def train(re_train=True):
"""Train CIFAR-10 for a number of steps."""
images, labels = get_images_and_labels()
# print "found " + str(len(images)) + " images"
train_size = 5
train_images = images[:train_size, :, :, :]
train_labels = labels[:train_size]
val_images = images[train_size:, :, :, :]
val_labels = labels[train_size:]
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size)
print (images.get_shape(), val_images.get_shape())
logits = my_cifar.inference(images_placeholder)
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy #
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary('Acc', acc)
# tf.scalar_summary('Val Acc', acc_val)
tf.scalar_summary('Loss', loss)
tf.image_summary('Images', tf.reshape(images, shape=[-1, 40, 40, 3]), max_images=10)
tf.image_summary('Val Images', tf.reshape(val_images, shape=[-1, 40, 40, 3]), max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
# NUM_CORES = 2 # Choose how many cores to use.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement, ))
# inter_op_parallelism_threads=NUM_CORES,
# intra_op_parallelism_threads=NUM_CORES))
sess.run(init)
# Write all terminal output results here
val_f = open("tmp/val.txt", "ab")
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr,
graph_def=sess.graph_def)
# ckpt = tf.train.get_checkpoint_state(checkpoint_dir=FLAGS.checkpoint_dir)
# print ckpt.model_checkpoint_path
# if ckpt and ckpt.model_checkpoint_path:
# saver.restore(sess, ckpt.model_checkpoint_path)
# print('Restored!')
for i in range(10):
images_val_r, labels_val_r = sess.run([val_images, val_labels])
val_feed = {images_placeholder: images_val_r,
labels_placeholder: labels_val_r}
tf.scalar_summary('Acc', acc)
print('Calculating Acc: ')
acc_r = sess.run(acc, feed_dict=val_feed)
print(acc_r)
coord.join(threads)
sess.close()
def train(re_train=True):
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
# Get images and labels for CIFAR-10.
# images, labels = my_input.inputs()
images, labels = imageflow.distorted_inputs(
filename='../my_data_raw/train.tfrecords',
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
num_threads=5,
imshape=[32, 32, 3],
imsize=32)
val_images, val_labels = imageflow.inputs(
filename='../my_data_raw/validation.tfrecords',
batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs,
num_threads=5,
imshape=[32, 32, 3])
print(images.get_shape(), val_images.get_shape())
# Build a Graph that computes the logits predictions from the inference model.
logits = my_cifar.inference(images_placeholder)
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy #
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary('Acc', acc)
# tf.scalar_summary('Val Acc', acc_val)
tf.scalar_summary('Loss', loss)
tf.image_summary('Images',
tf.reshape(images, shape=[-1, 32, 32, 3]),
max_images=10)
tf.image_summary('Val Images',
tf.reshape(val_images, shape=[-1, 32, 32, 3]),
max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
# NUM_CORES = 2 # Choose how many cores to use.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement, ))
# inter_op_parallelism_threads=NUM_CORES,
# intra_op_parallelism_threads=NUM_CORES))
sess.run(init)
# Write all terminal output results here
val_f = open("tmp/val.txt", "ab")
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr,
graph_def=sess.graph_def)
if re_train:
# Export graph to import it later in c++
# tf.train.write_graph(sess.graph_def, FLAGS.model_dir, 'train.pbtxt') # TODO: uncomment to get graph and use in c++
continue_from_pre = False
if continue_from_pre:
ckpt = tf.train.get_checkpoint_state(
checkpoint_dir=FLAGS.checkpoint_dir)
print ckpt.model_checkpoint_path
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Session Restored!')
try:
while not coord.should_stop():
for step in xrange(FLAGS.max_steps):
images_r, labels_r = sess.run([images, labels])
images_val_r, labels_val_r = sess.run(
[val_images, val_labels])
train_feed = {
images_placeholder: images_r,
labels_placeholder: labels_r
}
val_feed = {
images_placeholder: images_val_r,
labels_placeholder: labels_val_r
}
start_time = time.time()
_, loss_value = sess.run([train_op, loss],
feed_dict=train_feed)
duration = time.time() - start_time
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
if step % display_step == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.6f (%.1f examples/sec; %.3f '
'sec/batch)')
print_str_loss = format_str % (
datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch)
print(print_str_loss)
val_f.write(print_str_loss + NEW_LINE)
summary_str = sess.run([summary_op],
feed_dict=train_feed)
summary_writer.add_summary(summary_str[0], step)
if step % val_step == 0:
acc_value, num_corroect = sess.run(
[acc, n_correct], feed_dict=train_feed)
format_str = '%s: step %d, train acc = %.2f, n_correct= %d'
print_str_train = format_str % (
datetime.now(), step, acc_value, num_corroect)
val_f.write(print_str_train + NEW_LINE)
print(print_str_train)
