For static shape computations, use common_shapes.broadcast_shape..

Change: 130019247

2 files changed, 0 insertions, 140 deletions

diff --git a/tensorflow/examples/image_retraining/data/labels.txt b/tensorflow/examples/image_retraining/data/labels.txt
deleted file mode 100644
index bc1131ac45..0000000000
--- a/tensorflow/examples/image_retraining/data/labels.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-Runner-up
-Winner
-Loser
diff --git a/tensorflow/examples/image_retraining/label_image.py b/tensorflow/examples/image_retraining/label_image.py
deleted file mode 100644
index c93cab2893..0000000000
--- a/tensorflow/examples/image_retraining/label_image.py
+++ /dev/null
@@ -1,137 +0,0 @@
-
-# Copyright 2016 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.
-# ==============================================================================
-
-
-"""Simple image classification with Inception.
-
-Run image classification with your model.
-
-This script is usually used with retrain.py found in this same
-directory.
-
-This program creates a graph from a saved GraphDef protocol buffer,
-and runs inference on an input JPEG image. You are required
-to pass in the graph file and the txt file.
-
-It outputs human readable strings of the top 5 predictions along with
-their probabilities.
-
-Change the --image_file argument to any jpg image to compute a
-classification of that image.
-
-Example usage:
-python label_image.py --graph=retrained_graph.pb
-  --labels=retrained_labels.txt
-  --image=flower_photos/daisy/54377391_15648e8d18.jpg
-
-NOTE: To learn to use this file and retrain.py, please see:
-
-https://codelabs.developers.google.com/codelabs/tensorflow-for-poets
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import tensorflow as tf
-
-FLAGS = tf.app.flags.FLAGS
-
-# Flag definitions.
-tf.app.flags.DEFINE_string('image', 'NOT_SET',
-                           """Absolute path to image file.""")
-tf.app.flags.DEFINE_integer('num_top_predictions', 5,
-                            """Display this many predictions.""")
-tf.app.flags.DEFINE_string('graph', 'NOT_SET',
-                           """Absolute path to graph file (.pb)""")
-tf.app.flags.DEFINE_string('labels', 'NOT_SET',
-                           """Absolute path to labels file (.txt)""")
-
-tf.app.flags.DEFINE_string('output_layer', 'final_result:0',
-                           """Name of the result operation""")
-tf.app.flags.DEFINE_string('input_layer', 'DecodeJpeg/contents:0',
-                           """Name of the input operation""")
-
-
-def load_image(filename):
-  """Read in the image_data to be classified."""
-  return tf.gfile.FastGFile(filename, 'rb').read()
-
-
-def load_labels(filename):
-  """Read in labels, one label per line."""
-  return [line.rstrip() for line in tf.gfile.GFile(filename)]
-
-
-def load_graph(filename):
-  """Unpersists graph from file as default graph."""
-  with tf.gfile.FastGFile(filename, 'rb') as f:
-    graph_def = tf.GraphDef()
-    graph_def.ParseFromString(f.read())
-    tf.import_graph_def(graph_def, name='')
-
-
-def run_graph(image_data, labels, input_layer_name, output_layer_name):
-  with tf.Session() as sess:
-    # Feed the image_data as input to the graph.
-    #   predictions  will contain a two-dimensional array, where one
-    #   dimension represents the input image count, and the other has
-    #   predictions per class
-    predictions = sess.run(output_layer_name,
-                           {input_layer_name: image_data})
-
-    # We're only ever passing one image in to be classified, so we
-    # just need the first line of the softmax_tensor.
-    predictions = predictions[0]
-
-    # Sort to show labels in order of confidence
-    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
-    for node_id in top_k:
-      human_string = labels[node_id]
-      score = predictions[node_id]
-      print('%s (score = %.5f)' % (human_string, score))
-
-    return 0
-
-
-def main(_):
-  """Runs inference on an image.
-
-  Returns:
-    Nothing
-  """
-
-  if not tf.gfile.Exists(FLAGS.image):
-    tf.logging.fatal('image file does not exist %s', FLAGS.image)
-
-  if not tf.gfile.Exists(FLAGS.labels):
-    tf.logging.fatal('labels file does not exist %s', FLAGS.labels)
-
-  if not tf.gfile.Exists(FLAGS.graph):
-    tf.logging.fatal('graph file does not exist %s', FLAGS.graph)
-
-  # load image
-  image_data = load_image(FLAGS.image)
-
-  # load labels
-  labels = load_labels(FLAGS.labels)
-
-  # load graph, which is stored in the default session
-  load_graph(FLAGS.graph)
-
-  run_graph(image_data, labels, FLAGS.input_layer, FLAGS.output_layer)
-
-if __name__ == '__main__':
-  tf.app.run()