卷积神经网络中，用Im2col实现卷积层，但是在调用convolution类时报错

Question

这是实现代码，是深度学习入门书上的代码

import numpy as np


def im2col(input_data, filter_h, filter_w, stride=1, pad=0):
    """
    Parameters
    ----------
    input_data : 由(数据量, 通道, 高, 长)的4维数组构成的输入数据
    filter_h : 卷积核的高
    filter_w : 卷积核的长
    stride : 步幅
    pad : 填充

    Returns
    -------
    col : 2维数组
    """
    # 输入数据的形状
    # N：批数目，C：通道数，H：输入数据高，W：输入数据长
    N, C, H, W = input_data.shape
    out_h = (H + 2 * pad - filter_h) // stride + 1  # 输出数据的高
    out_w = (W + 2 * pad - filter_w) // stride + 1  # 输出数据的长
    # 填充 H,W
    img = np.pad(input_data, [(0, 0), (0, 0), (pad, pad), (pad, pad)], 'constant')
    # (N, C, filter_h, filter_w, out_h, out_w)的0矩阵
    col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))

    for y in range(filter_h):
        y_max = y + stride * out_h
        for x in range(filter_w):
            x_max = x + stride * out_w
            col[:, :, y, x, :, :] = img[:, :, y:y_max:stride, x:x_max:stride]
    # 按(0, 4, 5, 1, 2, 3)顺序，交换col的列，然后改变形状
    col = col.transpose(0, 4, 5, 1, 2, 3).reshape(N * out_h * out_w, -1)
    return col



class Convolution:
    def __init__(self,W,b,stride=1,pad=0):
        self.W = W
        self.b = b
        self.stride = stride
        self.pad = pad
    def forward(self,x):
        FN,C,FH,FW=self.W.shape
        N,C,H,W = x.shape
        out_h = int(1+(H+ 2*self.pad - FH) / self.stride)
        out_w = int(1+(W + 2*self.pad -FW) / self.stride)

        col = im2col(x,FH,FW,self.stride,self.pad)
        col_W = self.W.reshape(FN,-1).T  #矩阵的转置
        out = np.dot(col,col_W) + self.b
        print(self.W)
        out = out.reshape(N,out_h,out_w,-1).transpose(0,3,1,2)
        return out

e = np.array([[2,0,1],[0,1,2],[1,0,2]])
x = np.array([[1,2,3,0],[0,1,2,3],[3,0,1,2],[2,3,0,1]])
gdd = Convolution(e,3,1,0)
gdd.forward(x)

在gdd.forward（x）出现错误：
Traceback (most recent call last):
File "E:/PycharmProjects/untitled2/kk.py", line 61, in
gdd.forward(x)
File "E:/PycharmProjects/untitled2/kk.py", line 46, in forward
FN,C,FH,FW=self.W.shape
ValueError: not enough values to unpack (expected 4, got 2)

Answer 1

FN,C,FH,FW这里有4个变量，但是self.W.shape的长度只有2。