simple script to log timeline for a mint example

mnist_timeline.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 tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.python.client import timeline
from protobuf_to_dict import protobuf_to_dict
from google.protobuf.json_format import MessageToJson
import json

FLAGS = None

tensors_shape_arr = []


def test_device_placer(op):

  if 'layer1' in op.name:
    return '/gpu:0'
  elif 'layer2' in op.name:
    return '/gpu:1'
  return '/cpu:0' 
  # return op.device


def train():
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir,
                                    fake_data=FLAGS.fake_data)
  config_proto = tf.ConfigProto(allow_soft_placement=True, graph_options=tf.GraphOptions(build_cost_model=5))

# config_proto = tf.ConfigProto(
#       log_device_placement=log_device_placement,
#       #allow_soft_placement=allow_soft_placement)
#       allow_soft_placement=allow_soft_placement, graph_options=tf.GraphOptions(build_cost_model=5))
  sess = tf.InteractiveSession(config=config_proto)
  # 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])

  # 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 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])
      with tf.name_scope('biases'):
        biases = bias_variable([output_dim])
      with tf.name_scope('Wx_plus_b'):
        preactivate = tf.matmul(input_tensor, weights) + biases
      activations = act(preactivate, name='activation')
      return activations

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

  with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    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.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)

  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.global_variables_initializer().run()




  
  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: 
      acc = sess.run([accuracy], feed_dict=feed_dict(False))
      print('Accuracy at step %s: %s' % (i, acc))
    else:  
      if i % 100 == 99:  # Record execution stats
        run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
        run_metadata = tf.RunMetadata()
        sess.run([train_step], feed_dict=feed_dict(True), options=run_options, run_metadata=run_metadata)
        print('Adding run metadata for', i)


        jsonObj = MessageToJson(run_metadata.step_stats)
        with open('step_stats_%d.json' % (i), 'w') as outfile:
          json.dump(jsonObj, outfile)

        trace = timeline.Timeline(step_stats=run_metadata.step_stats)
        trace_file = open('timeline_%d.json' % (i), 'w')
        trace_file.write(trace.generate_chrome_trace_format())
        trace_file.close()
      else:
        sess.run([train_step], feed_dict=feed_dict(True))


def main(_):
  if tf.gfile.Exists(FLAGS.log_dir):
    tf.gfile.DeleteRecursively(FLAGS.log_dir)
  tf.gfile.MakeDirs(FLAGS.log_dir)
  #with tf.device(test_device_placer):
  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=100,
                      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)