mnist_deep.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 deep MNIST classifier using convolutional layers.

See extensive documentation at
https://www.tensorflow.org/get_started/mnist/pros
"""
# Disable linter warnings to maintain consistency with tutorial.
# pylint: disable=invalid-name
# pylint: disable=g-bad-import-order

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

import argparse
import time
import os
import sys
import math

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf
from collections import namedtuple
from datetime import datetime
import tools.storeincsv as incsv

FLAGS = None

ParamHeader = ['Timestamp', 'Script', 'Info', 'Batch_size', 'Num_steps', 'TestAccuracy', 'TotalTime', 'TestTime', 'TrainTime', 'MeanPerBatch', 'StDev']
ParamEntry = namedtuple('ParamEntry', ParamHeader)

def deepnn(x):
  """deepnn builds the graph for a deep net for classifying digits.

  Args:
    x: an input tensor with the dimensions (N_examples, 784), where 784 is the
    number of pixels in a standard MNIST image.

  Returns:
    A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
    equal to the logits of classifying the digit into one of 10 classes (the
    digits 0-9). keep_prob is a scalar placeholder for the probability of
    dropout.
  """
  # Reshape to use within a convolutional neural net.
  # Last dimension is for "features" - there is only one here, since images are
  # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
  x_image = tf.reshape(x, [-1, 28, 28, 1])

  # First convolutional layer - maps one grayscale image to 32 feature maps.
  W_conv1 = weight_variable([5, 5, 1, 32])
  b_conv1 = bias_variable([32])
  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)

  # Pooling layer - downsamples by 2X.
  h_pool1 = max_pool_2x2(h_conv1)

  # Second convolutional layer -- maps 32 feature maps to 64.
  W_conv2 = weight_variable([5, 5, 32, 64])
  b_conv2 = bias_variable([64])
  h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)

  # Second pooling layer.
  h_pool2 = max_pool_2x2(h_conv2)

  # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
  # is down to 7x7x64 feature maps -- maps this to 1024 features.
  W_fc1 = weight_variable([7 * 7 * 64, 1024])
  b_fc1 = bias_variable([1024])

  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

  # Dropout - controls the complexity of the model, prevents co-adaptation of
  # features.
  keep_prob = tf.placeholder(tf.float32)
  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

  # Map the 1024 features to 10 classes, one for each digit
  W_fc2 = weight_variable([1024, 10])
  b_fc2 = bias_variable([10])

  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
  return y_conv, keep_prob


def conv2d(x, W):
  """conv2d returns a 2d convolution layer with full stride."""
  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
  """max_pool_2x2 downsamples a feature map by 2X."""
  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                        strides=[1, 2, 2, 1], padding='SAME')


def weight_variable(shape):
  """weight_variable generates a weight variable of a given shape."""
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)


def bias_variable(shape):
  """bias_variable generates a bias variable of a given shape."""
  initial = tf.constant(0.1, shape=shape)
  return tf.Variable(initial)


def main(_):
  # Import data
  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)

  # Create the model
  x = tf.placeholder(tf.float32, [None, 784])

  # Define loss and optimizer
  y_ = tf.placeholder(tf.float32, [None, 10])

  # Build the graph for the deep net
  y_conv, keep_prob = deepnn(x)

  cross_entropy = tf.reduce_mean(
      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
  train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
  correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

  param_entries = []
  param_entries.append(ParamHeader) 
  check_step = 1000
  mnist_batchsize = 50
  mnist_steps = 20000
  num_steps_burn_in = 10
    
  if FLAGS.with_profiling :
    mnist_steps = 2
    num_steps_burn_in = 0
    print("=> Profiling is enabled!")

