def train(total_loss, global_step):
total_sample = 274
num_batches_per_epoch = 274 / 1
""" fix lr """
lr = INITIAL_LEARNING_RATE
loss_averages_op = _add_loss_summaries(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.AdamOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
def train(total_loss, global_step):
global initial_lr
lr = initial_lr
loss_averages_op = _add_loss_summaries(total_loss)
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.AdamOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
def train(total_loss, global_step):
total_sample = 274
num_batches_per_epoch = 274/1
""" fix lr """
lr = INITIAL_LEARNING_RATE
loss_averages_op = _add_loss_summaries(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.AdamOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
def train(total_loss, global_step, learning_rate):
""" fix lr """
lr = learning_rate
loss_averages_op = _add_loss_summaries(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.AdamOptimizer(lr)
grads = opt.compute_gradients(total_loss)
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
tf.summary.histogram(var.op.name + '/gradients', grad)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
trainable = [
v for v in tf.trainable_variables()
if v.name[:-2] not in ["last_loss", "steps_total", "loss_increased_t"]
]
# variables_averages_op = variable_averages.apply(tf.trainable_variables())
variables_averages_op = variable_averages.apply(trainable)
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op