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HenryAI
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KerasCodeExamples.txt

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def calculate_loss(self, images, test=False):
# Augment the input images.
if test:
augmented_images = self.test_augmentation_model(images)
else:
augmented_images = self.train_augmentation_model(images)
# Patch the augmented images.
patches = self.patch_layer(augmented_images)
# Encode the patches.
(
unmasked_embeddings,
masked_embeddings,
unmasked_positions,
mask_indices,
unmask_indices,
) = self.patch_encoder(patches)
# Pass the unmaksed patche to the encoder.
encoder_outputs = self.encoder(unmasked_embeddings)
# Create the decoder inputs.
encoder_outputs = encoder_outputs + unmasked_positions
decoder_inputs = tf.concat([encoder_outputs, masked_embeddings], axis=1)
# Decode the inputs.
decoder_outputs = self.decoder(decoder_inputs)
decoder_patches = self.patch_layer(decoder_outputs)
loss_patch = tf.gather(patches, mask_indices, axis=1, batch_dims=1)
loss_output = tf.gather(decoder_patches, mask_indices, axis=1, batch_dims=1)
# Compute the total loss.
total_loss = self.compiled_loss(loss_patch, loss_output)
return total_loss, loss_patch, loss_output
def train_step(self, images):
with tf.GradientTape() as tape:
total_loss, loss_patch, loss_output = self.calculate_loss(images)
# Apply gradients.
train_vars = [
self.train_augmentation_model.trainable_variables,
self.patch_layer.trainable_variables,
self.patch_encoder.trainable_variables,
self.encoder.trainable_variables,
self.decoder.trainable_variables,
]
grads = tape.gradient(total_loss, train_vars)
tv_list = []
for (grad, var) in zip(grads, train_vars):
for g, v in zip(grad, var):
tv_list.append((g, v))
self.optimizer.apply_gradients(tv_list)
# Report progress.
self.compiled_metrics.update_state(loss_patch, loss_output)
return {m.name: m.result() for m in self.metrics}
def test_step(self, images):
total_loss, loss_patch, loss_output = self.calculate_loss(images, test=True)
# Update the trackers.
self.compiled_metrics.update_state(loss_patch, loss_output)
return {m.name: m.result() for m in self.metrics}
Model initialization
train_augmentation_model = get_train_augmentation_model()
test_augmentation_model = get_test_augmentation_model()
patch_layer = Patches()
patch_encoder = PatchEncoder()
encoder = create_encoder()
decoder = create_decoder()
mae_model = MaskedAutoencoder(
train_augmentation_model=train_augmentation_model,
test_augmentation_model=test_augmentation_model,
patch_layer=patch_layer,
patch_encoder=patch_encoder,
encoder=encoder,
decoder=decoder,
)
Training callbacks
Visualization callback
# Taking a batch of test inputs to measure model's progress.
test_images = next(iter(test_ds))
class TrainMonitor(keras.callbacks.Callback):
def __init__(self, epoch_interval=None):
self.epoch_interval = epoch_interval
def on_epoch_end(self, epoch, logs=None):
if self.epoch_interval and epoch % self.epoch_interval == 0:
test_augmented_images = self.model.test_augmentation_model(test_images)
test_patches = self.model.patch_layer(test_augmented_images)
(
test_unmasked_embeddings,