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author | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
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committer | Manjunath Kudlur <keveman@gmail.com> | 2015-11-06 16:27:58 -0800 |
commit | f41959ccb2d9d4c722fe8fc3351401d53bcf4900 (patch) | |
tree | ef0ca22cb2a5ac4bdec9d080d8e0788a53ed496d /tensorflow/python/kernel_tests/bias_op_test.py |
TensorFlow: Initial commit of TensorFlow library.
TensorFlow is an open source software library for numerical computation
using data flow graphs.
Base CL: 107276108
Diffstat (limited to 'tensorflow/python/kernel_tests/bias_op_test.py')
-rw-r--r-- | tensorflow/python/kernel_tests/bias_op_test.py | 93 |
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() |