recurrent.py 文件源码

def build(self, input_shape):
        self.input_spec = [InputSpec(shape=input_shape)]
        if self.stateful:
            self.reset_states()
        else:
            # initial states: all-zero tensor of shape (output_dim)
            self.states = [None]
        input_dim = input_shape[2]
        self.input_dim = input_dim

        self.W = self.init((input_dim, self.output_dim),
                           name='{}_W'.format(self.name))
        # Only change in build compared to SimpleRNN:
        # U is of shape (inner_input_dim, output_dim) now.
        self.U = self.inner_init((self.inner_input_dim, self.output_dim),
                                 name='{}_U'.format(self.name))
        self.b = K.zeros((self.output_dim,), name='{}_b'.format(self.name))

        self.regularizers = []
        if self.W_regularizer:
            self.W_regularizer.set_param(self.W)
            self.regularizers.append(self.W_regularizer)
        if self.U_regularizer:
            self.U_regularizer.set_param(self.U)
            self.regularizers.append(self.U_regularizer)
        if self.b_regularizer:
            self.b_regularizer.set_param(self.b)
            self.regularizers.append(self.b_regularizer)

        self.trainable_weights = [self.W, self.U, self.b]

        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights