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)