numpy手写卷积

一.单通道输入，单卷积核

def numpy_conv(inputs, myfilter):
    h_ori, w_ori = inputs.shape
    h_k, w_k= myfilter.shape
    
    h_new, w_new = h_ori-h_k+1,w_ori-w_k+1
    result = np.zeros((h_new, w_new))
    
    for row in range(0, h_new, 1):
        for col in range(0, w_new, 1):
            # 池化大小的输入区域
            cur_input = inputs[row:row+h_k, col:col+w_k]
            #和核进行乘法计算
            cur_output = cur_input * myfilter
            #再把所有值求和
            conv_sum = np.sum(cur_output)
            #当前点输出值
            result[row, col] = conv_sum
    return result         

二.多通道多卷积核

def _conv(inputs, filter,strides=[1,1], padding="SAME"):
    C_in, H, W = inputs.shape
    filter_size = filter.shape[2]
    # C_out指核对个数，也是最后结果对通道个数
    C_out = filter.shape[0]