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Added new features #14

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130 changes: 80 additions & 50 deletions retrain.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,4 @@
import shutil
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
Expand Down Expand Up @@ -866,56 +867,59 @@ def main(_):
sess.run(init)

# Run the training for as many cycles as requested on the command line.
for i in range(FLAGS.how_many_training_steps):
# Get a batch of input bottleneck values, either calculated fresh every
# time with distortions applied, or from the cache stored on disk.
if do_distort_images:
(train_bottlenecks,
train_ground_truth) = get_random_distorted_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.image_dir, distorted_jpeg_data_tensor,
distorted_image_tensor, resized_image_tensor, bottleneck_tensor)
else:
(train_bottlenecks,
train_ground_truth, _) = get_random_cached_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
bottleneck_tensor)
# Feed the bottlenecks and ground truth into the graph, and run a training
# step. Capture training summaries for TensorBoard with the `merged` op.

train_summary, _ = sess.run(
[merged, train_step],
feed_dict={bottleneck_input: train_bottlenecks,
ground_truth_input: train_ground_truth})
train_writer.add_summary(train_summary, i)

# Every so often, print out how well the graph is training.
is_last_step = (i + 1 == FLAGS.how_many_training_steps)
if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
train_accuracy, cross_entropy_value = sess.run(
[evaluation_step, cross_entropy],
try:
for i in range(FLAGS.how_many_training_steps):
# Get a batch of input bottleneck values, either calculated fresh every
# time with distortions applied, or from the cache stored on disk.
if do_distort_images:
(train_bottlenecks,
train_ground_truth) = get_random_distorted_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.image_dir, distorted_jpeg_data_tensor,
distorted_image_tensor, resized_image_tensor, bottleneck_tensor)
else:
(train_bottlenecks,
train_ground_truth, _) = get_random_cached_bottlenecks(
sess, image_lists, FLAGS.train_batch_size, 'training',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
bottleneck_tensor)
# Feed the bottlenecks and ground truth into the graph, and run a training
# step. Capture training summaries for TensorBoard with the `merged` op.

train_summary, _ = sess.run(
[merged, train_step],
feed_dict={bottleneck_input: train_bottlenecks,
ground_truth_input: train_ground_truth})
print('%s: Step %d: Train accuracy = %.1f%%' % (datetime.now(), i,
train_accuracy * 100))
print('%s: Step %d: Cross entropy = %f' % (datetime.now(), i,
cross_entropy_value))
validation_bottlenecks, validation_ground_truth, _ = (
get_random_cached_bottlenecks(
sess, image_lists, FLAGS.validation_batch_size, 'validation',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
bottleneck_tensor))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
validation_summary, validation_accuracy = sess.run(
[merged, evaluation_step],
feed_dict={bottleneck_input: validation_bottlenecks,
ground_truth_input: validation_ground_truth})
validation_writer.add_summary(validation_summary, i)
print('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
(datetime.now(), i, validation_accuracy * 100,
len(validation_bottlenecks)))
train_writer.add_summary(train_summary, i)

# Every so often, print out how well the graph is training.
is_last_step = (i + 1 == FLAGS.how_many_training_steps)
if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
train_accuracy, cross_entropy_value = sess.run(
[evaluation_step, cross_entropy],
feed_dict={bottleneck_input: train_bottlenecks,
ground_truth_input: train_ground_truth})
print('%s: Step %d: Train accuracy = %.1f%%' % (datetime.now(), i,
train_accuracy * 100))
print('%s: Step %d: Cross entropy = %f' % (datetime.now(), i,
cross_entropy_value))
validation_bottlenecks, validation_ground_truth, _ = (
get_random_cached_bottlenecks(
sess, image_lists, FLAGS.validation_batch_size, 'validation',
FLAGS.bottleneck_dir, FLAGS.image_dir, jpeg_data_tensor,
bottleneck_tensor))
# Run a validation step and capture training summaries for TensorBoard
# with the `merged` op.
validation_summary, validation_accuracy = sess.run(
[merged, evaluation_step],
feed_dict={bottleneck_input: validation_bottlenecks,
ground_truth_input: validation_ground_truth})
validation_writer.add_summary(validation_summary, i)
print('%s: Step %d: Validation accuracy = %.1f%% (N=%d)' %
(datetime.now(), i, validation_accuracy * 100,
len(validation_bottlenecks)))
except KeyboardInterrupt:
print('CTRL+C caught, saving...')

# We've completed all our training, so run a final test evaluation on
# some new images we haven't used before.
Expand All @@ -937,7 +941,21 @@ def main(_):
if predictions[i] != test_ground_truth[i].argmax():
print('%70s %s' % (test_filename,
list(image_lists.keys())[predictions[i]]))


# Add the checkpoint saving function
saver = tf.train.Saver()
save_path = saver.save(sess, FLAGS.output_checkpoint)
print("Checkpoint saved to %s" % save_path)

# Add the SavedModel file saving function
if os.path.exists(FLAGS.output_savedmodel) and os.path.isdir(FLAGS.output_savedmodel):
shutil.rmtree(FLAGS.output_savedmodel) # SavedModel saver will throw error if dir exists
tf.saved_model.simple_save(sess,
FLAGS.output_savedmodel,
inputs={"%s" % JPEG_DATA_TENSOR_NAME: jpeg_data_tensor},
outputs={"%s" % FLAGS.final_tensor_name: final_tensor})
print("SavedModel saved to "+FLAGS.output_savedmodel)

# Write out the trained graph and labels with the weights stored as
# constants.
output_graph_def = graph_util.convert_variables_to_constants(
Expand All @@ -962,6 +980,18 @@ def main(_):
default='/tmp/output_graph.pb',
help='Where to save the trained graph.'
)
parser.add_argument(
'--output_checkpoint',
type=str,
default='/tmp/checkpoint.ckpt',
help='Where to save the checkpoint.'
)
parser.add_argument(
'--output_savedmodel',
type=str,
default='/tmp/saved_model.pb',
help='Where to save the SavedModel.'
)
parser.add_argument(
'--output_labels',
type=str,
Expand Down Expand Up @@ -1102,4 +1132,4 @@ def main(_):
"""
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)