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, cache，W为超参数会导致程序错误，应该如何修改

如果W是超参数，则应该在函数外部定义并传递给函数。你可以修改函数定义，使其接受额外的W参数，如下所示：

def affine_forward(x, w, b, W):
    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, cache

在调用函数时，你需要将W作为参数传递给函数。例如：

W = np.random.randn(784, 100)
out, cache = affine_forward(x, w, b, W)

这样，你就可以在函数内部使用超参数W，而不会出现错误。

相关问题

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)报错，当修改为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时，shapes (40,6272) and (100,8192) not aligned: 6272 (dim 1) != 100 (dim 0)报错

这个错误发生在第4行的 np.dot(x_row, w_row) 上，因为x_row的第二个维度是6272，而w_row的第一个维度是100，两个维度不相等，无法进行矩阵乘法。根据错误提示，w的形状是(100,8192)，应该将x的第二个维度重新设置为8192才能进行矩阵乘法。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

全连接层：矩阵变换，获取对应目标相同的行与列 输入x: 2321616 输入x_row: 28192 超参w：8192100 输出：矩阵乘法 28192 ->8192100 =>2100def 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, cache def affine_relu_forward(x, w, b): x = x.reshape(x.shape[0], -1) w = np.random.randn(784, 100) b = np.zeros((1, 100)) out, cache = layers.affine_forward(x, w, b) a, fc_cache = layers.affine_forward(x, w, b) out, relu_cache = layers.relu_forward(a) cache = (fc_cache, relu_cache) return out, cacheValueError: shapes (40,6272) and (784,100) not aligned: 6272 (dim 1) != 784 (dim 0)

这段代码中的注释不太清楚，但是看到输入x的维度为2321616，经过reshape变换后变成了28192行，这个28192可能是由于将输入x分成了28192个样本，每个样本的维度为82。而超参w的维度为8192100，这个维度可能是由于将输入x的每个样本与w的每一列进行矩阵乘法得到一个长度为2100的向量，所以w的列数为2100。

至于输出的维度为2100，这是因为输入x的每个样本与w的每一列进行矩阵乘法得到一个长度为2100的向量，然后将这些向量拼接起来得到一个28192行、2100列的矩阵。最后再加上偏置项b，得到的是一个28192行、2100列的矩阵。