本文整理汇总了Python中tensorflow.contrib.rnn.ResidualWrapper方法的典型用法代码示例。如果您正苦于以下问题:Python rnn.ResidualWrapper方法的具体用法?Python rnn.ResidualWrapper怎么用?Python rnn.ResidualWrapper使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.rnn
的用法示例。
在下文中一共展示了rnn.ResidualWrapper方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _make_cell
# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import ResidualWrapper [as 别名]
def _make_cell(self, hidden_size=None):
"""Create a single RNN cell"""
cell = self.cell_type(hidden_size or self.hidden_size)
if self.dropout:
cell = rnn.DropoutWrapper(cell, self.keep_prob)
if self.residual:
cell = rnn.ResidualWrapper(cell)
return cell
示例2: build_single_cell
# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import ResidualWrapper [as 别名]
def build_single_cell(self, n_hidden, use_residual):
"""构建一个单独的rnn cell
Args:
n_hidden: 隐藏层神经元数量
use_residual: 是否使用residual wrapper
"""
if self.cell_type == 'gru':
cell_type = GRUCell
else:
cell_type = LSTMCell
cell = cell_type(n_hidden)
if self.use_dropout:
cell = DropoutWrapper(
cell,
dtype=tf.float32,
output_keep_prob=self.keep_prob_placeholder,
seed=self.seed
)
if use_residual:
cell = ResidualWrapper(cell)
return cell
示例3: create_cell
# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import ResidualWrapper [as 别名]
def create_cell(unit_type, hidden_units, num_layers, use_residual=False, input_keep_prob=1.0, output_keep_prob=1.0, devices=None):
if unit_type == 'lstm':
def _new_cell():
return tf.nn.rnn_cell.BasicLSTMCell(hidden_units)
elif unit_type == 'gru':
def _new_cell():
return tf.contrib.rnn.GRUCell(hidden_units)
else:
raise ValueError('cell_type must be either lstm or gru')
def _new_cell_wrapper(residual_connection=False, device_id=None):
c = _new_cell()
if input_keep_prob < 1.0 or output_keep_prob < 1.0:
c = rnn.DropoutWrapper(c, input_keep_prob=input_keep_prob, output_keep_prob=output_keep_prob)
if residual_connection:
c = rnn.ResidualWrapper(c)
if device_id:
c = rnn.DeviceWrapper(c, device_id)
return c
if num_layers > 1:
cells = []
for i in range(num_layers):
is_residual = True if use_residual and i > 0 else False
cells.append(_new_cell_wrapper(is_residual, devices[i] if devices else None))
return tf.contrib.rnn.MultiRNNCell(cells)
else:
return _new_cell_wrapper(device_id=devices[0] if devices else None)
示例4: _single_cell
# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import ResidualWrapper [as 别名]
def _single_cell(unit_type,
num_units,
forget_bias,
dropout,
mode,
residual_connection=False,
residual_fn=None,
trainable=True):
"""Create an instance of a single RNN cell."""
# dropout (= 1 - keep_prob) is set to 0 during eval and infer
dropout = dropout if mode == tf.estimator.ModeKeys.TRAIN else 0.0
# Cell Type
if unit_type == "lstm":
single_cell = contrib_rnn.LSTMCell(
num_units, forget_bias=forget_bias, trainable=trainable)
elif unit_type == "gru":
single_cell = contrib_rnn.GRUCell(num_units, trainable=trainable)
elif unit_type == "layer_norm_lstm":
single_cell = contrib_rnn.LayerNormBasicLSTMCell(
num_units,
forget_bias=forget_bias,
layer_norm=True,
trainable=trainable)
elif unit_type == "nas":
single_cell = contrib_rnn.NASCell(num_units, trainable=trainable)
else:
raise ValueError("Unknown unit type %s!" % unit_type)
# Dropout (= 1 - keep_prob).
if dropout > 0.0:
single_cell = contrib_rnn.DropoutWrapper(
cell=single_cell, input_keep_prob=(1.0 - dropout))
# Residual.
if residual_connection:
single_cell = contrib_rnn.ResidualWrapper(
single_cell, residual_fn=residual_fn)
return single_cell
示例5: rnn_cell
# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import ResidualWrapper [as 别名]
def rnn_cell(rnn_cell_size, dropout_keep_prob, residual, is_training=True):
"""Builds an LSTMBlockCell based on the given parameters."""
dropout_keep_prob = dropout_keep_prob if is_training else 1.0
cells = []
for i in range(len(rnn_cell_size)):
cell = rnn.LSTMBlockCell(rnn_cell_size[i])
if residual:
cell = rnn.ResidualWrapper(cell)
if i == 0 or rnn_cell_size[i] != rnn_cell_size[i - 1]:
cell = rnn.InputProjectionWrapper(cell, rnn_cell_size[i])
cell = rnn.DropoutWrapper(
cell,
input_keep_prob=dropout_keep_prob)
cells.append(cell)
return rnn.MultiRNNCell(cells)