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batch: A test batch containing the keys \"source\" and \"target\" |
idx_to_token: A List containing the vocabulary tokens corresponding to their indices |
target_start_token_idx: A start token index in the target vocabulary |
target_end_token_idx: An end token index in the target vocabulary |
\"\"\" |
self.batch = batch |
self.target_start_token_idx = target_start_token_idx |
self.target_end_token_idx = target_end_token_idx |
self.idx_to_char = idx_to_token |
def on_epoch_end(self, epoch, logs=None): |
if epoch % 5 != 0: |
return |
source = self.batch[\"source\"] |
target = self.batch[\"target\"].numpy() |
bs = tf.shape(source)[0] |
preds = self.model.generate(source, self.target_start_token_idx) |
preds = preds.numpy() |
for i in range(bs): |
target_text = \"\".join([self.idx_to_char[_] for _ in target[i, :]]) |
prediction = \"\" |
for idx in preds[i, :]: |
prediction += self.idx_to_char[idx] |
if idx == self.target_end_token_idx: |
break |
print(f\"target: {target_text.replace('-','')}\") |
print(f\"prediction: {prediction}\n\") |
Learning rate schedule |
class CustomSchedule(keras.optimizers.schedules.LearningRateSchedule): |
def __init__( |
self, |
init_lr=0.00001, |
lr_after_warmup=0.001, |
final_lr=0.00001, |
warmup_epochs=15, |
decay_epochs=85, |
steps_per_epoch=203, |
): |
super().__init__() |
self.init_lr = init_lr |
self.lr_after_warmup = lr_after_warmup |
self.final_lr = final_lr |
self.warmup_epochs = warmup_epochs |
self.decay_epochs = decay_epochs |
self.steps_per_epoch = steps_per_epoch |
def calculate_lr(self, epoch): |
\"\"\" linear warm up - linear decay \"\"\" |
warmup_lr = ( |
self.init_lr |
+ ((self.lr_after_warmup - self.init_lr) / (self.warmup_epochs - 1)) * epoch |
) |
decay_lr = tf.math.maximum( |
self.final_lr, |
self.lr_after_warmup |
- (epoch - self.warmup_epochs) |
* (self.lr_after_warmup - self.final_lr) |
/ (self.decay_epochs), |
) |
return tf.math.minimum(warmup_lr, decay_lr) |
def __call__(self, step): |
epoch = step // self.steps_per_epoch |
return self.calculate_lr(epoch) |
Create & train the end-to-end model |
batch = next(iter(val_ds)) |
# The vocabulary to convert predicted indices into characters |
idx_to_char = vectorizer.get_vocabulary() |
display_cb = DisplayOutputs( |
batch, idx_to_char, target_start_token_idx=2, target_end_token_idx=3 |
) # set the arguments as per vocabulary index for '<' and '>' |
model = Transformer( |
num_hid=200, |
num_head=2, |
num_feed_forward=400, |
target_maxlen=max_target_len, |
num_layers_enc=4, |
num_layers_dec=1, |
num_classes=34, |
) |
loss_fn = tf.keras.losses.CategoricalCrossentropy( |
from_logits=True, label_smoothing=0.1, |
) |
learning_rate = CustomSchedule( |
init_lr=0.00001, |
lr_after_warmup=0.001, |
final_lr=0.00001, |
warmup_epochs=15, |
decay_epochs=85, |
steps_per_epoch=len(ds), |
) |
optimizer = keras.optimizers.Adam(learning_rate) |
model.compile(optimizer=optimizer, loss=loss_fn) |
history = model.fit(ds, validation_data=val_ds, callbacks=[display_cb], epochs=1) |
203/203 [==============================] - 349s 2s/step - loss: 1.7437 - val_loss: 1.4650 |
target: <he had neither a bed to lie upon nor a coat to his back.> |