# Save the model checkpoint periodically.
if step % save_step == 0 or (step +
1) == FLAGS.max_steps:
val_acc_r, val_n_correct_r = sess.run(
[acc, n_correct], feed_dict=val_feed)
frmt_str = ' step %d, Val Acc = %.2f, num correct = %d'
print_str_val = frmt_str % (step, val_acc_r,
val_n_correct_r)
val_f.write(print_str_val + NEW_LINE)
print(print_str_val)
checkpoint_path = os.path.join(
FLAGS.checkpoint_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
val_f.write(
NEW_LINE + NEW_LINE +
'############################ FINISHED ############################'
+ NEW_LINE)
val_f.close()
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
else:
ckpt = tf.train.get_checkpoint_state(
checkpoint_dir=FLAGS.checkpoint_dir)
print ckpt.model_checkpoint_path
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restored!')
for i in range(100):
images_val_r, labels_val_r = sess.run([val_images, val_labels])
val_feed = {
images_placeholder: images_val_r,
labels_placeholder: labels_val_r
}
tf.scalar_summary('Acc', acc)
print('Calculating Acc: ')
acc_r = sess.run(acc, feed_dict=val_feed)
print(acc_r)
coord.join(threads)
sess.close()
def train(re_train=True):
"""Train CIFAR-10 for a number of steps."""
images, labels = get_images_and_labels()
# print "found " + str(len(images)) + " images"
train_size = 5
train_images = images[:train_size, :, :, :]
train_labels = labels[:train_size]
val_images = images[train_size:, :, :, :]
val_labels = labels[train_size:]
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
print(images.get_shape(), val_images.get_shape())
logits = my_cifar.inference(images_placeholder)
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy #
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary('Acc', acc)
# tf.scalar_summary('Val Acc', acc_val)
tf.scalar_summary('Loss', loss)
tf.image_summary('Images',
tf.reshape(images, shape=[-1, 40, 40, 3]),
max_images=10)
tf.image_summary('Val Images',
tf.reshape(val_images, shape=[-1, 40, 40, 3]),
max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
# NUM_CORES = 2 # Choose how many cores to use.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement, ))
# inter_op_parallelism_threads=NUM_CORES,
# intra_op_parallelism_threads=NUM_CORES))
sess.run(init)
# Write all terminal output results here
val_f = open("tmp/val.txt", "ab")
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr,
graph_def=sess.graph_def)
# ckpt = tf.train.get_checkpoint_state(checkpoint_dir=FLAGS.checkpoint_dir)
# print ckpt.model_checkpoint_path
# if ckpt and ckpt.model_checkpoint_path:
# saver.restore(sess, ckpt.model_checkpoint_path)
# print('Restored!')
for i in range(10):
images_val_r, labels_val_r = sess.run([val_images, val_labels])
val_feed = {
images_placeholder: images_val_r,
labels_placeholder: labels_val_r
}
tf.scalar_summary('Acc', acc)
print('Calculating Acc: ')
acc_r = sess.run(acc, feed_dict=val_feed)
print(acc_r)
coord.join(threads)
sess.close()
def train(re_train=True):
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size)
# Get images and labels for CIFAR-10.
# images, labels = my_input.inputs()
images, labels = input_data.distorted_inputs()
val_images, val_labels = input_data.inputs(False)
# Build a Graph that computes the logits predictions from the inference model.
logits = my_cifar.inference(images_placeholder)
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy #
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary('Acc', acc)
# tf.scalar_summary('Val Acc', acc_val)
tf.scalar_summary('Loss', loss)
tf.image_summary('Images', tf.reshape(images, shape=[-1, 32, 32, 3]), max_images=10)
tf.image_summary('Val Images', tf.reshape(val_images, shape=[-1, 32, 32, 3]), max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
# NUM_CORES = 2 # Choose how many cores to use.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement, ))
# inter_op_parallelism_threads=NUM_CORES,
# intra_op_parallelism_threads=NUM_CORES))
sess.run(init)
# Write all terminal output results here
val_f = open("tmp/val.txt", "ab")
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr,
graph_def=sess.graph_def)
if re_train:
# Export graph to import it later in c++
# tf.train.write_graph(sess.graph_def, FLAGS.model_dir, 'train.pbtxt') # TODO: uncomment to get graph and use in c++
continue_from_pre = False
if continue_from_pre:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=FLAGS.checkpoint_dir)
print ckpt.model_checkpoint_path
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Session Restored!')
