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TensorFlow

DVCLive allows you to add experiment tracking capabilities to your TensorFlow projects.

Usage

If you prefer the Keras API, check the DVCLive - Keras page.

You need to add Live.log_metric() calls to each place where you would like to log metrics and one single Live.next_step() call to indicate that the epoch has ended.

let's consider the following example, extracted from the official TensorFlow guide:

from dvclive import Live

live = Live()

for epoch in range(epochs):
    start_time = time.time()
    for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
        with tf.GradientTape() as tape:
            logits = model(x_batch_train, training=True)
            loss_value = loss_fn(y_batch_train, logits)
        grads = tape.gradient(loss_value, model.trainable_weights)
        optimizer.apply_gradients(zip(grads, model.trainable_weights))
        train_acc_metric.update_state(y_batch_train, logits)

    live.log_metric("train/accuracy", float(train_acc_metric.result())
    train_acc_metric.reset_states()

    for x_batch_val, y_batch_val in val_dataset:
        val_logits = model(x_batch_val, training=False)
        val_acc_metric.update_state(y_batch_val, val_logits)
    live.log_metric("val/accuracy", float(val_acc_metric.result())
    val_acc_metric.reset_states()

    live.next_step()
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