tensorboard to the logdir:
pip install tensorboard # Unless you've already installed it.
tensorboard --logdir /tmp/fit_logs_1
Parameters:
- logdir (string) Directory to which the logs will be written. Optional
- args (Object) Optional configuration arguments. Optional
-
updateFreq
('batch'|'epoch')
The frequency at which loss and metric values are written to logs.
Currently supported options are:
- 'batch': Write logs at the end of every batch of training, in addition to the end of every epoch of training.
- 'epoch': Write logs at the end of every epoch of training.
Note that writing logs too often slows down the training.
Default: 'epoch'.
Returns:
TensorBoardCallback
Decode the first frame of a BMP-encoded image to a 3D Tensor of dtype
int32.
Parameters:
- contents (Uint8Array) The BMP-encoded image in an Uint8Array.
- channels (number) An optional int. Defaults to 0. Accepted values are 0: use the number of channels in the BMP-encoded image. 3: output an RGB image. 4: output an RGBA image. Optional
Returns:
Tensor3D
Decode the frame(s) of a GIF-encoded image to a 4D Tensor of dtype int32.
Parameters:
- contents (Uint8Array) The GIF-encoded image in an Uint8Array.
Returns:
Tensor4D
Given the encoded bytes of an image, it returns a 3D or 4D tensor of the decoded image. Supports BMP, GIF, JPEG and PNG formats.
Parameters:
- content (Uint8Array) The encoded image in an Uint8Array.
- channels (number) An optional int. Defaults to 0, use the number of channels in the image. Number of color channels for the decoded image. It is used when image is type Png, Bmp, or Jpeg. Optional
-
dtype
(string)
The data type of the result. Only
int32is supported at this time. Optional - expandAnimations (boolean) A boolean which controls the shape of the returned op's output. If True, the returned op will produce a 3-D tensor for PNG, JPEG, and BMP files; and a 4-D tensor for all GIFs, whether animated or not. If, False, the returned op will produce a 3-D tensor for all file types and will truncate animated GIFs to the first frame. Optional
Returns:
Tensor3D|Tensor4D
tf.node.decodeJpeg
(contents, channels?, ratio?, fancyUpscaling?, tryRecoverTruncated?, acceptableFraction?, dctMethod?)
function
Source
Decode a JPEG-encoded image to a 3D Tensor of dtype int32.
Parameters:
- contents (Uint8Array) The JPEG-encoded image in an Uint8Array.
- channels (number) An optional int. Defaults to 0. Accepted values are 0: use the number of channels in the PNG-encoded image. 1: output a grayscale image. 3: output an RGB image. Optional
- ratio (number) An optional int. Defaults to 1. Downscaling ratio. It is used when image is type Jpeg. Optional
- fancyUpscaling (boolean) An optional bool. Defaults to True. If true use a slower but nicer upscaling of the chroma planes. It is used when image is type Jpeg. Optional
- tryRecoverTruncated (boolean) An optional bool. Defaults to False. If true try to recover an image from truncated input. It is used when image is type Jpeg. Optional
- acceptableFraction (number) An optional float. Defaults to 1. The minimum required fraction of lines before a truncated input is accepted. It is used when image is type Jpeg. Optional
- dctMethod (string) An optional string. Defaults to "". string specifying a hint about the algorithm used for decompression. Defaults to "" which maps to a system-specific default. Currently valid values are ["INTEGER_FAST", "INTEGER_ACCURATE"]. The hint may be ignored (e.g., the internal jpeg library changes to a version that does not have that specific option.) It is used when image is type Jpeg. Optional
Returns:
Tensor3D
Decode a PNG-encoded image to a 3D Tensor of dtype int32.
Parameters:
- contents (Uint8Array) The BMP-encoded image in an Uint8Array.
- channels (number) An optional int. Defaults to 0. Accepted values are 0: use the number of channels in the PNG-encoded image. 1: output a grayscale image. 3: output an RGB image. 4: output an RGBA image. Optional
-
dtype
(string)
The data type of the result. Only
int32is supported at this time. Optional
Returns:
Tensor3D