Datasets:
HenryAI
/
KerasBERTv1-Data

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loss_value = loss_fn(y, logits)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
train_acc_metric.update_state(y, logits)
return loss_value
Let's do the same with the evaluation step:
@tf.function
def test_step(x, y):
val_logits = model(x, training=False)
val_acc_metric.update_state(y, val_logits)
Now, let's re-run our training loop with this compiled training step:
import time
epochs = 2
for epoch in range(epochs):
print("\nStart of epoch %d" % (epoch,))
start_time = time.time()
# Iterate over the batches of the dataset.
for step, (x_batch_train, y_batch_train) in enumerate(train_dataset):
loss_value = train_step(x_batch_train, y_batch_train)
# Log every 200 batches.
if step % 200 == 0:
print(
"Training loss (for one batch) at step %d: %.4f"
% (step, float(loss_value))
)
print("Seen so far: %d samples" % ((step + 1) * 64))
# Display metrics at the end of each epoch.
train_acc = train_acc_metric.result()
print("Training acc over epoch: %.4f" % (float(train_acc),))
# Reset training metrics at the end of each epoch
train_acc_metric.reset_states()
# Run a validation loop at the end of each epoch.
for x_batch_val, y_batch_val in val_dataset:
test_step(x_batch_val, y_batch_val)
val_acc = val_acc_metric.result()
val_acc_metric.reset_states()
print("Validation acc: %.4f" % (float(val_acc),))
print("Time taken: %.2fs" % (time.time() - start_time))
Start of epoch 0
Training loss (for one batch) at step 0: 0.6483
Seen so far: 64 samples
Training loss (for one batch) at step 200: 0.5966
Seen so far: 12864 samples
Training loss (for one batch) at step 400: 0.5951
Seen so far: 25664 samples
Training loss (for one batch) at step 600: 1.3830
Seen so far: 38464 samples
Training loss (for one batch) at step 800: 0.2758
Seen so far: 51264 samples
Training acc over epoch: 0.8756
Validation acc: 0.8955
Time taken: 1.18s
Start of epoch 1
Training loss (for one batch) at step 0: 0.4447
Seen so far: 64 samples
Training loss (for one batch) at step 200: 0.3794
Seen so far: 12864 samples
Training loss (for one batch) at step 400: 0.4636
Seen so far: 25664 samples
Training loss (for one batch) at step 600: 0.3694
Seen so far: 38464 samples
Training loss (for one batch) at step 800: 0.2763
Seen so far: 51264 samples
Training acc over epoch: 0.8926
Validation acc: 0.9078
Time taken: 0.71s
Much faster, isn't it?
Low-level handling of losses tracked by the model
Layers & models recursively track any losses created during the forward pass by layers that call self.add_loss(value). The resulting list of scalar loss values are available via the property model.losses at the end of the forward pass.
If you want to be using these loss components, you should sum them and add them to the main loss in your training step.
Consider this layer, that creates an activity regularization loss:
class ActivityRegularizationLayer(layers.Layer):
def call(self, inputs):
self.add_loss(1e-2 * tf.reduce_sum(inputs))
return inputs
Let's build a really simple model that uses it:
inputs = keras.Input(shape=(784,), name="digits")
x = layers.Dense(64, activation="relu")(inputs)
# Insert activity regularization as a layer
x = ActivityRegularizationLayer()(x)
x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10, name="predictions")(x)
model = keras.Model(inputs=inputs, outputs=outputs)
Here's what our training step should look like now: