本文整理汇总了Python中keras.layers.RNN属性的典型用法代码示例。如果您正苦于以下问题：Python layers.RNN属性的具体用法？Python layers.RNN怎么用？Python layers.RNN使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类keras.layers的用法示例。
在下文中一共展示了layers.RNN属性的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: __init__
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import RNN [as 别名]
def __init__(self, layers, cell_type, cell_params):
"""
Build the rnn with the given number of layers.
:param layers: list
list of integers. The i-th element of the list is the number of hidden neurons for the i-th layer.
:param cell_type: 'gru', 'rnn', 'lstm'
:param cell_params: dict
A dictionary containing all the paramters for the RNN cell.
see keras.layers.LSTMCell, keras.layers.GRUCell or keras.layers.SimpleRNNCell for more details.
"""
# init params
self.model = None
self.horizon = None
self.layers = layers
self.cell_params = cell_params
if cell_type == 'lstm':
self.cell = LSTMCell
elif cell_type == 'gru':
self.cell = GRUCell
elif cell_type == 'rnn':
self.cell = SimpleRNNCell
else:
raise NotImplementedError('{0} is not a valid cell type.'.format(cell_type))
# Build deep rnn
self.rnn = self._build_rnn() 示例2: _build_rnn
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import RNN [as 别名]
def _build_rnn(self):
cells = []
for _ in range(self.layers):
cells.append(self.cell(**self.cell_params))
deep_rnn = RNN(cells, return_sequences=False, return_state=False)
return deep_rnn 示例3: __init__
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import RNN [as 别名]
def __init__(self,
encoder_layers,
decoder_layers,
output_sequence_length,
dropout=0.0,
l2=0.01,
cell_type='lstm'):
"""
:param encoder_layers: list
encoder (RNN) architecture: [n_hidden_units_1st_layer, n_hidden_units_2nd_layer, ...]
:param decoder_layers: list
decoder (RNN) architecture: [n_hidden_units_1st_layer, n_hidden_units_2nd_layer, ...]
:param output_sequence_length: int
number of timestep to be predicted.
:param cell_type: str
gru or lstm.
"""
self.encoder_layers = encoder_layers
self.decoder_layers = decoder_layers
self.output_sequence_length = output_sequence_length
self.dropout = dropout
self.l2 = l2
if cell_type == 'lstm':
self.cell = LSTMCell
elif cell_type == 'gru':
self.cell = GRUCell
else:
raise ValueError('{0} is not a valid cell type. Choose between gru and lstm.'.format(cell_type)) 示例4: _build_encoder
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import RNN [as 别名]
def _build_encoder(self):
"""
Build the encoder multilayer RNN (stacked RNN)
"""
# Create a list of RNN Cells, these get stacked one after the other in the RNN,
# implementing an efficient stacked RNN
encoder_cells = []
for n_hidden_neurons in self.encoder_layers:
encoder_cells.append(self.cell(units=n_hidden_neurons,
dropout=self.dropout,
kernel_regularizer=l2(self.l2),
recurrent_regularizer=l2(self.l2)))
self.encoder = RNN(encoder_cells, return_state=True, name='encoder') 示例5: _build_decoder
# 需要导入模块: from keras import layers [as 别名]
# 或者: from keras.layers import RNN [as 别名]
def _build_decoder(self):
decoder_cells = []
for n_hidden_neurons in self.decoder_layers:
decoder_cells.append(self.cell(units=n_hidden_neurons,
dropout=self.dropout,
kernel_regularizer=l2(self.l2),
recurrent_regularizer=l2(self.l2)
))
# return output for EACH timestamp
self.decoder = RNN(decoder_cells, return_sequences=True, return_state=True, name='decoder')