1 files changed, 0 insertions, 93 deletions
diff --git a/tensorflow/python/keras/applications/imagenet_utils_test.py b/tensorflow/python/keras/applications/imagenet_utils_test.py
deleted file mode 100644
index 037e939ac5..0000000000
--- a/tensorflow/python/keras/applications/imagenet_utils_test.py
+++ /dev/null
@@ -1,93 +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.
-# ==============================================================================
-"""Tests for Inception V3 application."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import numpy as np
-
-from tensorflow.python import keras
-from tensorflow.python.keras.applications.imagenet_utils import preprocess_input
-from tensorflow.python.platform import test
-
-
-class ImageNetUtilsTest(test.TestCase):
-
-  def test_preprocess_input(self):
-    # Test batch of images
-    x = np.random.uniform(0, 255, (2, 10, 10, 3))
-    self.assertEqual(preprocess_input(x).shape, x.shape)
-    out1 = preprocess_input(x, 'channels_last')
-    out2 = preprocess_input(np.transpose(x, (0, 3, 1, 2)), 'channels_first')
-    self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
-
-    # Test single image
-    x = np.random.uniform(0, 255, (10, 10, 3))
-    self.assertEqual(preprocess_input(x).shape, x.shape)
-    out1 = preprocess_input(x, 'channels_last')
-    out2 = preprocess_input(np.transpose(x, (2, 0, 1)), 'channels_first')
-    self.assertAllClose(out1, out2.transpose(1, 2, 0))
-
-  def test_preprocess_input_symbolic(self):
-    # Test image batch
-    x = np.random.uniform(0, 255, (2, 10, 10, 3))
-    inputs = keras.layers.Input(shape=x.shape[1:])
-    outputs = keras.layers.Lambda(
-        preprocess_input, output_shape=x.shape[1:])(inputs)
-    model = keras.models.Model(inputs, outputs)
-    assert model.predict(x).shape == x.shape
-    # pylint: disable=g-long-lambda
-    outputs1 = keras.layers.Lambda(lambda x:
-                                   preprocess_input(x, 'channels_last'),
-                                   output_shape=x.shape[1:])(inputs)
-    model1 = keras.models.Model(inputs, outputs1)
-    out1 = model1.predict(x)
-    x2 = np.transpose(x, (0, 3, 1, 2))
-    inputs2 = keras.layers.Input(shape=x2.shape[1:])
-    # pylint: disable=g-long-lambda
-    outputs2 = keras.layers.Lambda(lambda x:
-                                   preprocess_input(x, 'channels_first'),
-                                   output_shape=x2.shape[1:])(inputs2)
-    model2 = keras.models.Model(inputs2, outputs2)
-    out2 = model2.predict(x2)
-    self.assertAllClose(out1, out2.transpose(0, 2, 3, 1))
-
-    # Test single image
-    x = np.random.uniform(0, 255, (10, 10, 3))
-    inputs = keras.layers.Input(shape=x.shape)
-    outputs = keras.layers.Lambda(preprocess_input,
-                                  output_shape=x.shape)(inputs)
-    model = keras.models.Model(inputs, outputs)
-    assert model.predict(x[np.newaxis])[0].shape == x.shape
-    # pylint: disable=g-long-lambda
-    outputs1 = keras.layers.Lambda(lambda x:
-                                   preprocess_input(x, 'channels_last'),
-                                   output_shape=x.shape)(inputs)
-    model1 = keras.models.Model(inputs, outputs1)
-    out1 = model1.predict(x[np.newaxis])[0]
-    x2 = np.transpose(x, (2, 0, 1))
-    inputs2 = keras.layers.Input(shape=x2.shape)
-    outputs2 = keras.layers.Lambda(lambda x:
-                                   preprocess_input(x, 'channels_first'),
-                                   output_shape=x2.shape)(inputs2)  # pylint: disable=g-long-lambda
-    model2 = keras.models.Model(inputs2, outputs2)
-    out2 = model2.predict(x2[np.newaxis])[0]
-    self.assertAllClose(out1, out2.transpose(1, 2, 0))
-
-
-if __name__ == '__main__':
-  test.main()