1 files changed, 93 insertions, 0 deletions
diff --git a/tensorflow/python/kernel_tests/bias_op_test.py b/tensorflow/python/kernel_tests/bias_op_test.py
new file mode 100644
index 0000000000..f3a26e2490
--- /dev/null
+++ b/tensorflow/python/kernel_tests/bias_op_test.py
@@ -0,0 +1,93 @@
+"""Functional tests for BiasAdd."""
+import tensorflow.python.platform
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow.python.kernel_tests import gradient_checker
+
+
+class BiasAddTest(tf.test.TestCase):
+
+  def _npBias(self, inputs, bias):
+    assert len(bias.shape) == 1
+    print inputs.shape
+    print bias.shape
+    assert inputs.shape[-1] == bias.shape[0]
+    return inputs + bias.reshape(([1] * (len(inputs.shape) - 1))
+                                 + [bias.shape[0]])
+
+  def testNpBias(self):
+    self.assertAllClose(np.array([[11, 22, 33], [41, 52, 63]]),
+                        self._npBias(np.array([[10, 20, 30], [40, 50, 60]]),
+                                     np.array([1, 2, 3])))
+
+  def _testBias(self, np_inputs, np_bias, use_gpu=False):
+    np_val = self._npBias(np_inputs, np_bias)
+    with self.test_session(use_gpu=use_gpu):
+      tf_val = tf.nn.bias_add(np_inputs, np_bias).eval()
+    self.assertAllClose(np_val, tf_val)
+
+  def _testAll(self, np_inputs, np_bias):
+    self._testBias(np_inputs, np_bias, use_gpu=False)
+    if np_inputs.dtype == np.float32 or np_inputs.dtype == np.float64:
+      self._testBias(np_inputs, np_bias, use_gpu=True)
+
+  def testInputDims(self):
+    with self.assertRaises(ValueError):
+      tf.nn.bias_add([1, 2], [1])
+
+  def testBiasVec(self):
+    with self.assertRaises(ValueError):
+      tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
+                      tf.reshape([1, 2], shape=[1, 2]))
+
+  def testBiasInputsMatch(self):
+    with self.assertRaises(ValueError):
+      tf.nn.bias_add(tf.reshape([1, 2], shape=[1, 2]),
+                      tf.reshape([1], shape=[1]))
+
+  def testIntTypes(self):
+    for t in [np.int8, np.int16, np.int32, np.int64]:
+      self._testAll(np.array([[10, 20, 30], [40, 50, 60]]).astype(t),
+                    np.array([1, 2, 3]).astype(t))
+
+  def testFloatTypes(self):
+    for t in [np.float32, np.float64]:
+      self._testAll(np.random.rand(4, 3, 3).astype(t),
+                    np.random.rand(3).astype(t))
+
+  def testGradientTensor(self):
+    with self.test_session():
+      t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
+                               dtype=tf.float64)
+      b = tf.constant([1.3, 2.4], dtype=tf.float64)
+      bo = tf.nn.bias_add(t, b)
+      err = gradient_checker.ComputeGradientError(t, [3, 2], bo, [3, 2])
+    print "bias add tensor gradient err = ", err
+    self.assertLess(err, 1e-10)
+
+  def testGradientBias(self):
+    with self.test_session():
+      t = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2],
+                               dtype=tf.float64)
+      b = tf.constant([1.3, 2.4], dtype=tf.float64)
+      bo = tf.nn.bias_add(t, b)
+      err = gradient_checker.ComputeGradientError(b, [2], bo, [3, 2])
+    print "bias add bias gradient err = ", err
+    self.assertLess(err, 1e-10)
+
+  def testGradientTensor4D(self):
+    with self.test_session():
+      s = [2, 3, 4, 2]
+      x = np.arange(1.0, 49.0).reshape(s).astype(np.float32)
+      t = tf.constant(x, shape=s, dtype=tf.float32)
+      b = tf.constant([1.3, 2.4], dtype=tf.float32)
+      bo = tf.nn.bias_add(t, b)
+      err = gradient_checker.ComputeGradientError(t, s, bo, s, x_init_value=x)
+    print "bias add tensor gradient err = ", err
+    self.assertLess(err, 1e-3)
+
+
+if __name__ == "__main__":
+  tf.test.main()