Поддержка пользователей

# 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.

# ==============================================================================

"""Builds the CIFAR-10 network.

Summary of available functions:

 # Compute input images and labels for training. If you would like to run

 # evaluations, use inputs() instead.

 inputs, labels = distorted_inputs()

 # Compute inference on the model inputs to make a prediction.

 predictions = inference(inputs)

 # Compute the total loss of the prediction with respect to the labels.

 loss = loss(predictions, labels)

 # Create a graph to run one step of training with respect to the loss.

 train_op = train(loss, global_step)

"""

# pylint: disable=missing-docstring

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import os

import re

import sys

import tarfile

from six.moves import urllib

import tensorflow as tf

import cifar10_input

FLAGS = tf.app.flags.FLAGS

# Basic model parameters.

username = str(os.environ['USER'])

tf.app.flags.DEFINE_integer('batch_size', 128,

                            """Number of images to process in a batch.""")

tf.app.flags.DEFINE_string('data_dir', '/tmp/'+username+'/cifar10_data',

                           """Path to the CIFAR-10 data directory.""")

tf.app.flags.DEFINE_boolean('use_fp16', False,

                            """Train the model using fp16.""")

# Global constants describing the CIFAR-10 data set.

IMAGE_SIZE = cifar10_input.IMAGE_SIZE

NUM_CLASSES = cifar10_input.NUM_CLASSES

NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN

NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

# Constants describing the training process.

MOVING_AVERAGE_DECAY = 0.9999     # The decay to use for the moving average.

NUM_EPOCHS_PER_DECAY = 350.0      # Epochs after which learning rate decays.

LEARNING_RATE_DECAY_FACTOR = 0.1  # Learning rate decay factor.

INITIAL_LEARNING_RATE = 0.1       # Initial learning rate.

# If a model is trained with multiple GPUs, prefix all Op names with tower_name

# to differentiate the operations. Note that this prefix is removed from the

# names of the summaries when visualizing a model.

TOWER_NAME = 'tower'

DATA_URL = 'http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz'

def _activation_summary(x):

  """Helper to create summaries for activations.

  Creates a summary that provides a histogram of activations.

  Creates a summary that measures the sparsity of activations.

  Args:

    x: Tensor

  Returns:

    nothing

  """

  # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training

  # session. This helps the clarity of presentation on tensorboard.

  tensor_name = re.sub('%s_[0-9]*/' % TOWER_NAME, '', x.op.name)

  tf.summary.histogram(tensor_name + '/activations', x)

  tf.summary.scalar(tensor_name + '/sparsity',

                                       tf.nn.zero_fraction(x))

def _variable_on_cpu(name, shape, initializer):

  """Helper to create a Variable stored on CPU memory.

  Args:

    name: name of the variable

    shape: list of ints

    initializer: initializer for Variable

  Returns:

    Variable Tensor

  """

  with tf.device('/cpu:0'):

    dtype = tf.float16 if FLAGS.use_fp16 else tf.float32

    var = tf.get_variable(name, shape, initializer=initializer, dtype=dtype)

  return var

def _variable_with_weight_decay(name, shape, stddev, wd):

  """Helper to create an initialized Variable with weight decay.

  Note that the Variable is initialized with a truncated normal distribution.

  A weight decay is added only if one is specified.

  Args:

    name: name of the variable

    shape: list of ints

    stddev: standard deviation of a truncated Gaussian

    wd: add L2Loss weight decay multiplied by this float. If None, weight

        decay is not added for this Variable.

  Returns:

    Variable Tensor

  """

  dtype = tf.float16 if FLAGS.use_fp16 else tf.float32

  var = _variable_on_cpu(

      name,

      shape,

      tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))

  if wd is not None:

    weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')

    tf.add_to_collection('losses', weight_decay)

  return var

def distorted_inputs():

  """Construct distorted input for CIFAR training using the Reader ops.

  Returns:

    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.

    labels: Labels. 1D tensor of [batch_size] size.

