47ed18a3af8be20dce91286a331d4671074ee0ca,keras/layers/recurrent.py,LSTM,build,#LSTM#,398

Before Change

        self.W_i = self.init((input_dim, self.output_dim))
        self.U_i = self.inner_init((self.output_dim, self.output_dim))
        self.b_i = shared_zeros((self.output_dim))

        self.W_f = self.init((input_dim, self.output_dim))
        self.U_f = self.inner_init((self.output_dim, self.output_dim))
        self.b_f = self.forget_bias_init((self.output_dim))

After Change

        self.input = K.placeholder(input_shape)

        if self.stateful:
            if not input_shape[0]:
                raise Exception("If a RNN is stateful, a complete " +
                                "input_shape must be provided " +
                                "(including batch size).")
            self.states = [K.zeros(input_shape[0], self.output_dim),
                           K.zeros(input_shape[0], self.output_dim)]
        else:
            // initial states: 2 all-zero tensor of shape (output_dim)
            self.states = [None, None]

Instances