Moved tensorflow/models to models/tutorials and replaced all tutorial references to tensorflow/models

Change: 141503531

1 files changed, 0 insertions, 157 deletions

diff --git a/tensorflow/models/image/cifar10/cifar10_eval.py b/tensorflow/models/image/cifar10/cifar10_eval.py
deleted file mode 100644
index 2f85051454..0000000000
--- a/tensorflow/models/image/cifar10/cifar10_eval.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# 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.
-# ==============================================================================
-
-"""Evaluation for CIFAR-10.
-
-Accuracy:
-cifar10_train.py achieves 83.0% accuracy after 100K steps (256 epochs
-of data) as judged by cifar10_eval.py.
-
-Speed:
-On a single Tesla K40, cifar10_train.py processes a single batch of 128 images
-in 0.25-0.35 sec (i.e. 350 - 600 images /sec). The model reaches ~86%
-accuracy after 100K steps in 8 hours of training time.
-
-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 math
-import time
-
-import numpy as np
-import tensorflow as tf
-
-from tensorflow.models.image.cifar10 import cifar10
-
-FLAGS = tf.app.flags.FLAGS
-
-tf.app.flags.DEFINE_string('eval_dir', '/tmp/cifar10_eval',
-                           """Directory where to write event logs.""")
-tf.app.flags.DEFINE_string('eval_data', 'test',
-                           """Either 'test' or 'train_eval'.""")
-tf.app.flags.DEFINE_string('checkpoint_dir', '/tmp/cifar10_train',
-                           """Directory where to read model checkpoints.""")
-tf.app.flags.DEFINE_integer('eval_interval_secs', 60 * 5,
-                            """How often to run the eval.""")
-tf.app.flags.DEFINE_integer('num_examples', 10000,
-                            """Number of examples to run.""")
-tf.app.flags.DEFINE_boolean('run_once', False,
-                         """Whether to run eval only once.""")
-
-
-def eval_once(saver, summary_writer, top_k_op, summary_op):
-  """Run Eval once.
-
-  Args:
-    saver: Saver.
-    summary_writer: Summary writer.
-    top_k_op: Top K op.
-    summary_op: Summary op.
-  """
-  with tf.Session() as sess:
-    ckpt = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
-    if ckpt and ckpt.model_checkpoint_path:
-      # Restores from checkpoint
-      saver.restore(sess, ckpt.model_checkpoint_path)
-      # Assuming model_checkpoint_path looks something like:
-      #   /my-favorite-path/cifar10_train/model.ckpt-0,
-      # extract global_step from it.
-      global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
-    else:
-      print('No checkpoint file found')
-      return
-
-    # Start the queue runners.
-    coord = tf.train.Coordinator()
-    try:
-      threads = []
-      for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
-        threads.extend(qr.create_threads(sess, coord=coord, daemon=True,
-                                         start=True))
-
-      num_iter = int(math.ceil(FLAGS.num_examples / FLAGS.batch_size))
-      true_count = 0  # Counts the number of correct predictions.
-      total_sample_count = num_iter * FLAGS.batch_size
-      step = 0
-      while step < num_iter and not coord.should_stop():
-        predictions = sess.run([top_k_op])
-        true_count += np.sum(predictions)
-        step += 1
-
-      # Compute precision @ 1.
-      precision = true_count / total_sample_count
-      print('%s: precision @ 1 = %.3f' % (datetime.now(), precision))
-
-      summary = tf.Summary()
-      summary.ParseFromString(sess.run(summary_op))
-      summary.value.add(tag='Precision @ 1', simple_value=precision)
-      summary_writer.add_summary(summary, global_step)
-    except Exception as e:  # pylint: disable=broad-except
-      coord.request_stop(e)
-
-    coord.request_stop()
-    coord.join(threads, stop_grace_period_secs=10)
-
-
-def evaluate():
-  """Eval CIFAR-10 for a number of steps."""
-  with tf.Graph().as_default() as g:
-    # Get images and labels for CIFAR-10.
-    eval_data = FLAGS.eval_data == 'test'
-    images, labels = cifar10.inputs(eval_data=eval_data)
-
-    # Build a Graph that computes the logits predictions from the
-    # inference model.
-    logits = cifar10.inference(images)
-
-    # Calculate predictions.
-    top_k_op = tf.nn.in_top_k(logits, labels, 1)
-
-    # Restore the moving average version of the learned variables for eval.
-    variable_averages = tf.train.ExponentialMovingAverage(
-        cifar10.MOVING_AVERAGE_DECAY)
-    variables_to_restore = variable_averages.variables_to_restore()
-    saver = tf.train.Saver(variables_to_restore)
-
-    # Build the summary operation based on the TF collection of Summaries.
-    summary_op = tf.summary.merge_all()
-
-    summary_writer = tf.summary.FileWriter(FLAGS.eval_dir, g)
-
-    while True:
-      eval_once(saver, summary_writer, top_k_op, summary_op)
-      if FLAGS.run_once:
-        break
-      time.sleep(FLAGS.eval_interval_secs)
-
-
-def main(argv=None):  # pylint: disable=unused-argument
-  cifar10.maybe_download_and_extract()
-  if tf.gfile.Exists(FLAGS.eval_dir):
-    tf.gfile.DeleteRecursively(FLAGS.eval_dir)
-  tf.gfile.MakeDirs(FLAGS.eval_dir)
-  evaluate()
-
-
-if __name__ == '__main__':
-  tf.app.run()