  Raises:

    ValueError: If no data_dir

  """

  if not FLAGS.data_dir:

    raise ValueError('Please supply a data_dir')

  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')

  images, labels = cifar10_input.distorted_inputs(data_dir=data_dir,

                                                  batch_size=FLAGS.batch_size)

  if FLAGS.use_fp16:

    images = tf.cast(images, tf.float16)

    labels = tf.cast(labels, tf.float16)

  return images, labels

def inputs(eval_data):

  """Construct input for CIFAR evaluation using the Reader ops.

  Args:

    eval_data: bool, indicating if one should use the train or eval data set.

  Returns:

    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.

    labels: Labels. 1D tensor of [batch_size] size.

  Raises:

    ValueError: If no data_dir

  """

  if not FLAGS.data_dir:

    raise ValueError('Please supply a data_dir')

  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')

  images, labels = cifar10_input.inputs(eval_data=eval_data,

                                        data_dir=data_dir,

                                        batch_size=FLAGS.batch_size)

  if FLAGS.use_fp16:

    images = tf.cast(images, tf.float16)

    labels = tf.cast(labels, tf.float16)

  return images, labels

def inference(images):

  """Build the CIFAR-10 model.

  Args:

    images: Images returned from distorted_inputs() or inputs().

  Returns:

    Logits.

  """

  # We instantiate all variables using tf.get_variable() instead of

  # tf.Variable() in order to share variables across multiple GPU training runs.

  # If we only ran this model on a single GPU, we could simplify this function

  # by replacing all instances of tf.get_variable() with tf.Variable().

  #

  # conv1

  with tf.variable_scope('conv1') as scope:

    kernel = _variable_with_weight_decay('weights',

                                         shape=[5, 5, 3, 64],

                                         stddev=5e-2,

                                         wd=0.0)

    conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')

    biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))

    pre_activation = tf.nn.bias_add(conv, biases)

    conv1 = tf.nn.relu(pre_activation, name=scope.name)

    _activation_summary(conv1)

  # pool1

  pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],

                         padding='SAME', name='pool1')

  # norm1

  norm1 = tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,

                    name='norm1')

  # conv2

  with tf.variable_scope('conv2') as scope:

    kernel = _variable_with_weight_decay('weights',

                                         shape=[5, 5, 64, 64],

                                         stddev=5e-2,

                                         wd=0.0)

    conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')

    biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.1))

    pre_activation = tf.nn.bias_add(conv, biases)

    conv2 = tf.nn.relu(pre_activation, name=scope.name)

    _activation_summary(conv2)

  # norm2

  norm2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75,

                    name='norm2')

  # pool2

  pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1],

                         strides=[1, 2, 2, 1], padding='SAME', name='pool2')

  # local3

  with tf.variable_scope('local3') as scope:

    # Move everything into depth so we can perform a single matrix multiply.

    reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])

    dim = reshape.get_shape()[1].value

    weights = _variable_with_weight_decay('weights', shape=[dim, 384],

                                          stddev=0.04, wd=0.004)

    biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))

    local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

    _activation_summary(local3)

  # local4

  with tf.variable_scope('local4') as scope:

    weights = _variable_with_weight_decay('weights', shape=[384, 192],

                                          stddev=0.04, wd=0.004)

    biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))

    local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)

    _activation_summary(local4)

  # linear layer(WX + b),

  # We don't apply softmax here because

  # tf.nn.sparse_softmax_cross_entropy_with_logits accepts the unscaled logits

  # and performs the softmax internally for efficiency.

  with tf.variable_scope('softmax_linear') as scope:

    weights = _variable_with_weight_decay('weights', [192, NUM_CLASSES],

                                          stddev=1/192.0, wd=0.0)

    biases = _variable_on_cpu('biases', [NUM_CLASSES],

                              tf.constant_initializer(0.0))

    softmax_linear = tf.add(tf.matmul(local4, weights), biases, name=scope.name)

    _activation_summary(softmax_linear)

  return softmax_linear

def loss(logits, labels):

  """Add L2Loss to all the trainable variables.

  Add summary for "Loss" and "Loss/avg".

