diff options
Diffstat (limited to 'tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py')
| -rw-r--r-- | tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py | 103 |
1 files changed, 54 insertions, 49 deletions
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index 3c84c34726..67e026dabf 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -154,6 +154,7 @@ class RNNTest(test.TestCase): def setUp(self): self._seed = 23489 np.random.seed(self._seed) + ops_lib.reset_default_graph() def testInvalidSequenceLengthShape(self): cell = Plus1RNNCell() @@ -583,7 +584,7 @@ class LSTMTest(test.TestCase): (state_notuple_v,) = sess.run((state_notuple,), feed_dict={inputs[0]: input_value}) state_tuple_v = sess.run(state_tuple, feed_dict={inputs[0]: input_value}) - self.assertAllEqual(state_notuple_v, np.hstack(state_tuple_v)) + self.assertAllClose(state_notuple_v, np.hstack(state_tuple_v)) def _testProjSharding(self, use_gpu): num_units = 3 @@ -806,7 +807,7 @@ class LSTMTest(test.TestCase): self.assertEqual(len(outputs0_values), len(outputs2_values)) for o1, o2, o3 in zip(outputs0_values, outputs1_values, outputs2_values): # Same weights used by both RNNs so outputs should be the same. - self.assertAllEqual(o1, o2) + self.assertAllClose(o1, o2) # Different weights used so outputs should be different. self.assertTrue(np.linalg.norm(o1 - o3) > 1e-6) @@ -844,7 +845,7 @@ class LSTMTest(test.TestCase): outputs1_values = output_values[max_length:] self.assertEqual(len(outputs0_values), len(outputs1_values)) for out0, out1 in zip(outputs0_values, outputs1_values): - self.assertAllEqual(out0, out1) + self.assertAllClose(out0, out1) def testNoProjNoShardingSimpleStateSaver(self): self._testNoProjNoShardingSimpleStateSaver(use_gpu=False) @@ -934,13 +935,13 @@ class LSTMTest(test.TestCase): feed_dict={inputs[0]: input_value}) outputs_dynamic_v = sess.run(outputs_dynamic, feed_dict={inputs[0]: input_value}) - self.assertAllEqual(outputs_static_v, outputs_dynamic_v) + self.assertAllClose(outputs_static_v, outputs_dynamic_v) state_static_v = sess.run(state_static, feed_dict={inputs[0]: input_value}) state_dynamic_v = sess.run(state_dynamic, feed_dict={inputs[0]: input_value}) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v)) def testDynamicRNNWithNestedTupleStates(self): num_units = 3 @@ -1003,13 +1004,13 @@ class LSTMTest(test.TestCase): feed_dict={inputs[0]: input_value}) outputs_dynamic_v = sess.run(outputs_dynamic, feed_dict={inputs[0]: input_value}) - self.assertAllEqual(outputs_static_v, outputs_dynamic_v) + self.assertAllClose(outputs_static_v, outputs_dynamic_v) state_static_v = sess.run(nest.flatten(state_static), feed_dict={inputs[0]: input_value}) state_dynamic_v = sess.run(nest.flatten(state_dynamic), feed_dict={inputs[0]: input_value}) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v)) def _testDynamicEquivalentToStaticRNN(self, use_gpu, use_sequence_length): time_steps = 8 @@ -1038,7 +1039,9 @@ class LSTMTest(test.TestCase): use_peepholes=True, initializer=initializer, num_proj=num_proj, - state_is_tuple=False) + state_is_tuple=False, + # TODO(b/XXX): Defun name aliasing causes errors + compiled=False) with variable_scope.variable_scope("dynamic_scope"): outputs_static, state_static = core_rnn.static_rnn( @@ -1096,7 +1099,9 @@ class LSTMTest(test.TestCase): use_peepholes=True, initializer=initializer, num_proj=num_proj, - state_is_tuple=False) + state_is_tuple=False, + # TODO(b/XXX): Defun name aliasing causes errors + compiled=False) with variable_scope.variable_scope("dynamic_scope"): outputs_dynamic, state_dynamic = rnn.dynamic_rnn( @@ -1150,10 +1155,10 @@ class LSTMTest(test.TestCase): ######### Step 3: Comparisons self.assertEqual(len(values_static), len(values_dynamic)) for (value_static, value_dynamic) in zip(values_static, values_dynamic): - self.assertAllEqual(value_static, value_dynamic) - self.assertAllEqual(state_value_static, state_value_dynamic) + self.assertAllClose(value_static, value_dynamic) + self.assertAllClose(state_value_static, state_value_dynamic) - self.assertAllEqual(static_grad_values, dynamic_grad_values) + self.assertAllClose(static_grad_values, dynamic_grad_values) self.assertEqual( len(static_individual_grad_values), len(dynamic_individual_grad_values)) @@ -1164,14 +1169,14 @@ class LSTMTest(test.TestCase): for i, (a, b) in enumerate( zip(static_individual_grad_values, dynamic_individual_grad_values)): tf_logging.info("Comparing individual gradients iteration %d" % i) - self.assertAllEqual(a, b) + self.assertAllClose(a, b) for i, (a, b) in enumerate( zip(static_individual_var_grad_values, dynamic_individual_var_grad_values)): tf_logging.info("Comparing individual variable gradients iteration %d" % i) - self.assertAllEqual(a, b) + self.assertAllClose(a, b) def testDynamicEquivalentToStaticRNN(self): self._testDynamicEquivalentToStaticRNN( @@ -1293,13 +1298,13 @@ class BidirectionalRNNTest(test.TestCase): # Both sequences in batch are length=8. Check that the time=i # forward output is equal to time=8-1-i backward output for i in xrange(8): - self.assertEqual(out[i][0][0], out[8 - 1 - i][0][3]) - self.assertEqual(out[i][0][1], out[8 - 1 - i][0][4]) - self.assertEqual(out[i][0][2], out[8 - 1 - i][0][5]) + self.assertAllClose(out[i][0][0], out[8 - 1 - i][0][3]) + self.assertAllClose(out[i][0][1], out[8 - 1 - i][0][4]) + self.assertAllClose(out[i][0][2], out[8 - 1 - i][0][5]) for i in xrange(8): - 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]) + self.assertAllClose(out[i][1][0], out[8 - 1 - i][1][3]) + self.assertAllClose(out[i][1][1], out[8 - 1 - i][1][4]) + self.assertAllClose(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) @@ -1399,27 +1404,27 @@ class BidirectionalRNNTest(test.TestCase): # Check that the time=0 forward output is equal to time=1 backward output if not use_time_major: out = np.swapaxes(out, 0, 1) - self.assertEqual(out[0][0][0], out[1][0][3]) - self.assertEqual(out[0][0][1], out[1][0][4]) - self.assertEqual(out[0][0][2], out[1][0][5]) + self.assertAllClose(out[0][0][0], out[1][0][3]) + self.assertAllClose(out[0][0][1], out[1][0][4]) + self.assertAllClose(out[0][0][2], out[1][0][5]) # Check that the time=1 forward output is equal to time=0 backward output - self.assertEqual(out[1][0][0], out[0][0][3]) - self.assertEqual(out[1][0][1], out[0][0][4]) - self.assertEqual(out[1][0][2], out[0][0][5]) + self.assertAllClose(out[1][0][0], out[0][0][3]) + self.assertAllClose(out[1][0][1], out[0][0][4]) + self.assertAllClose(out[1][0][2], out[0][0][5]) # Second sequence in batch is length=3 # Check that the time=0 forward output is equal to time=2 backward output - self.assertEqual(out[0][1][0], out[2][1][3]) - self.assertEqual(out[0][1][1], out[2][1][4]) - self.assertEqual(out[0][1][2], out[2][1][5]) + self.assertAllClose(out[0][1][0], out[2][1][3]) + self.assertAllClose(out[0][1][1], out[2][1][4]) + self.assertAllClose(out[0][1][2], out[2][1][5]) # Check that the time=1 forward output is equal to time=1 backward output - self.assertEqual(out[1][1][0], out[1][1][3]) - self.assertEqual(out[1][1][1], out[1][1][4]) - self.assertEqual(out[1][1][2], out[1][1][5]) + self.assertAllClose(out[1][1][0], out[1][1][3]) + self.assertAllClose(out[1][1][1], out[1][1][4]) + self.assertAllClose(out[1][1][2], out[1][1][5]) # Check that the time=2 forward output is equal to time=0 backward output - 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]) + self.assertAllClose(out[2][1][0], out[0][1][3]) + self.assertAllClose(out[2][1][1], out[0][1][4]) + self.assertAllClose(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) @@ -1560,13 +1565,13 @@ class MultiDimensionalLSTMTest(test.TestCase): outputs_sav_v = sess.run(outputs_sav, feed_dict={inputs_using_dim[0]: input_value}) - self.assertAllEqual(outputs_static_v, outputs_dynamic_v) - self.assertAllEqual(outputs_static_v, outputs_sav_v) + self.assertAllClose(outputs_static_v, outputs_dynamic_v) + self.assertAllClose(outputs_static_v, outputs_sav_v) outputs_static_array = np.array(outputs_static_v) outputs_static_array_double = np.concatenate( (outputs_static_array, outputs_static_array), axis=2) outputs_bid_array = np.array(outputs_bid_v) - self.assertAllEqual(outputs_static_array_double, outputs_bid_array) + self.assertAllClose(outputs_static_array_double, outputs_bid_array) state_static_v = sess.run(state_static, feed_dict={inputs[0]: input_value}) @@ -1578,10 +1583,10 @@ class MultiDimensionalLSTMTest(test.TestCase): feed_dict={inputs_using_dim[0]: input_value}) state_sav_v = sess.run(state_sav, feed_dict={inputs_using_dim[0]: input_value}) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_sav_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_bw_v)) class NestedLSTMTest(test.TestCase): @@ -1663,14 +1668,14 @@ class NestedLSTMTest(test.TestCase): outputs_bid_v = sess.run(outputs_bid, feed_dict={single_input_using_dim: input_value}) - self.assertAllEqual(outputs_static_v, + self.assertAllClose(outputs_static_v, np.transpose(outputs_dynamic_v, (1, 0, 2, 3))) - self.assertAllEqual(outputs_static_v, outputs_sav_v) + self.assertAllClose(outputs_static_v, outputs_sav_v) outputs_static_array = np.array(outputs_static_v) outputs_static_array_double = np.concatenate( (outputs_static_array, outputs_static_array), axis=3) outputs_bid_array = np.array(outputs_bid_v) - self.assertAllEqual(outputs_static_array_double, outputs_bid_array) + self.assertAllClose(outputs_static_array_double, outputs_bid_array) state_dynamic_v = sess.run(state_dynamic, feed_dict={single_input: input_value}) @@ -1682,10 +1687,10 @@ class NestedLSTMTest(test.TestCase): feed_dict={single_input_using_dim: input_value}) state_sav_v = sess.run(state_sav, feed_dict={single_input_using_dim: input_value}) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_dynamic_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_sav_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) - self.assertAllEqual(np.hstack(state_static_v), np.hstack(state_bid_bw_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_dynamic_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_sav_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_fw_v)) + self.assertAllClose(np.hstack(state_static_v), np.hstack(state_bid_bw_v)) class StateSaverRNNTest(test.TestCase): |