用于创建小批量的切片

Question

我打算为我的深度学习神经网络程序创建迷你批次，从一个由'm‘个示例组成的训练集。我试过了：

# First Shuffle (X, Y)
permutation = list(np.random.permutation(m))
shuffled_X = X[:, permutation]
shuffled_Y = Y[:, permutation].reshape((1,m))

# Partition (shuffled_X, shuffled_Y). Minus the end case where mini-batch will contain lesser number of training samples.
num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
for k in range(0, num_complete_minibatches):
    ### START CODE HERE ### (approx. 2 lines)
    mini_batch_X = shuffled_X[mini_batch_size*k:mini_batch_size*(k+2)]
    mini_batch_Y = shuffled_Y[mini_batch_size*k:mini_batch_size*(k+2)]

但这给了我以下结果：

shape of the 1st mini_batch_X: (128, 148)
shape of the 2nd mini_batch_X: (128, 148)
shape of the 3rd mini_batch_X: (12288, 148)
shape of the 1st mini_batch_Y: (1, 148)
shape of the 2nd mini_batch_Y: (0, 148)
shape of the 3rd mini_batch_Y: (1, 148)
mini batch sanity check: [ 0.90085595 -0.7612069   0.2344157 ]

预期输出为：

shape of the 1st mini_batch_X   (12288, 64)
shape of the 2nd mini_batch_X   (12288, 64)
shape of the 3rd mini_batch_X   (12288, 20)
shape of the 1st mini_batch_Y   (1, 64)
shape of the 2nd mini_batch_Y   (1, 64)
shape of the 3rd mini_batch_Y   (1, 20)
mini batch sanity check [ 0.90085595 -0.7612069 0.2344157 ] 

我确信我已经实现的切片有问题，但无法解决它。任何帮助都是非常感谢的。谢谢!

Answer 1

我认为你没有正确地切分numpy数组。最初，当您对数组进行混洗时，这种方式是正确的。您不希望对第一个维度进行切片，因此请保持使用:的方式，并使用<Start Index>:<End Index>对第二个维度进行切片。这就是我在下面的代码中所做的事情。

for k in range(num_complete_minibatches+1):
### START CODE HERE ### (approx. 2 lines)
    mini_batch_X = shuffled_X[:,mini_batch_size*(k):mini_batch_size*(k+1)]
    mini_batch_Y = shuffled_Y[:,mini_batch_size*(k):mini_batch_size*(k+1)]
    print(mini_batch_X.shape,mini_batch_Y.shape)