def prepare_model(self):
"""Prepares the model for training."""
# Set the Keras directory.
set_keras_base_directory()
# Deserialize the Keras model.
self.model = deserialize_keras_model(self.model)
# Compile the model with the specified loss and optimizer.
self.model.compile(loss=self.loss, optimizer=self.optimizer, metrics=self.metrics)
def __init__(self, keras_model, loss, worker_optimizer):
set_keras_base_directory()
self.master_model = serialize_keras_model(keras_model)
self.loss = loss
self.worker_optimizer = worker_optimizer
self.history = []
self.training_time_start = 0
self.training_time_end = 0
self.training_time = 0
def __init__(self, keras_model, loss, worker_optimizer, metrics=["accuracy"]):
set_keras_base_directory()
self.master_model = serialize_keras_model(keras_model)
self.loss = loss
self.worker_optimizer = worker_optimizer
self.metrics = metrics
self.history = []
self.training_time_start = 0
self.training_time_end = 0
self.training_time = 0
self.max_mini_batches_prefetch = 100
def __init__(self, keras_model, loss, worker_optimizer, metrics=["accuracy"], loss_weights=None):
set_keras_base_directory()
self.master_model = serialize_keras_model(keras_model)
self.loss = loss
self.loss_weights = loss_weights
self.worker_optimizer = worker_optimizer
self.metrics = metrics
self.history = []
self.training_time_start = 0
self.training_time_end = 0
self.training_time = 0
self.max_mini_batches_prefetch = 100
def prepare_model(self):
"""Prepares the model for training."""
# Set the Keras directory.
set_keras_base_directory()
if K.backend() == 'tensorflow':
# set GPU option allow_growth to False for GPU-enabled tensorflow
config = tf.ConfigProto()
config.gpu_options.allow_growth = False
sess = tf.Session(config=config)
K.set_session(sess)
# Deserialize the Keras model.
self.model = deserialize_keras_model(self.model)
self.optimizer = deserialize(self.optimizer)
# Compile the model with the specified loss and optimizer.
self.model.compile(loss=self.loss, loss_weights = self.loss_weights,
optimizer=self.optimizer, metrics=self.metrics)