convolution.py 文件源码

def forward(self):
        pad = [[self.lay.pad, self.lay.pad]] * 2;
        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])

        k = self.lay.w['kernels']
        ksz = self.lay.ksize
        half = int(ksz / 2)
        out = list()
        for i in range(self.lay.h_out):
            row_i = list()
            for j in range(self.lay.w_out):
                kij = k[i * self.lay.w_out + j]
                i_, j_ = i + 1 - half, j + 1 - half
                tij = temp[:, i_ : i_ + ksz, j_ : j_ + ksz,:]
                row_i.append(
                    tf.nn.conv2d(tij, kij, 
                        padding = 'VALID', 
                        strides = [1] * 4))
            out += [tf.concat(row_i, 2)]

        self.out = tf.concat(out, 1)