tensorflow/examples/tutorials/mnist/mnist_with_summaries.py - platform/external/tensorflow - Git at Google

 # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the 'License');
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an 'AS IS' BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
 """A simple MNIST classifier which displays summaries in TensorBoard.

 This is an unimpressive MNIST model, but it is a good example of using
 tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
 naming summary tags so that they are grouped meaningfully in TensorBoard.

 It demonstrates the functionality of every TensorBoard dashboard.
 """
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 import argparse
 import os
 import sys

 import tensorflow as tf

 from tensorflow.examples.tutorials.mnist import input_data

 FLAGS = None


 def train():
   # Import data
   mnist = input_data.read_data_sets(FLAGS.data_dir,
                                     fake_data=FLAGS.fake_data)

   sess = tf.InteractiveSession()
   # Create a multilayer model.

   # Input placeholders
   with tf.name_scope('input'):
     x = tf.placeholder(tf.float32, [None, 784], name='x-input')
     y_ = tf.placeholder(tf.int64, [None], name='y-input')

   with tf.name_scope('input_reshape'):
     image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
     tf.summary.image('input', image_shaped_input, 10)

   # We can't initialize these variables to 0 - the network will get stuck.
   def weight_variable(shape):
     """Create a weight variable with appropriate initialization."""
     initial = tf.truncated_normal(shape, stddev=0.1)
     return tf.Variable(initial)

   def bias_variable(shape):
     """Create a bias variable with appropriate initialization."""
     initial = tf.constant(0.1, shape=shape)
     return tf.Variable(initial)

   def variable_summaries(var):
     """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
     with tf.name_scope('summaries'):
       mean = tf.reduce_mean(var)
       tf.summary.scalar('mean', mean)
       with tf.name_scope('stddev'):
         stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
       tf.summary.scalar('stddev', stddev)
       tf.summary.scalar('max', tf.reduce_max(var))
       tf.summary.scalar('min', tf.reduce_min(var))
       tf.summary.histogram('histogram', var)

   def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
     """Reusable code for making a simple neural net layer.

     It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
     It also sets up name scoping so that the resultant graph is easy to read,
     and adds a number of summary ops.
     """
     # Adding a name scope ensures logical grouping of the layers in the graph.
     with tf.name_scope(layer_name):
       # This Variable will hold the state of the weights for the layer
       with tf.name_scope('weights'):
         weights = weight_variable([input_dim, output_dim])
         variable_summaries(weights)
       with tf.name_scope('biases'):
         biases = bias_variable([output_dim])
         variable_summaries(biases)
       with tf.name_scope('Wx_plus_b'):
         preactivate = tf.matmul(input_tensor, weights) + biases
         tf.summary.histogram('pre_activations', preactivate)
       activations = act(preactivate, name='activation')
       tf.summary.histogram('activations', activations)
       return activations

   hidden1 = nn_layer(x, 784, 500, 'layer1')

   with tf.name_scope('dropout'):
     keep_prob = tf.placeholder(tf.float32)
     tf.summary.scalar('dropout_keep_probability', keep_prob)
     dropped = tf.nn.dropout(hidden1, rate=(1 - keep_prob))

   # Do not apply softmax activation yet, see below.
   y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

   with tf.name_scope('cross_entropy'):
     # The raw formulation of cross-entropy,
     #
     # tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.softmax(y)),
     #                               reduction_indices=[1]))
     #
     # can be numerically unstable.
     #
     # So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the
     # raw logit outputs of the nn_layer above, and then average across
     # the batch.
     with tf.name_scope('total'):
       cross_entropy = tf.losses.sparse_softmax_cross_entropy(
           labels=y_, logits=y)
   tf.summary.scalar('cross_entropy', cross_entropy)

   with tf.name_scope('train'):
     train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
         cross_entropy)

   with tf.name_scope('accuracy'):
     with tf.name_scope('correct_prediction'):
       correct_prediction = tf.equal(tf.argmax(y, 1), y_)
     with tf.name_scope('accuracy'):
       accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   tf.summary.scalar('accuracy', accuracy)

   # Merge all the summaries and write them out to
   # /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
   merged = tf.summary.merge_all()
   train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
   test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
   tf.global_variables_initializer().run()

