qa_cnn.py 文件源码

def __call__(self, q_input, a_input, *args, **kwargs):
        # convolve input feature maps with filters
        q_conv_out = conv2d(
            input=q_input,
            filters=self.W,
            filter_shape=self.filter_shape
        )
        a_conv_out = conv2d(
            input=a_input,
            filters=self.W,
            filter_shape=self.filter_shape
        )
        # add the bias term. Since the bias is a vector (1D array), we first
        # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
        # thus be broadcasted across mini-batches and feature map
        # width & height
        if self.non_linear == "tanh":
            q_conv_out_tanh = Tanh(q_conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
            a_conv_out_tanh = Tanh(a_conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
            q_output = pool.pool_2d(input=q_conv_out_tanh, ws=self.pool_size, ignore_border=True) # max
            a_output = pool.pool_2d(input=a_conv_out_tanh, ws=self.pool_size, ignore_border=True)
        elif self.non_linear == "relu":
            q_conv_out_relu = ReLU(q_conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
            a_conv_out_relu = ReLU(a_conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
            q_output = pool.pool_2d(input=q_conv_out_relu, ws=self.pool_size, ignore_border=True)
            a_output = pool.pool_2d(input=a_conv_out_relu, ws=self.pool_size, ignore_border=True)
        else:
            q_output = pool.pool_2d(input=q_conv_out, ws=self.pool_size, ignore_border=True)
            a_output = pool.pool_2d(input=a_conv_out, ws=self.pool_size, ignore_border=True)

        return q_output, a_output