machine-learning - 为什么宽度是此代码中的第一个维度？

Question

fig, ax = plt.subplots(figsize = (14, 14)) 

i = randint(0, len(X_train))
img = X_train[i]

ax.imshow(img, cmap='gray')
ax.set_title('Digit: {}'.format(y_train[i]), fontsize=16)

width, height = img.shape
thresh = img.max()/2.5
for x in range(width):
    for y in range(height):
        ax.annotate('{:2}'.format(img[x][y]), 
                    xy=(y,x),
                    horizontalalignment='center',
                    verticalalignment='center',
                    color='white' if img[x][y]<thresh else 'black')

此代码来自https://github.com/stefan-jansen/machine-learning-for-trading/blob/main/18_convolutional_neural_nets/02_digit_classification_with_lenet5.ipynb

我认为高度应该是第一个维度，我将代码更改为

fig, ax = plt.subplots(figsize = (14, 14)) 

i = randint(0, len(X_train))
img = X_train[i]

ax.imshow(img, cmap='gray')
ax.set_title('Digit: {}'.format(y_train[i]), fontsize=16)

height, width = img.shape
thresh = img.max()/2.5
for x in range(height):
    for y in range(width):
        ax.annotate('{:2}'.format(img[x][y]), 
                    xy=(y,x),
                    horizontalalignment='center',
                    verticalalignment='center',
                    color='white' if img[x][y]<thresh else 'black')

他们得到了相同的输出，但我仍然不明白为什么作者将宽度设为第一个维度？

标签： machine-learningdeep-learningcomputer-visionconv-neural-network