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);
}

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

Decode the first frame of a BMP-encoded image to a 3D Tensor of dtype int32.

Decode the frame(s) of a GIF-encoded image to a 4D Tensor of dtype int32.

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.

Decode a JPEG-encoded image to a 3D Tensor of dtype int32.

Decode a PNG-encoded image to a 3D Tensor of dtype int32.

Encodes an image tensor to JPEG.

Encodes an image tensor to PNG.

Inspect the MetaGraphs of the SavedModel from the provided path. This function will return an array of MetaGraphInfo objects.

Load a TensorFlow SavedModel from disk. TensorFlow SavedModel is different from TensorFlow.js model format. A SavedModel is a directory containing serialized signatures and the states needed to run them. The directory has a saved_model.pb (or saved_model.pbtxt) file storing the actual TensorFlow program, or model, and a set of named signatures, each identifying a function. The directory also has a variables directory contains a standard training checkpoint. The directory may also has a assets directory contains files used by the TensorFlow graph, for example text files used to initialize vocabulary tables. These are supported datatypes: float32, int32, complex64, string.For more information, see this guide: https://www.tensorflow.org/guide/saved_model.