  Args:

    logits: Logits from inference().

    labels: Labels from distorted_inputs or inputs(). 1-D tensor

            of shape [batch_size]

  Returns:

    Loss tensor of type float.

  """

  # Calculate the average cross entropy loss across the batch.

  labels = tf.cast(labels, tf.int64)

  cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(

      labels=labels, logits=logits, name='cross_entropy_per_example')

  cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')

  tf.add_to_collection('losses', cross_entropy_mean)

  # The total loss is defined as the cross entropy loss plus all of the weight

  # decay terms (L2 loss).

  return tf.add_n(tf.get_collection('losses'), name='total_loss')

def _add_loss_summaries(total_loss):

  """Add summaries for losses in CIFAR-10 model.

  Generates moving average for all losses and associated summaries for

  visualizing the performance of the network.

  Args:

    total_loss: Total loss from loss().

  Returns:

    loss_averages_op: op for generating moving averages of losses.

  """

  # Compute the moving average of all individual losses and the total loss.

  loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')

  losses = tf.get_collection('losses')

  loss_averages_op = loss_averages.apply(losses + [total_loss])

  # Attach a scalar summary to all individual losses and the total loss; do the

  # same for the averaged version of the losses.

  for l in losses + [total_loss]:

    # Name each loss as '(raw)' and name the moving average version of the loss

    # as the original loss name.

    tf.summary.scalar(l.op.name + ' (raw)', l)

    tf.summary.scalar(l.op.name, loss_averages.average(l))

  return loss_averages_op

def train(total_loss, global_step):

  """Train CIFAR-10 model.

  Create an optimizer and apply to all trainable variables. Add moving

  average for all trainable variables.

  Args:

    total_loss: Total loss from loss().

    global_step: Integer Variable counting the number of training steps

      processed.

  Returns:

    train_op: op for training.

  """

  # Variables that affect learning rate.

  num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS.batch_size

  decay_steps = int(num_batches_per_epoch * NUM_EPOCHS_PER_DECAY)

  # Decay the learning rate exponentially based on the number of steps.

  lr = tf.train.exponential_decay(INITIAL_LEARNING_RATE,

                                  global_step,

                                  decay_steps,

                                  LEARNING_RATE_DECAY_FACTOR,

                                  staircase=True)

  tf.summary.scalar('learning_rate', lr)

  # Generate moving averages of all losses and associated summaries.

  loss_averages_op = _add_loss_summaries(total_loss)

  # Compute gradients.

  with tf.control_dependencies([loss_averages_op]):

    opt = tf.train.GradientDescentOptimizer(lr)

    grads = opt.compute_gradients(total_loss)

  # Apply gradients.

  apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)

  # Add histograms for trainable variables.

  for var in tf.trainable_variables():

    tf.summary.histogram(var.op.name, var)

  # Add histograms for gradients.

  for grad, var in grads:

    if grad is not None:

      tf.summary.histogram(var.op.name + '/gradients', grad)

  # Track the moving averages of all trainable variables.

  variable_averages = tf.train.ExponentialMovingAverage(

      MOVING_AVERAGE_DECAY, global_step)

  variables_averages_op = variable_averages.apply(tf.trainable_variables())

  with tf.control_dependencies([apply_gradient_op, variables_averages_op]):

    train_op = tf.no_op(name='train')

  return train_op

def maybe_download_and_extract():

  """Download and extract the tarball from Alex's website."""

  dest_directory = FLAGS.data_dir

  if not os.path.exists(dest_directory):

    os.makedirs(dest_directory)

  filename = DATA_URL.split('/')[-1]

  filepath = os.path.join(dest_directory, filename)

  if not os.path.exists(filepath):

    print('Download strarted')

    def _progress(count, block_size, total_size):

      sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,

          float(count * block_size) / float(total_size) * 100.0))

      sys.stdout.flush()

    #filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress)

    filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath)

    statinfo = os.stat(filepath)

    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')

  extracted_dir_path = os.path.join(dest_directory, 'cifar-10-batches-bin')

  if not os.path.exists(extracted_dir_path):

    tarfile.open(filepath, 'r:gz').extractall(dest_directory)