PointerLSTM.py 文件源码

def step(self, x_input, states):
        #print "x_input:", x_input, x_input.shape
        # <TensorType(float32, matrix)>

        input_shape = self.input_spec[0].shape
        en_seq = states[-1]
        _, [h, c] = super(PointerLSTM, self).step(x_input, states[:-1])

        # vt*tanh(W1*e+W2*d)
        dec_seq = K.repeat(h, input_shape[1])
        Eij = time_distributed_dense(en_seq, self.W1, output_dim=1)
        Dij = time_distributed_dense(dec_seq, self.W2, output_dim=1)
        U = self.vt * tanh(Eij + Dij)
        U = K.squeeze(U, 2)

        # make probability tensor
        pointer = softmax(U)
        return pointer, [h, c]