recurrent.py 文件源码

def __init__(self, input_dim, output_dim, depth=3,
        init='glorot_uniform', inner_init='orthogonal', 
        activation='sigmoid', inner_activation='hard_sigmoid',
        weights=None, truncate_gradient=-1, return_sequences=False):

        super(SimpleDeepRNN,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.inner_activation = activations.get(inner_activation)
        self.depth = depth
        self.return_sequences = return_sequences
        self.input = T.tensor3()

        self.W = self.init((self.input_dim, self.output_dim))
        self.Us = [self.inner_init((self.output_dim, self.output_dim)) for _ in range(self.depth)]
        self.b = shared_zeros((self.output_dim))
        self.params = [self.W] + self.Us + [self.b]

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