def __init__(self, num_layers, hidden_size, dropout_prob=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = [
layers.LSTMCell(hidden_size) for i in range(num_layers)
]
def __init__(self,
num_layers,
hidden_size,
dropout_prob=0.,
forget_bias=0.):
self.num_layers = num_layers
self.hidden_size = hidden_size
self.dropout_prob = dropout_prob
self.lstm_cells = []
for i in range(num_layers):
self.lstm_cells.append(
layers.LSTMCell(
hidden_size,
forget_bias=forget_bias,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.
UniformInitializer(low=-init_scale,
high=init_scale))))
def __init__(self,
hidden_size,
cell_type="lstm",
dropout=0.0,
init_scale=-1,
name="BasicRNNCell"):
"""init of class
Args:
hidden_size (TYPE): NULL
cell_type (str): lstm|gru
dropout (TYPE): Default is 0.0
init_scale (TYPE): Default is -1
name (TYPE): Default is "BasicRNNCell"
"""
super(BasicRNNCell, self).__init__()
self._hidden_size = hidden_size
self._cell_type = cell_type.lower()
self._dropout = dropout
self._init_scale = init_scale
self._name = name
param = fluid.ParamAttr(initializer=nn_utils.uniform(self._init_scale))
bias = fluid.ParamAttr(initializer=nn_utils.zero)
if self._cell_type == 'lstm':
self._cell = layers.LSTMCell(self._hidden_size,
param,
bias,
name=self._name)
elif self._cell_type == 'gru':
self._cell = layers.GRUCell(self._hidden_size,
param,
bias,
name=self._name)
else:
raise ValueError(
"cell type only supported <lstm|gru>, but got %s" %
(cell_type))
def __init__(self,
hidden_size,
dropout=0.,
init_scale=-1,
name="rnn_decode_cell"):
"""init of class
Args:
hidden_size (TYPE): NULL
dropout (TYPE): Default is 0.
init_scale (TYPE): Default is -1, means paddle default initializer is used.
name (str): param name scope
"""
super(RNNDecodeCell, self).__init__()
self._hidden_size = hidden_size
self._dropout = dropout
self._init_scale = init_scale
self._name = name
param = fluid.ParamAttr(initializer=nn_utils.uniform(self._init_scale))
bias = fluid.ParamAttr(initializer=nn_utils.zero)
self.rnn_cell = layers.LSTMCell(hidden_size, param, bias, name=name)
