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