Move Keras code out of _impl folder and remove API files.

PiperOrigin-RevId: 197097430

1 files changed, 103 insertions, 0 deletions

diff --git a/tensorflow/python/keras/constraints_test.py b/tensorflow/python/keras/constraints_test.py
new file mode 100644
index 0000000000..84e2db1033
--- /dev/null
+++ b/tensorflow/python/keras/constraints_test.py
@@ -0,0 +1,103 @@
+# 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 Keras weights constraints."""
+
+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.platform import test
+
+
+def get_test_values():
+  return [0.1, 0.5, 3, 8, 1e-7]
+
+
+def get_example_array():
+  np.random.seed(3537)
+  example_array = np.random.random((100, 100)) * 100. - 50.
+  example_array[0, 0] = 0.  # 0 could possibly cause trouble
+  return example_array
+
+
+class KerasConstraintsTest(test.TestCase):
+
+  def test_serialization(self):
+    all_activations = ['max_norm', 'non_neg',
+                       'unit_norm', 'min_max_norm']
+    for name in all_activations:
+      fn = keras.constraints.get(name)
+      ref_fn = getattr(keras.constraints, name)()
+      assert fn.__class__ == ref_fn.__class__
+      config = keras.constraints.serialize(fn)
+      fn = keras.constraints.deserialize(config)
+      assert fn.__class__ == ref_fn.__class__
+
+  def test_max_norm(self):
+    with self.test_session():
+      array = get_example_array()
+      for m in get_test_values():
+        norm_instance = keras.constraints.max_norm(m)
+        normed = norm_instance(keras.backend.variable(array))
+        assert np.all(keras.backend.eval(normed) < m)
+
+      # a more explicit example
+      norm_instance = keras.constraints.max_norm(2.0)
+      x = np.array([[0, 0, 0], [1.0, 0, 0], [3, 0, 0], [3, 3, 3]]).T
+      x_normed_target = np.array([[0, 0, 0], [1.0, 0, 0],
+                                  [2.0, 0, 0],
+                                  [2. / np.sqrt(3),
+                                   2. / np.sqrt(3),
+                                   2. / np.sqrt(3)]]).T
+      x_normed_actual = keras.backend.eval(
+          norm_instance(keras.backend.variable(x)))
+      self.assertAllClose(x_normed_actual, x_normed_target, rtol=1e-05)
+
+  def test_non_neg(self):
+    with self.test_session():
+      non_neg_instance = keras.constraints.non_neg()
+      normed = non_neg_instance(keras.backend.variable(get_example_array()))
+      assert np.all(np.min(keras.backend.eval(normed), axis=1) == 0.)
+
+  def test_unit_norm(self):
+    with self.test_session():
+      unit_norm_instance = keras.constraints.unit_norm()
+      normalized = unit_norm_instance(
+          keras.backend.variable(get_example_array()))
+      norm_of_normalized = np.sqrt(
+          np.sum(keras.backend.eval(normalized) ** 2, axis=0))
+      # In the unit norm constraint, it should be equal to 1.
+      difference = norm_of_normalized - 1.
+      largest_difference = np.max(np.abs(difference))
+      assert np.abs(largest_difference) < 10e-5
+
+  def test_min_max_norm(self):
+    with self.test_session():
+      array = get_example_array()
+      for m in get_test_values():
+        norm_instance = keras.constraints.min_max_norm(min_value=m,
+                                                       max_value=m * 2)
+        normed = norm_instance(keras.backend.variable(array))
+        value = keras.backend.eval(normed)
+        l2 = np.sqrt(np.sum(np.square(value), axis=0))
+        assert not l2[l2 < m]
+        assert not l2[l2 > m * 2 + 1e-5]
+
+
+if __name__ == '__main__':
+  test.main()