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 = 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 = 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=rnn_cell_impl.DropoutWrapper(
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 testDropoutWrapperKerasStyle(self):
"""Tests if DropoutWrapperV2 cell is instantiated in keras style scope."""
wrapped_cell_v2 = rnn_cell_impl.DropoutWrapperV2(
rnn_cell_impl.BasicRNNCell(1))
self.assertTrue(wrapped_cell_v2._keras_style)
wrapped_cell = rnn_cell_impl.DropoutWrapper(rnn_cell_impl.BasicRNNCell(1))
self.assertFalse(wrapped_cell._keras_style)
def _build_multi_lstm_cell(num_units,
num_layers,
train_test_predict,
keep_prob=1.0):
cell = rnn_cell_impl.BasicLSTMCell(
num_units, reuse=not (train_test_predict == 'train'))
if train_test_predict == 'train' and keep_prob < 1.0:
cell = rnn_cell_impl.DropoutWrapper(cell, output_keep_prob=keep_prob)
cells = [cell for _ in range(num_layers)]
return rnn_cell_impl.MultiRNNCell(cells)
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 _rnn_cell(cell_type,
cell_size,
is_drop_out=True,
_dropout_in=1.0,
_dropout_out=0.5,
use_peepholes=True,
activation=None):
if isinstance(cell_type, rnn_cell_impl.LSTMCell):
cell = cell_type(num_units=cell_size,
use_peepholes=use_peepholes,
activation=activation)
else:
cell = cell_type(num_units=cell_size, activation=activation)
if is_drop_out:
cell = rnn_cell_impl.DropoutWrapper(cell=cell,
input_keep_prob=_dropout_in,
output_keep_prob=_dropout_out)
return cell
def get_rnncell(cell_type, cell_size, keep_prob, num_layer):
# thanks for this solution from @dimeldo
cells = []
for _ in range(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)
cells.append(cell)
if num_layer > 1:
cell = rnn_cell.MultiRNNCell(cells, state_is_tuple=True)
else:
cell = cells[0]
return cell
def testWrappedCellProperty(self):
cell = rnn_cell_impl.BasicRNNCell(10)
wrapper = rnn_cell_impl.DropoutWrapper(cell)
# Github issue 15810
self.assertEqual(wrapper.wrapped_cell, cell)
