def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
data_sets = input_data.read_data_sets(tempfile.mkdtemp(), FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels and mark as input.
placeholders = placeholder_inputs()
keys_placeholder, images_placeholder, labels_placeholder = placeholders
inputs = {'key': keys_placeholder.name, 'image': images_placeholder.name}
tf.add_to_collection('inputs', json.dumps(inputs))
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# To be able to extract the id, we need to add the identity function.
keys = tf.identity(keys_placeholder)
# The prediction will be the index in logits with the highest score.
# We also use a softmax operation to produce a probability distribution
# over all possible digits.
prediction = tf.argmax(logits, 1)
scores = tf.nn.softmax(logits)
# Mark the outputs.
outputs = {'key': keys.name,
'prediction': prediction.name,
'scores': scores.name}
tf.add_to_collection('outputs', json.dumps(outputs))
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary operation based on the TF collection of Summaries.
# TODO(b/33420312): remove the if once 0.12 is fully rolled out to prod.
if tf.__version__ < '0.12':
summary_op = tf.merge_all_summaries()
else:
summary_op = tf.contrib.deprecated.merge_all_summaries()
# Add the variable initializer Op.
init = tf.initialize_all_variables()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.train_dir, 'checkpoint')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.test)
# Export the model so that it can be loaded and used later for predictions.
file_io.create_dir(FLAGS.model_dir)
saver.save(sess, os.path.join(FLAGS.model_dir, 'export'))
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST. If input_path is specified, download the data from GCS to
# the folder expected by read_data_sets.
data_dir = tempfile.mkdtemp()
if FLAGS.input_path:
files = [
os.path.join(FLAGS.input_path, file_name)
for file_name in INPUT_FILES
]
subprocess.check_call(['gsutil', '-m', '-q', 'cp', '-r'] + files +
[data_dir])
data_sets = input_data.read_data_sets(data_dir, FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels and mark as input.
placeholders = placeholder_inputs()
keys_placeholder, images_placeholder, labels_placeholder = placeholders
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder, FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# To be able to extract the id, we need to add the identity function.
keys = tf.identity(keys_placeholder)
# The prediction will be the index in logits with the highest score.
# We also use a softmax operation to produce a probability distribution
# over all possible digits.
prediction = tf.argmax(logits, 1)
scores = tf.nn.softmax(logits)
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary operation based on the TF collection of Summaries.
# Remove this if once Tensorflow 0.12 is standard.
try:
summary_op = tf.contrib.deprecated.merge_all_summaries()
except AttributeError:
summary_op = tf.merge_all_summaries()
# Add the variable initializer Op.
init = tf.initialize_all_variables()
# Create a saver for writing legacy training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
# Remove this if once Tensorflow 0.12 is standard.
try:
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
except AttributeError:
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train, images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' %
(step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.train_dir, 'checkpoint')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess, eval_correct, images_placeholder,
labels_placeholder, data_sets.test)
file_io.create_dir(FLAGS.model_dir)
# Create a saver for writing SavedModel training checkpoints.
saved_model_util.simple_save(sess,
os.path.join(FLAGS.model_dir,
'saved_model'),
inputs={
'key': keys_placeholder,
'image': images_placeholder
},
outputs={
'key': keys,
'prediction': prediction,
'scores': scores
})
logging.debug('Saved model path %s',
os.path.join(FLAGS.model_dir, 'saved_model'))
def run_training():
"""Train MNIST for a number of steps."""
# Get the sets of images and labels for training, validation, and
# test on MNIST.
data_sets = input_data.read_data_sets(tempfile.mkdtemp(), FLAGS.fake_data)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Generate placeholders for the images and labels and mark as input.
placeholders = placeholder_inputs()
keys_placeholder, images_placeholder, labels_placeholder = placeholders
inputs = {'key': keys_placeholder.name, 'image': images_placeholder.name}
tf.add_to_collection('inputs', json.dumps(inputs))
# Build a Graph that computes predictions from the inference model.
logits = mnist.inference(images_placeholder,
FLAGS.hidden1,
FLAGS.hidden2)
# Add to the Graph the Ops for loss calculation.
loss = mnist.loss(logits, labels_placeholder)
# To be able to extract the id, we need to add the identity function.
keys = tf.identity(keys_placeholder)
# The prediction will be the index in logits with the highest score.
# We also use a softmax operation to produce a probability distribution
# over all possible digits.
prediction = tf.argmax(logits, 1)
scores = tf.nn.softmax(logits)
# Mark the outputs.
outputs = {'key': keys.name,
'prediction': prediction.name,
'scores': scores.name}
tf.add_to_collection('outputs', json.dumps(outputs))
# Add to the Graph the Ops that calculate and apply gradients.
train_op = mnist.training(loss, FLAGS.learning_rate)
# Add the Op to compare the logits to the labels during evaluation.
eval_correct = mnist.evaluation(logits, labels_placeholder)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Add the variable initializer Op.
init = tf.initialize_all_variables()
# Create a saver for writing training checkpoints.
saver = tf.train.Saver()
# Create a session for running Ops on the Graph.
sess = tf.Session()
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
# And then after everything is built:
# Run the Op to initialize the variables.
sess.run(init)
# Start the training loop.
for step in xrange(FLAGS.max_steps):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(data_sets.train,
images_placeholder,
labels_placeholder)
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % 100 == 0:
# Print status to stdout.
print('Step %d: loss = %.2f (%.3f sec)' % (step, loss_value, duration))
# Update the events file.
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
# Save a checkpoint and evaluate the model periodically.
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.train_dir, 'checkpoint')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.train)
# Evaluate against the validation set.
print('Validation Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.validation)
# Evaluate against the test set.
print('Test Data Eval:')
do_eval(sess,
eval_correct,
images_placeholder,
labels_placeholder,
data_sets.test)
# Export the model so that it can be loaded and used later for predictions.
file_io.create_dir(FLAGS.model_dir)
saver.save(sess, os.path.join(FLAGS.model_dir, 'export'))