mnist_with_summaries.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

def train(server, log_dir):
  # Import data
  data_dir = '/tmp/tensorflow/mnist/input_data'
  mnist = input_data.read_data_sets(data_dir, one_hot=True)

  # 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'):
    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'):
    global_step = tf.contrib.framework.get_or_create_global_step()
    train_step = tf.train.AdamOptimizer(0.001).minimize(
        cross_entropy, global_step=global_step)

  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)

  # Define Hooks
  stop_at_step_hook = tf.train.StopAtStepHook(last_step=30000)
  hooks=[stop_at_step_hook]

  # Merge all the summaries and write them out to
  # /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
  with tf.device("/job:worker/task:0"):
      merged = tf.summary.merge_all()
      train_writer = tf.summary.FileWriter(log_dir + '/train', tf.get_default_graph())
      test_writer = tf.summary.FileWriter(log_dir + '/test')

  # Define run ops
  run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
  run_metadata = tf.RunMetadata()

  with tf.train.MonitoredTrainingSession(master=server.target,
                                     is_chief=(server.server_def.task_index == 0),
                                     hooks=hooks) as sess:

      # 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:
          xs, ys = mnist.train.next_batch(100)
          k = 0.9
        else:
          xs, ys = mnist.test.images, mnist.test.labels
          k = 1.0
        return {x: xs, y_: ys, keep_prob: k}

      i = 0
      while not sess.should_stop():
        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
            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)
        i += 1
      train_writer.close()
      test_writer.close()

def main(server, volume, context):
  train(server, volume)