def test_adam(self):
"""Test creating an Adam optimizer."""
opt = optimizers.Adam(learning_rate=0.01)
with self.test_session() as sess:
global_step = tf.Variable(0)
tfopt = opt._create_optimizer(global_step)
assert isinstance(tfopt, tf.train.AdamOptimizer)
def test_linearCosine_decay(self):
"""test creating an optimizer with a linear cosine decay to the learning rate"""
rate = optimizers.LinearCosineDecay(initial_rate=0.1, decay_steps=10000)
opt = optimizers.Adam(learning_rate=rate)
with self.session() as sess:
global_step = tf.Variable(0)
tfopt = opt._create_optimizer(global_step)
def test_polynomial_decay(self):
"""Test creating an optimizer with a polynomially decaying learning rate."""
rate = optimizers.PolynomialDecay(
initial_rate=0.001, final_rate=0.0001, decay_steps=10000)
opt = optimizers.Adam(learning_rate=rate)
with self.test_session() as sess:
global_step = tf.Variable(0)
tfopt = opt._create_optimizer(global_step)
def test_exponential_decay(self):
"""Test creating an optimizer with an exponentially decaying learning rate."""
rate = optimizers.ExponentialDecay(
initial_rate=0.001, decay_rate=0.99, decay_steps=10000)
opt = optimizers.Adam(learning_rate=rate)
with self.session() as sess:
global_step = tf.Variable(0)
tfopt = opt._create_optimizer(global_step)
def train_seqtoseq(train_data, embedding_dimension, tokens, max_length, encoder_layers=1,
decoder_layers=1, dropout=0.1, tb_folder='fingerprint',
batch_size=32, n_epochs=100, steps_per_epoch=None):
train_generator = generate_sequences(train_data, n_epochs)
model = dc.models.SeqToSeq(tokens, tokens, max_length,
encoder_layers=encoder_layers,
decoder_layers=decoder_layers,
embedding_dimension=embedding_dimension,
batch_size=batch_size,
verbose=True,
tensorboard=True,
tensorboard_log_frequency=1,
model_dir=tb_folder)
if steps_per_epoch is None:
steps_per_epoch = min(len(train_data), THRESHOLD_SIZE)/model.batch_size
model.set_optimizer(
dcopt.Adam(learning_rate=dcopt.ExponentialDecay(0.001, 0.95, steps_per_epoch)))
model.fit_sequences(train_generator, checkpoint_interval=steps_per_epoch)
return model