try:
while not coord.should_stop():
for step in xrange(FLAGS.max_steps):
images_r, labels_r = sess.run([images, labels])
images_val_r, labels_val_r = sess.run([val_images, val_labels])
train_feed = {images_placeholder: images_r,
labels_placeholder: labels_r}
val_feed = {images_placeholder: images_val_r,
labels_placeholder: labels_val_r}
start_time = time.time()
_, loss_value = sess.run([train_op, loss], feed_dict=train_feed)
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % display_step == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.6f (%.1f examples/sec; %.3f '
'sec/batch)')
print_str_loss = format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch)
print (print_str_loss)
val_f.write(print_str_loss + NEW_LINE)
summary_str = sess.run([summary_op], feed_dict=train_feed)
summary_writer.add_summary(summary_str[0], step)
if step % val_step == 0:
acc_value, num_corroect = sess.run([acc, n_correct], feed_dict=train_feed)
format_str = '%s: step %d, train acc = %.2f, n_correct= %d'
print_str_train = format_str % (datetime.now(), step, acc_value, num_corroect)
val_f.write(print_str_train + NEW_LINE)
print (print_str_train)
# Save the model checkpoint periodically.
if step % save_step == 0 or (step + 1) == FLAGS.max_steps:
val_acc_r, val_n_correct_r = sess.run([acc, n_correct], feed_dict=val_feed)
frmt_str = ' step %d, Val Acc = %.2f, num correct = %d'
print_str_val = frmt_str % (step, val_acc_r, val_n_correct_r)
val_f.write(print_str_val + NEW_LINE)
print(print_str_val)
checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
except tf.errors.OutOfRangeError:
print ('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
val_f.write(NEW_LINE +
NEW_LINE +
'############################ FINISHED ############################' +
NEW_LINE)
val_f.close()
coord.request_stop()
# Wait for threads to finish.
coord.join(threads)
sess.close()
else:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=FLAGS.checkpoint_dir)
print ckpt.model_checkpoint_path
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restored!')
for i in range(100):
images_val_r, labels_val_r = sess.run([val_images, val_labels])
val_feed = {images_placeholder: images_val_r,
labels_placeholder: labels_val_r}
tf.scalar_summary('Acc', acc)
print('Calculating Acc: ')
acc_r = sess.run(acc, feed_dict=val_feed)
print(acc_r)
coord.join(threads)
sess.close()
def train(TRAIN=True):
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(
FLAGS.batch_size)
# Get images and labels for CIFAR-10.
images, labels = my_input.distorted_inputs()
val_images, val_labels = my_input.inputs(False)
print('2- images shape is ', images.get_shape())
print('3- labels shape is ', labels.get_shape())
# Build a Graph that computes the logits predictions from the
# inference model.
logits = my_cifar.inference(images_placeholder)
print('4- logits shape is ', logits.get_shape())
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy ##
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary('Acc', acc)
tf.scalar_summary('Loss', loss)
tf.image_summary('Images',
tf.reshape(images, shape=[-1, 32, 32, 3]),
max_images=10)
tf.image_summary('Val Images',
tf.reshape(val_images, shape=[-1, 32, 32, 3]),
max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr,
graph_def=sess.graph_def)
if TRAIN:
try:
while not coord.should_stop():
for step in xrange(FLAGS.max_steps):
images_r, labels_r = sess.run([images, labels])
images_val_r, labels_val_r = sess.run(
[val_images, val_labels])
train_feed = {
images_placeholder: images_r,
labels_placeholder: labels_r
}
val_feed = {
images_placeholder: images_val_r,
labels_placeholder: labels_val_r
}
start_time = time.time()
_, loss_value = sess.run([train_op, loss],
feed_dict=train_feed)
duration = time.time() - start_time
assert not np.isnan(
loss_value), 'Model diverged with loss = NaN'
if step % display_step == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = (
'%s: step %d, loss = %.6f (%.1f examples/sec; %.3f '
'sec/batch)')
print_str_loss = format_str % (
datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch)
print(print_str_loss)
summary_str = sess.run([summary_op],
feed_dict=train_feed)
summary_writer.add_summary(summary_str[0], step)
if step % (display_step * 5) == 0:
acc_value, num_corroect = sess.run(
[acc, n_correct], feed_dict=train_feed)
format_str = '%s: step %d, train acc = %.2f, n_correct= %d'
print_str_train = format_str % (
datetime.now(), step, acc_value, num_corroect)
print(print_str_train)
