recurrent.py 文件源码

def __init__(self, input_dim, output_dim, 
        init='glorot_uniform', inner_init='orthogonal', activation='sigmoid', weights=None,
        truncate_gradient=-1, return_sequences=False):

        super(SimpleRNN,self).__init__()
        self.init = initializations.get(init)
        self.inner_init = initializations.get(inner_init)
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.truncate_gradient = truncate_gradient
        self.activation = activations.get(activation)
        self.return_sequences = return_sequences
        self.input = T.tensor3()

        self.W = self.init((self.input_dim, self.output_dim))
        self.U = self.inner_init((self.output_dim, self.output_dim))
        self.b = shared_zeros((self.output_dim))
        self.params = [self.W, self.U, self.b]

        if weights is not None:
            self.set_weights(weights)