1 files changed, 181 insertions, 0 deletions
diff --git a/tensorflow/python/kernel_tests/relu_op_test.py b/tensorflow/python/kernel_tests/relu_op_test.py
new file mode 100644
index 0000000000..a4b353f253
--- /dev/null
+++ b/tensorflow/python/kernel_tests/relu_op_test.py
@@ -0,0 +1,181 @@
+"""Tests for Relu and ReluGrad."""
+import tensorflow.python.platform
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.kernel_tests import gradient_checker as gc
+
+
+class ReluTest(tf.test.TestCase):
+
+  def _npRelu(self, np_features):
+    return np.maximum(np_features, np.zeros(np_features.shape))
+
+  def testNpRelu(self):
+    self.assertAllClose(
+        np.array([[0.0, 0.7, 0.0, 0.3, 0.0],
+                  [0.1, 0.0, 0.5, 0.0, 0.9]]),
+        self._npRelu(np.array([[-0.9, 0.7, -0.5, 0.3, -0.1],
+                               [0.1, -0.3, 0.5, -0.7, 0.9]])))
+
+  def _testRelu(self, np_features, use_gpu=False):
+    np_relu = self._npRelu(np_features)
+    with self.test_session(use_gpu=use_gpu):
+      relu = tf.nn.relu(np_features)
+      tf_relu = relu.eval()
+    self.assertAllClose(np_relu, tf_relu)
+    self.assertShapeEqual(np_relu, relu)
+
+  def testNumbers(self):
+    for t in [np.int32, np.int64, np.float, np.double]:
+      self._testRelu(
+          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+          use_gpu=False)
+      if t in [np.float, np.double]:
+        self._testRelu(
+            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+            use_gpu=True)
+
+  # The gradient test for ReLU is a bit tricky as the derivative is not well
+  # defined at around zero and we want to avoid that in terms of input values.
+  def testGradientFloat(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], name="x")
+      y = tf.nn.relu(x, name="relu")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float32, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print "relu (float) gradient err = ", err
+    self.assertLess(err, 1e-4)
+
+  def testGradientNaN(self):
+    with self.test_session():
+      # Note the NaN is injected as an input to the gradient calculation.
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, np.nan, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], name="x")
+      y = tf.nn.relu(x, name="relu")
+      grad_ys = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], name="ys")
+      g_op = tf.gradients(
+          [y], [x], grad_ys=[grad_ys], name="gradients")[0]
+      try:
+        g_op.op.run()
+        assert False, "ReluGrad should have failed due to CheckNumerics."
+      except Exception as e:  # pylint: disable=broad-except
+        assert "ReluGrad input is not finite." in str(e)
+
+  def testGradientDouble(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], dtype=tf.float64, name="x")
+      y = tf.nn.relu(x, name="relu")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float64, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print "relu (double) gradient err = ", err
+    self.assertLess(err, 1e-10)
+
+  def testGradGradFloat(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], name="x")
+      y = tf.nn.relu(x, name="relu")
+      z = tf.gradients(y, x)
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float32, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], z[0], [2, 5],
+                                    x_init_value=x_init)
+    print "relu (float) gradient of gradient err = ", err
+    self.assertLess(err, 1e-4)
+
+  def testGradGradDouble(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 0.1, 0.3, 0.5, 0.7, 0.9],
+          shape=[2, 5], dtype=tf.float64, name="x")
+      y = tf.nn.relu(x, name="relu")
+      z = tf.gradients(y, x)
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [0.1, 0.3, 0.5, 0.7, 0.9]],
+          dtype=np.float64, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], z[0], [2, 5],
+                                    x_init_value=x_init)
+    print "relu (double) gradient of gradient err = ", err
+    self.assertLess(err, 1e-10)
+
+
+class Relu6Test(tf.test.TestCase):
+
+  def _npRelu6(self, np_features):
+    sixes = np.copy(np_features)
+    sixes.fill(6.0)
+    return np.minimum(np.maximum(np_features, np.zeros(np_features.shape)),
+                      sixes)
+
+  def testNpRelu6(self):
+    self.assertAllClose(
+        np.array([[0.0, 0.7, 0.0, 0.3, 6.0],
+                  [0.1, 0.0, 6.0, 0.0, 0.9]]),
+        self._npRelu6(np.array([[-0.9, 0.7, -0.5, 0.3, 6.0],
+                                [0.1, -0.3, 6.5, -0.7, 0.9]])))
+
+  def _testRelu6(self, np_features, use_gpu=False):
+    np_relu6 = self._npRelu6(np_features)
+    with self.test_session(use_gpu=use_gpu):
+      relu6 = tf.nn.relu6(np_features)
+      tf_relu6 = relu6.eval()
+    self.assertAllClose(np_relu6, tf_relu6)
+    self.assertShapeEqual(np_relu6, relu6)
+
+  def testNumbers(self):
+    for t in [np.int32, np.int64, np.float, np.double]:
+      self._testRelu6(
+          np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+          use_gpu=False)
+      if t in [np.float, np.double]:
+        self._testRelu6(
+            np.array([[-9, 7, -5, 3, -1], [1, -3, 5, -7, 9]]).astype(t),
+            use_gpu=True)
+
+  # The gradient test for ReLU6 is a bit tricky as the derivative is
+  # not well defined at around zero and six and we want to avoid that
+  # in terms of input values.
+  def testGradientFloat(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
+          shape=[2, 5], name="x")
+      y = tf.nn.relu6(x, name="relu6")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
+          dtype=np.float32, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print "relu6 (float) gradient err = ", err
+    self.assertLess(err, 1e-4)
+
+  def testGradientDouble(self):
+    with self.test_session():
+      x = tf.constant(
+          [-0.9, -0.7, -0.5, -0.3, -0.1, 6.1, 6.3, 6.5, 6.7, 6.9],
+          shape=[2, 5], dtype=tf.float64, name="x")
+      y = tf.nn.relu6(x, name="relu6")
+      x_init = np.asarray(
+          [[-0.9, -0.7, -0.5, -0.3, -0.1], [6.1, 6.3, 6.5, 6.7, 6.9]],
+          dtype=np.float64, order="F")
+      err = gc.ComputeGradientError(x, [2, 5], y, [2, 5], x_init_value=x_init)
+    print "relu6 (double) gradient err = ", err
+    self.assertLess(err, 1e-10)
+
+
+if __name__ == "__main__":
+  tf.test.main()