def _testDropoutWrapper(self, batch_size=None, time_steps=None,
parallel_iterations=None, **kwargs):
with self.test_session() as sess:
with variable_scope.variable_scope(
"root", initializer=init_ops.constant_initializer(0.5)):
if batch_size is None and time_steps is None:
# 2 time steps, batch size 1, depth 3
batch_size = 1
time_steps = 2
x = constant_op.constant(
[[[2., 2., 2.]], [[1., 1., 1.]]], dtype=dtypes.float32)
m = core_rnn_cell_impl.LSTMStateTuple(
*[constant_op.constant([[0.1, 0.1, 0.1]],
dtype=dtypes.float32)] * 2)
else:
x = constant_op.constant(
np.random.randn(time_steps, batch_size, 3).astype(np.float32))
m = core_rnn_cell_impl.LSTMStateTuple(
*[constant_op.constant([[0.1, 0.1, 0.1]] * batch_size,
dtype=dtypes.float32)] * 2)
outputs, final_state = rnn.dynamic_rnn(
cell=core_rnn_cell_impl.DropoutWrapper(
core_rnn_cell_impl.LSTMCell(3),
dtype=x.dtype,
**kwargs),
time_major=True,
parallel_iterations=parallel_iterations,
inputs=x, initial_state=m)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run([outputs, final_state])
self.assertEqual(res[0].shape, (time_steps, batch_size, 3))
self.assertEqual(res[1].c.shape, (batch_size, 3))
self.assertEqual(res[1].h.shape, (batch_size, 3))
return res
def get_rnncell(cell_type, cell_size, keep_prob, num_layer):
if cell_type == "gru":
cell = rnn_cell.GRUCell(cell_size)
else:
cell = rnn_cell.LSTMCell(cell_size, use_peepholes=False, forget_bias=1.0)
if keep_prob < 1.0:
cell = rnn_cell.DropoutWrapper(cell, output_keep_prob=keep_prob)
if num_layer > 1:
cell = rnn_cell.MultiRNNCell([cell] * num_layer, state_is_tuple=True)
return cell
def testDropoutWrapper(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, 3])
m = array_ops.zeros([1, 3])
keep = array_ops.zeros([]) + 1
g, new_m = core_rnn_cell_impl.DropoutWrapper(
core_rnn_cell_impl.GRUCell(3), keep, keep)(x, m)
sess.run([variables_lib.global_variables_initializer()])
res = sess.run(
[g, new_m], {
x.name: np.array([[1., 1., 1.]]),
m.name: np.array([[0.1, 0.1, 0.1]])
})
self.assertEqual(res[1].shape, (1, 3))
# The numbers in results were not calculated, this is just a smoke test.
self.assertAllClose(res[0], [[0.154605, 0.154605, 0.154605]])
