def affine_forward(x, w, b): out = None N = x.shape[0] x_row = x.reshape(N, -1) out = np.dot(x_row, w) + b.reshape(1, -1) cache = (x, w, b) return out, cacheshapes (40,6272) and (784,100) not aligned: 6272 (dim 1) != 784 (dim 0)

这个错误发生在第3行的 np.dot(x_row, w) 上，因为x和w的维度不兼容。x的形状是(40,6272)，w的形状是(784,100)，两个矩阵的第二个维度不相等，所以无法进行矩阵乘法。要解决这个问题，需要将w转置成(100,784)的形状，然后再进行矩阵乘法。修改代码如下：

def affine_forward(x, w, b):
    out = None
    N = x.shape[0]
    x_row = x.reshape(N, -1)
    w_row = w.reshape(w.shape[0], -1).T
    out = np.dot(x_row, w_row) + b.reshape(1, -1)
    cache = (x, w, b)
    return out, cache