def get_task_training_op(self, graph, losses, task):
"""Get training op for applying gradients to variables.
Subclasses that need to do anything fancy with gradients should override
this method.
Parameters
----------
graph: tf.Graph
Graph for this op
losses: dict
Dictionary mapping task to losses
Returns
-------
A training op.
"""
with graph.as_default():
task_loss = losses[task]
task_root = "task%d_ops" % task
task_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
task_root)
opt = model_ops.optimizer(self.optimizer, self.learning_rate,
self.momentum)
return opt.minimize(task_loss, name='train', var_list=task_vars)
def get_training_op(self, graph, loss):
"""Get training op for applying gradients to variables.
Subclasses that need to do anything fancy with gradients should override
this method.
Returns:
A training op.
"""
with graph.as_default():
opt = model_ops.optimizer(self.optimizer, self.learning_rate, self.momentum)
return opt.minimize(loss, name='train')
def get_training_op(self, graph, loss):
"""Get training op for applying gradients to variables.
Subclasses that need to do anything fancy with gradients should override
this method.
Returns:
A training op.
"""
with graph.as_default():
opt = model_ops.optimizer(self.optimizer, self.learning_rate,
self.momentum)
return opt.minimize(loss, name='train')
def get_task_training_op(self, graph, losses, task):
"""Get training op for applying gradients to variables.
Subclasses that need to do anything fancy with gradients should override
this method.
Parameters
----------
graph: tf.Graph
Graph for this op
losses: dict
Dictionary mapping task to losses
Returns
-------
A training op.
"""
with graph.as_default():
task_loss = losses[task]
task_root = "task%d_ops" % task
task_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, task_root)
opt = model_ops.optimizer(self.optimizer, self.learning_rate, self.momentum)
return opt.minimize(task_loss, name='train', var_list=task_vars)
def fit(self,
dataset,
nb_epoch=10,
max_checkpoints_to_keep=5,
log_every_N_batches=50,
learning_rate=.001,
batch_size=50,
checkpoint_interval=10):
"""Trains the model for a fixed number of epochs.
TODO(rbharath0: This is mostly copied from TensorflowGraphModel. Should
eventually refactor both together.
Parameters
----------
dataset: dc.data.Dataset
nb_epoch: 10
Number of training epochs.
Dataset object holding training data
batch_size: integer. Number of samples per gradient update.
nb_epoch: integer, the number of epochs to train the model.
verbose: 0 for no logging to stdout,
1 for progress bar logging, 2 for one log line per epoch.
initial_epoch: epoch at which to start training
(useful for resuming a previous training run)
checkpoint_interval: int
Frequency at which to write checkpoints, measured in epochs
"""
############################################################## TIMING
time1 = time.time()
############################################################## TIMING
print("Training for %d epochs" % nb_epoch)
with self.graph.as_default():
opt = model_ops.optimizer("adam", learning_rate)
train_op = opt.minimize(self.loss, name='train')
with self.session as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
# Save an initial checkpoint.
saver.save(sess, self._save_path, global_step=0)
for epoch in range(nb_epoch):
avg_loss, n_batches = 0., 0
# TODO(rbharath): Don't support example weighting yet.
for ind, (X_b, y_b, w_b, ids_b) in enumerate(
dataset.iterbatches(batch_size)):
if ind % log_every_N_batches == 0:
print("On batch %d" % ind)
feed_dict = {self.features: X_b, self.labels: y_b}
fetches = [self.outputs] + [train_op, self.loss]
fetched_values = sess.run(fetches, feed_dict=feed_dict)
output = fetched_values[:1]
loss = fetched_values[-1]
avg_loss += loss
y_pred = np.squeeze(np.array(output))
y_b = y_b.flatten()
n_batches += 1
if epoch % checkpoint_interval == checkpoint_interval - 1:
saver.save(sess, self._save_path, global_step=epoch)
avg_loss = float(avg_loss) / n_batches
print('Ending epoch %d: Average loss %g' %
(epoch, avg_loss))
# Always save a final checkpoint when complete.
saver.save(sess, self._save_path, global_step=epoch + 1)
############################################################## TIMING
time2 = time.time()
print("TIMING: model fitting took %0.3f s" % (time2 - time1))
def fit(self,
dataset,
nb_epoch=10,
max_checkpoints_to_keep=5,
log_every_N_batches=50,
learning_rate=.001,
batch_size=50,
checkpoint_interval=10):
"""Trains the model for a fixed number of epochs.
TODO(rbharath0: This is mostly copied from TensorflowGraphModel. Should
eventually refactor both together.
Parameters
----------
dataset: dc.data.Dataset
nb_epoch: 10
Number of training epochs.
Dataset object holding training data
batch_size: integer. Number of samples per gradient update.
nb_epoch: integer, the number of epochs to train the model.
verbose: 0 for no logging to stdout,
1 for progress bar logging, 2 for one log line per epoch.
initial_epoch: epoch at which to start training
(useful for resuming a previous training run)
checkpoint_interval: int
Frequency at which to write checkpoints, measured in epochs
"""
############################################################## TIMING
time1 = time.time()
############################################################## TIMING
print("Training for %d epochs" % nb_epoch)
with self.graph.as_default():
opt = model_ops.optimizer("adam", learning_rate)
train_op = opt.minimize(self.loss, name='train')
with self.session as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
# Save an initial checkpoint.
saver.save(sess, self._save_path, global_step=0)
for epoch in range(nb_epoch):
avg_loss, n_batches = 0., 0
# TODO(rbharath): Don't support example weighting yet.
for ind, (X_b, y_b, w_b,
ids_b) in enumerate(dataset.iterbatches(batch_size)):
if ind % log_every_N_batches == 0:
print("On batch %d" % ind)
feed_dict = {self.features: X_b, self.labels: y_b}
fetches = [self.outputs] + [train_op, self.loss]
fetched_values = sess.run(fetches, feed_dict=feed_dict)
output = fetched_values[:1]
loss = fetched_values[-1]
avg_loss += loss
y_pred = np.squeeze(np.array(output))
y_b = y_b.flatten()
n_batches += 1
if epoch % checkpoint_interval == checkpoint_interval - 1:
saver.save(sess, self._save_path, global_step=epoch)
avg_loss = float(avg_loss) / n_batches
print('Ending epoch %d: Average loss %g' % (epoch, avg_loss))
# Always save a final checkpoint when complete.
saver.save(sess, self._save_path, global_step=epoch + 1)
############################################################## TIMING
time2 = time.time()
print("TIMING: model fitting took %0.3f s" % (time2 - time1))