deep-learning - X.reshape(X.shape[0], -1).T & X.reshape(-1, X.shape[0]) 的区别

Question

我正在尝试解决编程评估：来自 Andrew NG 在 coursera 上的“神经网络与深度学习课程”第 2 周的带有神经网络思维的逻辑回归。

这是代码：

# X.reshape(X.shape[0], -1).T
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
print(train_set_x_flatten.shape)
print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))
train_set_x_flattenExtra = train_set_x_orig.reshape(-1, train_set_x_orig.shape[0])
print ("train_set_x_flattenExtra shape: " + str(train_set_x_flattenExtra.shape))
print()

# X.reshape(-1, X.shape[0])
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
print((test_set_x_orig.reshape(-1, test_set_x_orig.shape[0])).shape)
print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))
test_set_x_flattenExtra = test_set_x_orig.reshape(-1, test_set_x_orig.shape[0])
print(test_set_x_flattenExtra.shape)
print ("train_set_x_flattenExtra shape: " + str(train_set_x_flattenExtra.shape))
print()

根据我的理解，两者都应该做同样的事情，输出也显示相同的形状，但 coursera 不验证 X.reshape(-1, X.shape[0]) 方法。

这两个 fn 的工作方式不同还是只是 coursera 没有验证另一种方法

输出： 输出