def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
# Get images and labels for CIFAR-10.
images, labels = cifar10.distorted_inputs()
print(images.shape)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = cifar10.inference(images)
print ("logits shape:", logits.shape)
# Calculate loss.
print ("label shape", labels.shape)
# Calculate loss.
loss = cifar10.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = cifar10.train(loss, global_step)
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams)
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
# Use the pruning_obj to add ops to the training graph to update the masks
# The conditional_mask_update_op will update the masks only when the
# training step is in [begin_pruning_step, end_pruning_step] specified in
# the pruning spec proto
mask_update_op = pruning_obj.conditional_mask_update_op()
# Use the pruning_obj to add summaries to the graph to track the sparsity
# of each of the layers
pruning_obj.add_pruning_summaries()
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
def before_run(self, run_context):
self._step += 1
self._start_time = time.time()
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
duration = time.time() - self._start_time
loss_value = run_values.results
if self._step % 10 == 0:
num_examples_per_step = 128
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()],
config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
# Update the masks
mon_sess.run(mask_update_op)
def train():
"""Train LeNet for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
dataset = mnist.get_split('train', './tmp/LeNet_data')
# Creates a TF-Slim DataProvider which reads the dataset in the background
# during both training and testing.
provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, num_readers=10, shuffle=True)
image, label = provider.get(['image', 'label'])
# batch up some training data
images, labels = tf.train.batch([image, label],
batch_size=LeNet.BATCH_SIZE)
print(images.shape)
images = tf.cast(images, tf.float32)
# images=tf.transpose(images,[1,2,3,0])#tf.reshape(images,[28,28,1,64])
print(images.shape)
# labels=tf.reshape(labels,[128,])
# print (images.shape)
logits = LeNet.inference(images)
print("logits shape:", logits.shape)
# Calculate loss.
print("label shape", labels.shape)
loss = LeNet.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = LeNet.train(loss, global_step)
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(
FLAGS.pruning_hparams)
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
# Use the pruning_obj to add ops to the training graph to update the masks
# The conditional_mask_update_op will update the masks only when the
# training step is in [begin_pruning_step, end_pruning_step] specified in
# the pruning spec proto
mask_update_op = pruning_obj.conditional_mask_update_op()
# Use the pruning_obj to add summaries to the graph to track the sparsity
# of each of the layers
pruning_obj.add_pruning_summaries()
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
def before_run(self, run_context):
self._step += 1
self._start_time = time.time()
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
duration = time.time() - self._start_time
loss_value = run_values.results
if self._step % 10 == 0:
num_examples_per_step = 128
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str %
(datetime.datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
# Update the masks
mon_sess.run(mask_update_op)
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
# images, labels = vgg.distorted_inputs()
dataset = imagenet.get_split('train', '/data/ramyadML/TF-slim-data/imageNet/processed')
# Creates a TF-Slim DataProvider which reads the dataset in the background
# during both training and testing.
provider = slim.dataset_data_provider.DatasetDataProvider(dataset,
num_readers=4,
common_queue_capacity=20*32,
common_queue_min=10*32,
shuffle=True)
preprocessing_name = 'vgg_16'
image_preprocessing_fn = preprocessing_factory.get_preprocessing(
preprocessing_name,
is_training=True)
[image, label] = provider.get(['image', 'label'])
image = image_preprocessing_fn(image, 224, 224)
label -= 1
# batch up some training data
images, labels = tf.train.batch([image, label],
batch_size=32,
num_threads=4,
capacity=5*32)
print (images.shape)
images = tf.cast(images, tf.float32)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = vgg.inference(images)
print ("logits shape:", logits.shape)
# Calculate loss.
print ("label shape", labels.shape)
# Calculate loss.
loss = vgg.loss(logits, labels)
# Save
list_var_names = [ 'vgg_16/conv1/conv1_1/biases',
'vgg_16/conv1/conv1_1/weights',
'vgg_16/conv1/conv1_2/biases',
'vgg_16/conv1/conv1_2/weights',
'vgg_16/conv2/conv2_1/biases',
'vgg_16/conv2/conv2_1/weights',
'vgg_16/conv2/conv2_2/biases',
'vgg_16/conv2/conv2_2/weights',
'vgg_16/conv3/conv3_1/biases',
'vgg_16/conv3/conv3_1/weights',
'vgg_16/conv3/conv3_2/biases',
'vgg_16/conv3/conv3_2/weights',
'vgg_16/conv3/conv3_3/biases',
'vgg_16/conv3/conv3_3/weights',
'vgg_16/conv4/conv4_1/biases',
'vgg_16/conv4/conv4_1/weights',
'vgg_16/conv4/conv4_2/biases',
'vgg_16/conv4/conv4_2/weights',
'vgg_16/conv4/conv4_3/biases',
'vgg_16/conv4/conv4_3/weights',
'vgg_16/conv5/conv5_1/biases',
'vgg_16/conv5/conv5_1/weights',
'vgg_16/conv5/conv5_2/biases',
'vgg_16/conv5/conv5_2/weights',
'vgg_16/conv5/conv5_3/biases',
'vgg_16/conv5/conv5_3/weights',
'vgg_16/fc6/biases',
'vgg_16/fc6/weights',
'vgg_16/fc7/biases',
'vgg_16/fc7/weights',
'vgg_16/fc8/biases',
'vgg_16/fc8/weights']
var_list_to_restore = []
for name in list_var_names:
var_list_to_restore = var_list_to_restore + tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, name)
saver = tf.train.Saver(var_list_to_restore)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = vgg.train(loss, global_step)
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams)
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
# Use the pruning_obj to add ops to the training graph to update the masks
# The conditional_mask_update_op will update the masks only when the
# training step is in [begin_pruning_step, end_pruning_step] specified in
# the pruning spec proto
mask_update_op = pruning_obj.conditional_mask_update_op()
# Use the pruning_obj to add summaries to the graph to track the sparsity
# of each of the layers
pruning_obj.add_pruning_summaries()
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
def before_run(self, run_context):
self._step += 1
self._start_time = time.time()
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
duration = time.time() - self._start_time
loss_value = run_values.results
if self._step % 10 == 0:
num_examples_per_step = 128
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()],
config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement)) as mon_sess:
saver.restore(mon_sess,"trained_weights/vgg_16.ckpt")
while not mon_sess.should_stop():
mon_sess.run(train_op)
# Update the masks
mon_sess.run(mask_update_op)