Improving the training model by retraining the last layer (Team7)

label_image.py

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+# Copyright 2017 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.
+# ==============================================================================
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+
+import numpy as np
+import tensorflow as tf
+
+
+def load_graph(model_file):
+  graph = tf.Graph()
+  graph_def = tf.GraphDef()
+
+  with open(model_file, "rb") as f:
+    graph_def.ParseFromString(f.read())
+  with graph.as_default():
+    tf.import_graph_def(graph_def)
+
+  return graph
+
+
+def read_tensor_from_image_file(file_name,
+                                input_height=299,
+                                input_width=299,
+                                input_mean=0,
+                                input_std=255):
+  input_name = "file_reader"
+  output_name = "normalized"
+  file_reader = tf.read_file(file_name, input_name)
+  if file_name.endswith(".png"):
+    image_reader = tf.image.decode_png(
+        file_reader, channels=3, name="png_reader")
+  elif file_name.endswith(".gif"):
+    image_reader = tf.squeeze(
+        tf.image.decode_gif(file_reader, name="gif_reader"))
+  elif file_name.endswith(".bmp"):
+    image_reader = tf.image.decode_bmp(file_reader, name="bmp_reader")
+  else:
+    image_reader = tf.image.decode_jpeg(
+        file_reader, channels=3, name="jpeg_reader")
+  float_caster = tf.cast(image_reader, tf.float32)
+  dims_expander = tf.expand_dims(float_caster, 0)
+  resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
+  normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
+  sess = tf.Session()
+  result = sess.run(normalized)
+
+  return result
+
+
+def load_labels(label_file):
+  label = []
+  proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
+  for l in proto_as_ascii_lines:
+    label.append(l.rstrip())
+  return label
+
+
+if __name__ == "__main__":
+  file_name = "tensorflow/examples/label_image/data/grace_hopper.jpg"
+  model_file = \
+    "tensorflow/examples/label_image/data/inception_v3_2016_08_28_frozen.pb"
+  label_file = "tensorflow/examples/label_image/data/imagenet_slim_labels.txt"
+  input_height = 299
+  input_width = 299
+  input_mean = 0
+  input_std = 255
+  input_layer = "input"
+  output_layer = "InceptionV3/Predictions/Reshape_1"
+
+  parser = argparse.ArgumentParser()
+  parser.add_argument("--image", help="image to be processed")
+  parser.add_argument("--graph", help="graph/model to be executed")
+  parser.add_argument("--labels", help="name of file containing labels")
+  parser.add_argument("--input_height", type=int, help="input height")
+  parser.add_argument("--input_width", type=int, help="input width")
+  parser.add_argument("--input_mean", type=int, help="input mean")
+  parser.add_argument("--input_std", type=int, help="input std")
+  parser.add_argument("--input_layer", help="name of input layer")
+  parser.add_argument("--output_layer", help="name of output layer")
+  args = parser.parse_args()
+
+  if args.graph:
+    model_file = args.graph
+  if args.image:
+    file_name = args.image
+  if args.labels:
+    label_file = args.labels
+  if args.input_height:
+    input_height = args.input_height
+  if args.input_width:
+    input_width = args.input_width
+  if args.input_mean:
+    input_mean = args.input_mean
+  if args.input_std:
+    input_std = args.input_std
+  if args.input_layer:
+    input_layer = args.input_layer
+  if args.output_layer:
+    output_layer = args.output_layer
+
+  graph = load_graph(model_file)
+  t = read_tensor_from_image_file(
+      file_name,
+      input_height=input_height,
+      input_width=input_width,
+      input_mean=input_mean,
+      input_std=input_std)
+
+  input_name = "import/" + input_layer
+  output_name = "import/" + output_layer
+  input_operation = graph.get_operation_by_name(input_name)
+  output_operation = graph.get_operation_by_name(output_name)
+
+  with tf.Session(graph=graph) as sess:
+    results = sess.run(output_operation.outputs[0], {
+        input_operation.outputs[0]: t
+    })
+  results = np.squeeze(results)
+
+  top_k = results.argsort()[-5:][::-1]
+  labels = load_labels(label_file)
+  for i in top_k:
+    print(labels[i], results[i])

output_labels.txt

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+clouds
+daysky with lightorb
+daysky with object
+nightsky with lightorb
+trace