动手学深度学习3——优化和深度学习

鞍点

一维梯度下降

学习率

小批量随机梯度下降

动量法

图像增广

d2l.set_figsize()
img = Image.open('../../img/cat1.jpg')
d2l.plt.imshow(img)
def show_images(imgs, num_rows, num_cols, scale=2):
figsize = (num_cols * scale, num_rows * scale)
_, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
for i in range(num_rows):
for j in range(num_cols):
axes[i][j].imshow(imgs[i * num_cols + j])
axes[i][j].axes.get_xaxis().set_visible(False)
axes[i][j].axes.get_yaxis().set_visible(False)
return axes
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
show_images(Y, num_rows, num_cols, scale)

图像翻转

#左右翻转
apply(img, torchvision.transforms.RandomHorizontalFlip())
#上下翻转
apply(img, torchvision.transforms.RandomVerticalFlip())

图像裁剪

#随机裁剪原图的10%--100%
shape_aug = torchvision.transforms.RandomResizedCrop(200, scale=
(0.1, 1), ratio=(0.5, 2))
apply(img, shape_aug)

变换颜色

#更改亮度brightness
apply(img, torchvision.transforms.ColorJitter(brightness=0.5))
#更改色调 hue
apply(img, torchvision.transforms.ColorJitter(hue=0.5))
#更改对比度 contrast
apply(img, torchvision.transforms.ColorJitter(contrast=0.5))
#同时更改多种类型
color_aug = torchvision.transforms.ColorJitter(
brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5)
apply(img, color_aug)
#通过compose将上面定义的多个图像增光方法叠加起来，再应用到每张图像上
augs = torchvision.transforms.Compose([
torchvision.transforms.RandomHorizontalFlip(), color_aug,
shape_aug])
apply(img, augs)