diff options
Diffstat (limited to 'tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py')
| -rw-r--r-- | tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py | 161 |
1 files changed, 115 insertions, 46 deletions
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index 86f1e27abd..85f0f8ced9 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools import os import numpy as np @@ -35,7 +34,6 @@ from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops -from tensorflow.python.ops import random_ops from tensorflow.python.ops import rnn from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope @@ -117,6 +115,27 @@ class RNNCellTest(test.TestCase): }) self.assertEqual(res[0].shape, (1, 2)) + def testIndRNNCell(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2]) + m = array_ops.zeros([1, 2]) + cell = contrib_rnn_cell.IndRNNCell(2) + g, _ = cell(x, m) + self.assertEqual([ + "root/ind_rnn_cell/%s_w:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/ind_rnn_cell/%s_u:0" % rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/ind_rnn_cell/%s:0" % rnn_cell_impl._BIAS_VARIABLE_NAME + ], [v.name for v in cell.trainable_variables]) + self.assertFalse(cell.non_trainable_variables) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + self.assertEqual(res[0].shape, (1, 2)) + def testGRUCell(self): with self.test_session() as sess: with variable_scope.variable_scope( @@ -145,6 +164,34 @@ class RNNCellTest(test.TestCase): # Smoke test self.assertAllClose(res[0], [[0.156736, 0.156736]]) + def testIndyGRUCell(self): + with self.test_session() as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.IndyGRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + # Smoke test + self.assertAllClose(res[0], [[0.185265, 0.17704]]) + with variable_scope.variable_scope( + "other", initializer=init_ops.constant_initializer(0.5)): + # Test IndyGRUCell with input_size != num_units. + x = array_ops.zeros([1, 3]) + m = array_ops.zeros([1, 2]) + g, _ = contrib_rnn_cell.IndyGRUCell(2)(x, m) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run([g], { + x.name: np.array([[1., 1., 1.]]), + m.name: np.array([[0.1, 0.1]]) + }) + # Smoke test + self.assertAllClose(res[0], [[0.155127, 0.157328]]) + def testSRUCell(self): with self.test_session() as sess: with variable_scope.variable_scope( @@ -345,6 +392,72 @@ class RNNCellTest(test.TestCase): self.assertAllClose(res[1], expected_mem0) self.assertAllClose(res[2], expected_mem1) + def testIndyLSTMCell(self): + for dtype in [dtypes.float16, dtypes.float32]: + np_dtype = dtype.as_numpy_dtype + with self.test_session(graph=ops.Graph()) as sess: + with variable_scope.variable_scope( + "root", initializer=init_ops.constant_initializer(0.5)): + x = array_ops.zeros([1, 2], dtype=dtype) + state_0 = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + state_1 = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + cell = rnn_cell_impl.MultiRNNCell( + [contrib_rnn_cell.IndyLSTMCell(2) for _ in range(2)]) + self.assertEqual(cell.dtype, None) + self.assertEqual("cell-0", cell._checkpoint_dependencies[0].name) + self.assertEqual("cell-1", cell._checkpoint_dependencies[1].name) + cell.get_config() # Should not throw an error + g, (out_state_0, out_state_1) = cell(x, (state_0, state_1)) + # Layer infers the input type. + self.assertEqual(cell.dtype, dtype.name) + expected_variable_names = [ + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s_w:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s_u:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_0/indy_lstm_cell/%s:0" % + rnn_cell_impl._BIAS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s_w:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s_u:0" % + rnn_cell_impl._WEIGHTS_VARIABLE_NAME, + "root/multi_rnn_cell/cell_1/indy_lstm_cell/%s:0" % + rnn_cell_impl._BIAS_VARIABLE_NAME + ] + self.assertEqual(expected_variable_names, + [v.name for v in cell.trainable_variables]) + self.assertFalse(cell.non_trainable_variables) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run( + [g, out_state_0, out_state_1], { + x.name: np.array([[1., 1.]]), + state_0[0].name: 0.1 * np.ones([1, 2]), + state_0[1].name: 0.1 * np.ones([1, 2]), + state_1[0].name: 0.1 * np.ones([1, 2]), + state_1[1].name: 0.1 * np.ones([1, 2]), + }) + self.assertEqual(len(res), 3) + variables = variables_lib.global_variables() + self.assertEqual(expected_variable_names, [v.name for v in variables]) + # Only check the range of outputs as this is just a smoke test. + self.assertAllInRange(res[0], -1.0, 1.0) + self.assertAllInRange(res[1], -1.0, 1.0) + self.assertAllInRange(res[2], -1.0, 1.0) + with variable_scope.variable_scope( + "other", initializer=init_ops.constant_initializer(0.5)): + # Test IndyLSTMCell with input_size != num_units. + x = array_ops.zeros([1, 3], dtype=dtype) + state = (array_ops.zeros([1, 2], dtype=dtype),) * 2 + g, out_state = contrib_rnn_cell.IndyLSTMCell(2)(x, state) + sess.run([variables_lib.global_variables_initializer()]) + res = sess.run( + [g, out_state], { + x.name: np.array([[1., 1., 1.]], dtype=np_dtype), + state[0].name: 0.1 * np.ones([1, 2], dtype=np_dtype), + state[1].name: 0.1 * np.ones([1, 2], dtype=np_dtype), + }) + self.assertEqual(len(res), 2) + def testLSTMCell(self): with self.test_session() as sess: num_units = 8 @@ -935,50 +1048,6 @@ class DropoutWrapperTest(test.TestCase): self.assertAllClose(res0[1].h, res1[1].h) -class SlimRNNCellTest(test.TestCase): - - def testBasicRNNCell(self): - with self.test_session() as sess: - with variable_scope.variable_scope( - "root", initializer=init_ops.constant_initializer(0.5)): - x = array_ops.zeros([1, 2]) - m = array_ops.zeros([1, 2]) - my_cell = functools.partial(basic_rnn_cell, num_units=2) - # pylint: disable=protected-access - g, _ = rnn_cell_impl._SlimRNNCell(my_cell)(x, m) - # pylint: enable=protected-access - sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([g], { - x.name: np.array([[1., 1.]]), - m.name: np.array([[0.1, 0.1]]) - }) - self.assertEqual(res[0].shape, (1, 2)) - - def testBasicRNNCellMatch(self): - batch_size = 32 - input_size = 100 - num_units = 10 - with self.test_session() as sess: - with variable_scope.variable_scope( - "root", initializer=init_ops.constant_initializer(0.5)): - inputs = random_ops.random_uniform((batch_size, input_size)) - _, initial_state = basic_rnn_cell(inputs, None, num_units) - rnn_cell = rnn_cell_impl.BasicRNNCell(num_units) - outputs, state = rnn_cell(inputs, initial_state) - variable_scope.get_variable_scope().reuse_variables() - my_cell = functools.partial(basic_rnn_cell, num_units=num_units) - # pylint: disable=protected-access - slim_cell = rnn_cell_impl._SlimRNNCell(my_cell) - # pylint: enable=protected-access - slim_outputs, slim_state = slim_cell(inputs, initial_state) - self.assertEqual(slim_outputs.get_shape(), outputs.get_shape()) - self.assertEqual(slim_state.get_shape(), state.get_shape()) - sess.run([variables_lib.global_variables_initializer()]) - res = sess.run([slim_outputs, slim_state, outputs, state]) - self.assertAllClose(res[0], res[2]) - self.assertAllClose(res[1], res[3]) - - def basic_rnn_cell(inputs, state, num_units, scope=None): if state is None: if inputs is not None: |