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HenryAI
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KerasCodeExamples.txt

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# 1. Get image embeddings
img_embed = self.cnn_model(batch_img)
# 2. Pass each of the five captions one by one to the decoder
# along with the encoder outputs and compute the loss as well as accuracy
# for each caption.
for i in range(self.num_captions_per_image):
with tf.GradientTape() as tape:
loss, acc = self._compute_caption_loss_and_acc(
img_embed, batch_seq[:, i, :], training=True
)
# 3. Update loss and accuracy
batch_loss += loss
batch_acc += acc
# 4. Get the list of all the trainable weights
train_vars = (
self.encoder.trainable_variables + self.decoder.trainable_variables
)
# 5. Get the gradients
grads = tape.gradient(loss, train_vars)
# 6. Update the trainable weights
self.optimizer.apply_gradients(zip(grads, train_vars))
# 7. Update the trackers
batch_acc /= float(self.num_captions_per_image)
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 8. Return the loss and accuracy values
return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}
def test_step(self, batch_data):
batch_img, batch_seq = batch_data
batch_loss = 0
batch_acc = 0
# 1. Get image embeddings
img_embed = self.cnn_model(batch_img)
# 2. Pass each of the five captions one by one to the decoder
# along with the encoder outputs and compute the loss as well as accuracy
# for each caption.
for i in range(self.num_captions_per_image):
loss, acc = self._compute_caption_loss_and_acc(
img_embed, batch_seq[:, i, :], training=False
)
# 3. Update batch loss and batch accuracy
batch_loss += loss
batch_acc += acc
batch_acc /= float(self.num_captions_per_image)
# 4. Update the trackers
self.loss_tracker.update_state(batch_loss)
self.acc_tracker.update_state(batch_acc)
# 5. Return the loss and accuracy values
return {\"loss\": self.loss_tracker.result(), \"acc\": self.acc_tracker.result()}
@property
def metrics(self):
# We need to list our metrics here so the `reset_states()` can be
# called automatically.
return [self.loss_tracker, self.acc_tracker]
cnn_model = get_cnn_model()
encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1)
decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2)
caption_model = ImageCaptioningModel(
cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation,
)
Model training
# Define the loss function
cross_entropy = keras.losses.SparseCategoricalCrossentropy(
from_logits=False, reduction=\"none\"
)
# EarlyStopping criteria
early_stopping = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)
# Learning Rate Scheduler for the optimizer
class LRSchedule(keras.optimizers.schedules.LearningRateSchedule):
def __init__(self, post_warmup_learning_rate, warmup_steps):
super().__init__()
self.post_warmup_learning_rate = post_warmup_learning_rate
self.warmup_steps = warmup_steps
def __call__(self, step):
global_step = tf.cast(step, tf.float32)
warmup_steps = tf.cast(self.warmup_steps, tf.float32)
warmup_progress = global_step / warmup_steps
warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress