Created
May 6, 2020 14:26
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# Define the checkpoint directory to store the checkpoints | |
checkpoint_dir = './training_checkpoints' | |
# Name of the checkpoint files | |
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") | |
# Function for decaying the learning rate. | |
def decay(epoch): | |
if epoch < 3: | |
return 1e-3 | |
elif epoch >= 3 and epoch < 7: | |
return 1e-4 | |
else: | |
return 1e-5 | |
# Define the callbacks | |
callbacks = [ | |
tf.keras.callbacks.TensorBoard(log_dir='./logs'), | |
tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix, | |
save_weights_only=True), | |
tf.keras.callbacks.LearningRateScheduler(decay), | |
PrintLR() | |
] | |
# Callback for printing the LR at the end of each epoch. | |
class PrintLR(tf.keras.callbacks.Callback): | |
def on_epoch_end(self, epoch, logs=None): | |
print('\nLearning rate for epoch {} is {}'.format(epoch + 1, | |
model.optimizer.lr.numpy())) |
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