tensorflow: train and evaluate Cifar10 model during the same run. Train and test data are evaluated and sent to tensorboard.

cifar10_train_and_eval.py

# !!! Note for cifar10_train_and_eval.py !!!
#
# 1. Put this file into tensorflow/models/image/cifar10 directory.
# 2. For this file to work, you need to comment out tf.image_summary() in
#      file tensorflow/models/image/cifar_input.py
#


# Copyright 2015 Google Inc. 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 binary to train CIFAR-10 using a single GPU.

Accuracy:
cifar10_train.py achieves ~86% accuracy after 100K steps (256 epochs of
data) as judged by cifar10_eval.py.

Speed: With batch_size 128.

System        | Step Time (sec/batch)  |     Accuracy
------------------------------------------------------------------
1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)
1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)

Usage:
Please see the tutorial and website for how to download the CIFAR-10
data set, compile the program and train the model.

http://tensorflow.org/tutorials/deep_cnn/
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from datetime import datetime
import os.path
import time
import math

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

from tensorflow.models.image.cifar10 import cifar10

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',
                           """Directory where to write event logs """
                           """and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,
                            """Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
                            """Whether to log device placement.""")


def evaluate_set (sess, top_k_op, num_examples):
  """Convenience function to run evaluation for for every batch. 
     Sum the number of correct predictions and output one precision value.
  Args:
    sess:          current Session
    top_k_op:      tensor of type tf.nn.in_top_k
    num_examples:  number of examples to evaluate
  """
  num_iter = int(math.ceil(num_examples / FLAGS.batch_size))
  true_count = 0  # Counts the number of correct predictions.
  total_sample_count = num_iter * FLAGS.batch_size

  for step in xrange(num_iter):
    predictions = sess.run([top_k_op])
    true_count += np.sum(predictions)

  # Compute precision
  return true_count / total_sample_count



def train():
  """Train CIFAR-10 for a number of steps."""
  with tf.Graph().as_default():
    with tf.variable_scope("model") as scope:
      global_step = tf.Variable(0, trainable=False)

      # Get images and labels for CIFAR-10.
      images, labels = cifar10.distorted_inputs()      
      images_eval, labels_eval = cifar10.inputs(eval_data=True)

      # Build a Graph that computes the logits predictions from the
      # inference model.
      logits = cifar10.inference(images)
      scope.reuse_variables()
      logits_eval = cifar10.inference(images_eval)

      # Calculate loss.
      loss = cifar10.loss(logits, labels)

      # For evaluation
      top_k      = tf.nn.in_top_k (logits,      labels,      1)
      top_k_eval = tf.nn.in_top_k (logits_eval, labels_eval, 1)

      # Add precision summary
      summary_train_prec = tf.placeholder(tf.float32)
      summary_eval_prec  = tf.placeholder(tf.float32)
      tf.scalar_summary('precision/train', summary_train_prec)
      tf.scalar_summary('precision/eval',  summary_eval_prec)

      # Build a Graph that trains the model with one batch of examples and
      # updates the model parameters.
      train_op = cifar10.train(loss, global_step)

      # Create a saver.
      saver = tf.train.Saver(tf.all_variables())

      # Build the summary operation based on the TF collection of Summaries.
      summary_op = tf.merge_all_summaries()

      # Build an initialization operation to run below.
      init = tf.initialize_all_variables()

      # Start running operations on the Graph.
      sess = tf.Session(config=tf.ConfigProto(
	  log_device_placement=FLAGS.log_device_placement))
      sess.run(init)

      # Start the queue runners.
      tf.train.start_queue_runners(sess=sess)

      summary_writer = tf.train.SummaryWriter(FLAGS.train_dir,
					      graph_def=sess.graph_def)

      for step in xrange(FLAGS.max_steps):
	start_time = time.time()
	_, loss_value = sess.run([train_op, loss])
	duration = time.time() - start_time

	assert not np.isnan(loss_value), 'Model diverged with loss = NaN'

	if step % 10 == 0:
	  num_examples_per_step = FLAGS.batch_size
	  examples_per_sec = num_examples_per_step / duration
	  sec_per_batch = float(duration)

	  format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
			'sec/batch)')
	  print (format_str % (datetime.now(), step, loss_value,
			       examples_per_sec, sec_per_batch))

        EVAL_STEP = 10
        EVAL_NUM_EXAMPLES = 1024
        if step % EVAL_STEP == 0:
	  prec_train = evaluate_set (sess, top_k,      EVAL_NUM_EXAMPLES)
	  prec_eval  = evaluate_set (sess, top_k_eval, EVAL_NUM_EXAMPLES)
          print('%s: precision train = %.3f' % (datetime.now(), prec_train))
          print('%s: precision eval  = %.3f' % (datetime.now(), prec_eval))

	if step % 100 == 0:
	  summary_str = sess.run(summary_op, feed_dict={summary_train_prec: prec_train,
                                                  summary_eval_prec:  prec_eval})
	  summary_writer.add_summary(summary_str, step)

	# Save the model checkpoint periodically.
	if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
	  checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
	  saver.save(sess, checkpoint_path, global_step=step)


def main(argv=None):  # pylint: disable=unused-argument
  cifar10.maybe_download_and_extract()
  if tf.gfile.Exists(FLAGS.train_dir):
    tf.gfile.DeleteRecursively(FLAGS.train_dir)
  tf.gfile.MakeDirs(FLAGS.train_dir)
  train()


if __name__ == '__main__':
  tf.app.run()