main.py

# 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 ast
import json

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

FLAGS = None

def train():
  tf_config_json = os.environ.get("TF_CONFIG", "{}")
  tf_config = json.loads(tf_config_json)

  task = tf_config.get("task", {})
  cluster_spec = tf_config.get("cluster", {})
  cluster_spec_object = tf.train.ClusterSpec(cluster_spec)
  job_name = task["type"]
  task_id = task["index"]
  server_def = tf.train.ServerDef(
      cluster=cluster_spec_object.as_cluster_def(),
      protocol="grpc",
      job_name=job_name,
      task_index=task_id)
  server = tf.train.Server(server_def)

  is_chief = (job_name == 'master')

  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir,
                                    one_hot=True,
                                    fake_data=FLAGS.fake_data)

  
  # Create a multilayer model.


  # Between-graph replication
  with tf.device(tf.train.replica_device_setter(
    worker_device="/job:worker/task:%d" % task_id,
    cluster=cluster_spec)):

    # count the number of updates
    global_step = tf.get_variable(
      'global_step',
      [],
      initializer = tf.constant_initializer(0),
      trainable = False)

    # Input placeholders
    with tf.name_scope('input'):
      x = tf.placeholder(tf.float32, [None, 784], name='x-input')
      y_ = tf.placeholder(tf.float32, [None, 10], 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, 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.log(tf.softmax(y)),
      #                               reduction_indices=[1]))
      #
      # can be numerically unstable.
      #
      # So here we use tf.nn.softmax_cross_entropy_with_logits on the
      # raw outputs of the nn_layer above, and then average across
      # the batch.
      diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)
      with tf.name_scope('total'):
        cross_entropy = tf.reduce_mean(diff)
    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), tf.argmax(y_, 1))
      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()  

    init_op = tf.global_variables_initializer()

  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}



  sv = tf.train.Supervisor(is_chief=is_chief,
						global_step=global_step,
						init_op=init_op,
						logdir=FLAGS.logdir)

  with sv.prepare_or_wait_for_session(server.target) as sess:  
    train_writer = tf.summary.FileWriter(FLAGS.logdir + '/train', sess.graph)
    test_writer = tf.summary.FileWriter(FLAGS.logdir + '/test')
    # 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

    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(_):
  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/input_data'),
      help='Directory for storing input data')
  parser.add_argument(
      '--logdir',
      type=str,
      default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
                           'tensorflow/logs'),
      help='Summaries log directory')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)