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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？那么恭喜您, 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模块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，您的评价将有助于我们的系统推荐出更棒的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))
开发者ID:albertogaspar，项目名称:dts，代码行数:30，代码来源:Seq2Seq.py
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')
开发者ID:albertogaspar，项目名称:dts，代码行数:16，代码来源:Seq2Seq.py
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')
开发者ID:albertogaspar，项目名称:dts，代码行数:12，代码来源:Seq2Seq.py
注：本文中的keras.layers.RNN属性
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