diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/rnn_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/rnn_test.py | 33 |
1 files changed, 21 insertions, 12 deletions
diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index 7d040c221e..be59ac08c2 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -782,11 +782,11 @@ class BidirectionalRNNTest(tf.test.TestCase): tf.float32, shape=(batch_size, input_size) if use_shape else (None, input_size)) ] - outputs = tf.nn.bidirectional_rnn(cell_fw, - cell_bw, - inputs, - dtype=tf.float32, - sequence_length=sequence_length) + outputs, state_fw, state_bw = tf.nn.bidirectional_rnn(cell_fw, + cell_bw, + inputs, + dtype=tf.float32, + sequence_length=sequence_length) self.assertEqual(len(outputs), len(inputs)) for out in outputs: self.assertEqual( @@ -794,17 +794,19 @@ class BidirectionalRNNTest(tf.test.TestCase): [batch_size if use_shape else None, 2 * num_units]) input_value = np.random.randn(batch_size, input_size) + outputs = tf.pack(outputs) - return input_value, inputs, outputs, sequence_length + return input_value, inputs, outputs, state_fw, state_bw, sequence_length def _testBidirectionalRNN(self, use_gpu, use_shape): with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess: - input_value, inputs, outputs, sequence_length = ( + input_value, inputs, outputs, state_fw, state_bw, sequence_length = ( self._createBidirectionalRNN(use_gpu, use_shape, True)) tf.initialize_all_variables().run() # Run with pre-specified sequence length of 2, 3 - out = sess.run(outputs, feed_dict={inputs[0]: input_value, - sequence_length: [2, 3]}) + out, s_fw, s_bw = sess.run([outputs, state_fw, state_bw], + feed_dict={inputs[0]: input_value, + sequence_length: [2, 3]}) # Since the forward and backward LSTM cells were initialized with the # same parameters, the forward and backward output has to be the same, @@ -836,13 +838,17 @@ class BidirectionalRNNTest(tf.test.TestCase): self.assertEqual(out[2][1][0], out[0][1][3]) self.assertEqual(out[2][1][1], out[0][1][4]) self.assertEqual(out[2][1][2], out[0][1][5]) + # Via the reasoning above, the forward and backward final state should be + # exactly the same + self.assertAllClose(s_fw, s_bw) def _testBidirectionalRNNWithoutSequenceLength(self, use_gpu, use_shape): with self.test_session(use_gpu=use_gpu, graph=tf.Graph()) as sess: - input_value, inputs, outputs, _ = self._createBidirectionalRNN( - use_gpu, use_shape, False) + input_value, inputs, outputs, state_fw, state_bw, _ = self._createBidirectionalRNN( + use_gpu, use_shape, False) tf.initialize_all_variables().run() - out = sess.run(outputs, feed_dict={inputs[0]: input_value}) + out, s_fw, s_bw = sess.run([outputs, state_fw, state_bw], + feed_dict={inputs[0]: input_value}) # Since the forward and backward LSTM cells were initialized with the # same parameters, the forward and backward output has to be the same, @@ -861,6 +867,9 @@ class BidirectionalRNNTest(tf.test.TestCase): self.assertEqual(out[i][1][0], out[8 - 1 - i][1][3]) self.assertEqual(out[i][1][1], out[8 - 1 - i][1][4]) self.assertEqual(out[i][1][2], out[8 - 1 - i][1][5]) + # Via the reasoning above, the forward and backward final state should be + # exactly the same + self.assertAllClose(s_fw, s_bw) def testBidirectionalRNN(self): self._testBidirectionalRNN(use_gpu=False, use_shape=False) |