   # Train the model, and also write summaries.
   # Every 10th step, measure test-set accuracy, and write test summaries
   # All other steps, run train_step on training data, & add training summaries

   def feed_dict(train):
     """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
     if train or FLAGS.fake_data:
       xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
       k = FLAGS.dropout
     else:
       xs, ys = mnist.test.images, mnist.test.labels
       k = 1.0
     return {x: xs, y_: ys, keep_prob: k}

   for i in range(FLAGS.max_steps):
     if i % 10 == 0:  # Record summaries and test-set accuracy
       summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
       test_writer.add_summary(summary, i)
       print('Accuracy at step %s: %s' % (i, acc))
     else:  # Record train set summaries, and train
       if i % 100 == 99:  # Record execution stats
         run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
         run_metadata = tf.RunMetadata()
         summary, _ = sess.run([merged, train_step],
                               feed_dict=feed_dict(True),
                               options=run_options,
                               run_metadata=run_metadata)
         train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
         train_writer.add_summary(summary, i)
         print('Adding run metadata for', i)
       else:  # Record a summary
         summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
         train_writer.add_summary(summary, i)
   train_writer.close()
   test_writer.close()


 def main(_):
   if tf.gfile.Exists(FLAGS.log_dir):
     tf.gfile.DeleteRecursively(FLAGS.log_dir)
   tf.gfile.MakeDirs(FLAGS.log_dir)
   with tf.Graph().as_default():
     train()


 if __name__ == '__main__':
   parser = argparse.ArgumentParser()
   parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
                       default=False,
                       help='If true, uses fake data for unit testing.')
   parser.add_argument('--max_steps', type=int, default=1000,
                       help='Number of steps to run trainer.')
   parser.add_argument('--learning_rate', type=float, default=0.001,
                       help='Initial learning rate')
   parser.add_argument('--dropout', type=float, default=0.9,
                       help='Keep probability for training dropout.')
   parser.add_argument(
       '--data_dir',
       type=str,
       default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
                            'tensorflow/mnist/input_data'),
       help='Directory for storing input data')
   parser.add_argument(
       '--log_dir',
       type=str,
       default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
                            'tensorflow/mnist/logs/mnist_with_summaries'),
       help='Summaries log directory')
   FLAGS, unparsed = parser.parse_known_args()
   tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
	# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the 'License');
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an 'AS IS' BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	# ==============================================================================
	"""A simple MNIST classifier which displays summaries in TensorBoard.

	This is an unimpressive MNIST model, but it is a good example of using
	tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
	naming summary tags so that they are grouped meaningfully in TensorBoard.

	It demonstrates the functionality of every TensorBoard dashboard.
	"""
	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import argparse
	import os
	import sys

	import tensorflow as tf

	from tensorflow.examples.tutorials.mnist import input_data

	FLAGS = None


	def train():
	# Import data
	mnist = input_data.read_data_sets(FLAGS.data_dir,
	fake_data=FLAGS.fake_data)

	sess = tf.InteractiveSession()
	# Create a multilayer model.

	# Input placeholders
	with tf.name_scope('input'):
	x = tf.placeholder(tf.float32, [None, 784], name='x-input')
	y_ = tf.placeholder(tf.int64, [None], name='y-input')

	with tf.name_scope('input_reshape'):
	image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
	tf.summary.image('input', image_shaped_input, 10)

	# We can't initialize these variables to 0 - the network will get stuck.
	def weight_variable(shape):
	"""Create a weight variable with appropriate initialization."""
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)

	def bias_variable(shape):
	"""Create a bias variable with appropriate initialization."""
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)

	def variable_summaries(var):
	"""Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
	with tf.name_scope('summaries'):
	mean = tf.reduce_mean(var)
	tf.summary.scalar('mean', mean)
	with tf.name_scope('stddev'):
	stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
	tf.summary.scalar('stddev', stddev)
	tf.summary.scalar('max', tf.reduce_max(var))
	tf.summary.scalar('min', tf.reduce_min(var))
	tf.summary.histogram('histogram', var)

	def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
	"""Reusable code for making a simple neural net layer.

	It does a matrix multiply, bias add, and then uses ReLU to nonlinearize.
	It also sets up name scoping so that the resultant graph is easy to read,
	and adds a number of summary ops.
	"""
	# Adding a name scope ensures logical grouping of the layers in the graph.
	with tf.name_scope(layer_name):
	# This Variable will hold the state of the weights for the layer
	with tf.name_scope('weights'):
	weights = weight_variable([input_dim, output_dim])
	variable_summaries(weights)
	with tf.name_scope('biases'):
	biases = bias_variable([output_dim])
	variable_summaries(biases)
	with tf.name_scope('Wx_plus_b'):
	preactivate = tf.matmul(input_tensor, weights) + biases
	tf.summary.histogram('pre_activations', preactivate)
	activations = act(preactivate, name='activation')
	tf.summary.histogram('activations', activations)
	return activations

	hidden1 = nn_layer(x, 784, 500, 'layer1')

	with tf.name_scope('dropout'):
	keep_prob = tf.placeholder(tf.float32)
	tf.summary.scalar('dropout_keep_probability', keep_prob)
	dropped = tf.nn.dropout(hidden1, rate=(1 - keep_prob))

	# Do not apply softmax activation yet, see below.
	y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

	with tf.name_scope('cross_entropy'):
	# The raw formulation of cross-entropy,
	#
	# tf.reduce_mean(-tf.reduce_sum(y_ * tf.math.log(tf.softmax(y)),
	# reduction_indices=[1]))
	#
	# can be numerically unstable.
	#
	# So here we use tf.compat.v1.losses.sparse_softmax_cross_entropy on the
	# raw logit outputs of the nn_layer above, and then average across
	# the batch.
	with tf.name_scope('total'):
	cross_entropy = tf.losses.sparse_softmax_cross_entropy(
	labels=y_, logits=y)
	tf.summary.scalar('cross_entropy', cross_entropy)

	with tf.name_scope('train'):
	train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
	cross_entropy)

	with tf.name_scope('accuracy'):
	with tf.name_scope('correct_prediction'):
	correct_prediction = tf.equal(tf.argmax(y, 1), y_)
	with tf.name_scope('accuracy'):
	accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
	tf.summary.scalar('accuracy', accuracy)

	# Merge all the summaries and write them out to
	# /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
	merged = tf.summary.merge_all()
	train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
	test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
	tf.global_variables_initializer().run()

	# Train the model, and also write summaries.
	# Every 10th step, measure test-set accuracy, and write test summaries
	# All other steps, run train_step on training data, & add training summaries

	def feed_dict(train):
	"""Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
	if train or FLAGS.fake_data:
	xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
	k = FLAGS.dropout
	else:
	xs, ys = mnist.test.images, mnist.test.labels
	k = 1.0
	return {x: xs, y_: ys, keep_prob: k}

	for i in range(FLAGS.max_steps):
	if i % 10 == 0: # Record summaries and test-set accuracy
	summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
	test_writer.add_summary(summary, i)
	print('Accuracy at step %s: %s' % (i, acc))
	else: # Record train set summaries, and train
	if i % 100 == 99: # Record execution stats
	run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
	run_metadata = tf.RunMetadata()
	summary, _ = sess.run([merged, train_step],
	feed_dict=feed_dict(True),
	options=run_options,
	run_metadata=run_metadata)
	train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
	train_writer.add_summary(summary, i)
	print('Adding run metadata for', i)
	else: # Record a summary
	summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
	train_writer.add_summary(summary, i)
	train_writer.close()
	test_writer.close()


	def main(_):
	if tf.gfile.Exists(FLAGS.log_dir):
	tf.gfile.DeleteRecursively(FLAGS.log_dir)
	tf.gfile.MakeDirs(FLAGS.log_dir)
	with tf.Graph().as_default():
	train()


	if __name__ == '__main__':
	parser = argparse.ArgumentParser()
	parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
	default=False,
	help='If true, uses fake data for unit testing.')
	parser.add_argument('--max_steps', type=int, default=1000,
	help='Number of steps to run trainer.')
	parser.add_argument('--learning_rate', type=float, default=0.001,
	help='Initial learning rate')
	parser.add_argument('--dropout', type=float, default=0.9,
	help='Keep probability for training dropout.')
	parser.add_argument(
	'--data_dir',
	type=str,
	default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
	'tensorflow/mnist/input_data'),
	help='Directory for storing input data')
	parser.add_argument(
	'--log_dir',
	type=str,
	default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
	'tensorflow/mnist/logs/mnist_with_summaries'),
	help='Summaries log directory')
	FLAGS, unparsed = parser.parse_known_args()
	tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)