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τιμολογιο ααδε εισοδος

Hurt Hamerkop answered on March 14, 2023 Popularity 1/10 Helpfulness 1/10

τιμολογιο ααδε εισοδος

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Usage in a custom training loop

# Instantiate an optimizer.
optimizer = tf.keras.optimizers.Adam()

# Iterate over the batches of a dataset.
for x, y in dataset:
    # Open a GradientTape.
    with tf.GradientTape() as tape:
        # Forward pass.
        logits = model(x)
        # Loss value for this batch.
        loss_value = loss_fn(y, logits)

    # Get gradients of loss wrt the weights.
    gradients = tape.gradient(loss_value, model.trainable_weights)

    # Update the weights of the model.
    optimizer.apply_gradients(zip(gradients, model.trainable_weights))

Popularity 1/10 Helpfulness 1/10 Language whatever

Source: keras.io

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Contributed on Mar 14 2023

Hurt Hamerkop

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