# Save the model checkpoint periodically.
if (step + 1) % (display_step * 10) == 0 or (
step + 1) == FLAGS.max_steps:
val_acc_r, val_n_correct_r = sess.run(
[acc, n_correct],
feed_dict=val_feed) # , feed_dict=val_feed
frmt_str = 'Step %d, Val Acc = %.2f, num correct = %d'
print_str_val = frmt_str % (step, val_acc_r,
val_n_correct_r)
print(print_str_val)
# checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'model.ckpt')
# saver.save(sess, checkpoint_path, global_step=step, latest_filename=checkpoint_state_name)
checkpoint_prefix = os.path.join(
FLAGS.checkpoint_dir, "saved_checkpoint")
saver.save(sess,
checkpoint_prefix,
global_step=0,
latest_filename=checkpoint_state_name)
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
'''
TODO #3.1: Start freezing the graph when training finished
'''
freeze_my_graph(sess)
# Wait for threads to finish.
coord.join(threads)
sess.close()
# If you define TRAIN argument to False, so it will load from the checkpoint file and freeze.
else:
'''
TODO #3.2: You can also freeze the graph from the latest checkpoint if you don't want to wait for a long time.
'''
freeze_my_graph(sess)
def train(TRAIN=True):
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images_placeholder, labels_placeholder = placeholder_inputs(FLAGS.batch_size)
# Get images and labels for CIFAR-10.
images, labels = my_input.distorted_inputs()
val_images, val_labels = my_input.inputs(False)
print("2- images shape is ", images.get_shape())
print("3- labels shape is ", labels.get_shape())
# Build a Graph that computes the logits predictions from the
# inference model.
logits = my_cifar.inference(images_placeholder)
print("4- logits shape is ", logits.get_shape())
# Calculate loss.
loss = my_cifar.loss(logits, labels_placeholder)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = my_cifar.training(loss, global_step)
# Calculate accuracy ##
acc, n_correct = my_cifar.evaluation(logits, labels_placeholder)
# Create a saver.
saver = tf.train.Saver()
tf.scalar_summary("Acc", acc)
tf.scalar_summary("Loss", loss)
tf.image_summary("Images", tf.reshape(images, shape=[-1, 32, 32, 3]), max_images=10)
tf.image_summary("Val Images", tf.reshape(val_images, shape=[-1, 32, 32, 3]), max_images=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start the queue runners.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dirr, graph_def=sess.graph_def)
if TRAIN:
try:
while not coord.should_stop():
for step in xrange(FLAGS.max_steps):
images_r, labels_r = sess.run([images, labels])
images_val_r, labels_val_r = sess.run([val_images, val_labels])
train_feed = {images_placeholder: images_r, labels_placeholder: labels_r}
val_feed = {images_placeholder: images_val_r, labels_placeholder: labels_val_r}
start_time = time.time()
_, loss_value = sess.run([train_op, loss], feed_dict=train_feed)
duration = time.time() - start_time
assert not np.isnan(loss_value), "Model diverged with loss = NaN"
if step % display_step == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = "%s: step %d, loss = %.6f (%.1f examples/sec; %.3f " "sec/batch)"
print_str_loss = format_str % (
datetime.now(),
step,
loss_value,
examples_per_sec,
sec_per_batch,
)
print(print_str_loss)
summary_str = sess.run([summary_op], feed_dict=train_feed)
summary_writer.add_summary(summary_str[0], step)
if step % (display_step * 5) == 0:
acc_value, num_corroect = sess.run([acc, n_correct], feed_dict=train_feed)
format_str = "%s: step %d, train acc = %.2f, n_correct= %d"
print_str_train = format_str % (datetime.now(), step, acc_value, num_corroect)
print(print_str_train)
# Save the model checkpoint periodically.
if (step + 1) % (display_step * 10) == 0 or (step + 1) == FLAGS.max_steps:
val_acc_r, val_n_correct_r = sess.run(
[acc, n_correct], feed_dict=val_feed
) # , feed_dict=val_feed
frmt_str = "Step %d, Val Acc = %.2f, num correct = %d"
print_str_val = frmt_str % (step, val_acc_r, val_n_correct_r)
print(print_str_val)
# checkpoint_path = os.path.join(FLAGS.checkpoint_dir, 'model.ckpt')
# saver.save(sess, checkpoint_path, global_step=step, latest_filename=checkpoint_state_name)
checkpoint_prefix = os.path.join(FLAGS.checkpoint_dir, "saved_checkpoint")
saver.save(sess, checkpoint_prefix, global_step=0, latest_filename=checkpoint_state_name)
except tf.errors.OutOfRangeError:
print("Done training -- epoch limit reached")
finally:
# When done, ask the threads to stop.
coord.request_stop()
"""
TODO #3.1: Start freezing the graph when training finished
"""
freeze_my_graph(sess)
# Wait for threads to finish.
coord.join(threads)
sess.close()
# If you define TRAIN argument to False, so it will load from the checkpoint file and freeze.
else:
"""
TODO #3.2: You can also freeze the graph from the latest checkpoint if you don't want to wait for a long time.
"""
freeze_my_graph(sess)