  if FLAGS.mnist_batch > -1:
    mnist_batchsize = FLAGS.mnist_batch
  
  if FLAGS.mnist_steps > -1:
    mnist_steps = FLAGS.mnist_steps
  
  print("mnist_steps: ", mnist_steps)

  print("Ready for training, start time counting")
  # start time
  start = time.time()

  train_duration = 0.0
  train_duration_squared = 0.0
  check_duration = 0.0
 
  config = tf.ConfigProto()
  config.gpu_options.per_process_gpu_memory_fraction = FLAGS.gpu_fraction
  
  with tf.Session(config=config) as sess:
    sess.run(tf.global_variables_initializer())
    batch0 = mnist.train.next_batch(mnist_batchsize) # for burn_in we can use a fixed batch
    for i in range(mnist_steps + num_steps_burn_in):
      if (i == num_steps_burn_in):
          burn_in_end = time.time()
          tcheck_prev = burn_in_end
          
      batch = mnist.train.next_batch(mnist_batchsize) if (i >= num_steps_burn_in) else batch0

      if (i >= num_steps_burn_in) and (i - num_steps_burn_in) % check_step == 0 :
        tcheck = time.time()
        train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) 
        dtcheck = tcheck - tcheck_prev
        nbatches = check_step if (i - num_steps_burn_in) > 0 else 0
        t1batch = dtcheck/float(nbatches) if nbatches > 0 else 0
        print('step {0:6d}, training accuracy {1:5.3f} ({2:5d} batches trained in {3:6.4f} s, i.e. {4:9.07f} s/batch)'
              .format(i - num_steps_burn_in, train_accuracy, nbatches, dtcheck, t1batch))
        tcheck_prev = time.time()
        check_duration += (time.time() - tcheck)
        
      start_train = time.time()              # measure training time per batch
 
      if FLAGS.with_profiling:
        run_metadata = tf.RunMetadata()
        train_step_ = sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5},
                options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), run_metadata=run_metadata)
      else:
        train_step_ = sess.run(train_step, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

      duration = time.time() - start_train   # measure training time per batch
      if (i >= num_steps_burn_in):
          train_duration += duration
          train_duration_squared += duration * duration
    
    start_test = time.time()
    param_accuracy = accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
    test_duration = time.time() - start_test
    total_runtime = time.time() - burn_in_end

    mn = train_duration / mnist_steps
    vr = train_duration_squared / mnist_steps - mn * mn
    sd = math.sqrt(vr)
    print('test accuracy %g' % param_accuracy)
    print('run in %g s, test: %g s, checks: %g s, train: %g s, burn_in: %g s' % 
         (total_runtime, test_duration, check_duration, train_duration, burn_in_end - start))
    print('mean per batch %g +/- %g s' % (mn, sd))
    param_entries.append(ParamEntry(
                         datetime.now(), os.path.basename(__file__), 
                         "", mnist_batchsize, mnist_steps, param_accuracy, 
                         total_runtime, test_duration, train_duration, mn, sd))

    # Dump profiling data (*)
    if FLAGS.with_profiling:
      ProfileOptionBuilder = tf.profiler.ProfileOptionBuilder
      opts = ProfileOptionBuilder(ProfileOptionBuilder.time_and_memory()).with_node_names().build()
      tf.profiler.profile(tf.get_default_graph(),
            run_meta=run_metadata,
            cmd='code',
            options=opts)

#    prof_timeline = tf.python.client.timeline.Timeline(run_metadata.step_stats)
#    prof_ctf = prof_timeline.generate_chrome_trace_format()
#    with open('./prof_ctf.json', 'w') as fp:
#        print("Dumped to prof_ctf.json")
#        fp.write(prof_ctf)

  if FLAGS.csv_file:
    incsv.store_data_in_csv(FLAGS.csv_file, param_entries)


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument('--data_dir', type=str,
                      default='/tmp/tensorflow/mnist/input_data',
                      help='Directory for storing input data')
  parser.add_argument("--mnist_batch", type=int, default=-1,
        help="Batch size")                      
  parser.add_argument("--mnist_steps", type=int, default=-1,
        help="Number of steps to train")
  parser.add_argument("--gpu_fraction", type=float, default=0.9,
        help="GPU Memory fraction to use 0..1. Default is 0.9.")
  parser.add_argument("--with_profiling", nargs='?', const=True, type=bool, default=False,
        help="(experimental) Enable profiling. If --mnist_steps is not specified, only 2 epochs are processed!")
  parser.add_argument('--csv_file', type=str,
                      default='',
                      help='File (.csv) to output script results. If no file is passed in, csv file will not be created.')      
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)