为什么是false？
①检查GPU是否支持CUDA？
支持
理解Package结构及法宝函数的作用
pytorch就像一个工具箱
dir():打开操作，能看到里面有什么东西---->dir(torch)
help():说明书---->help(torch.cuda.is_available)
From torch.utils.data import Dataset
pytorch如何读取数据？
①Dataset
提供一种方式去获取数据及其label
如何获取每一个数据及其label
告诉我们总共有多少的数据
②Dataloader
为网络提供不同的数据形式
Tensorboard的使用
Global_step：x轴
Scalar_value：y轴
如何读取logs里面的文件
PS F:\HDU\PyTorchStudy> tensorboard --logdir=logs
指定端口：tensorboard --logdir=logs --port=6007
结果：有图像展示
图像变换，transform的使用
利用numpy.array()，对PIL图片进行转换
torchvision中的transforms:对图像进行一个变换
Transforms.py 工具箱
Totensor/resize/。。。
图片--->工具 --->结果
结果：
tensor([[[0.3137, 0.3137, 0.3137, ..., 0.3176, 0.3098, 0.2980],
0.3176, 0.3176, 0.3176, ..., 0.3176, 0.3098, 0.2980\], \[0.3216, 0.3216, 0.3216, ..., 0.3137, 0.3098, 0.3020\], ..., \[0.3412, 0.3412, 0.3373, ..., 0.1725, 0.3725, 0.3529\], \[0.3412, 0.3412, 0.3373, ..., 0.3294, 0.3529, 0.3294\], \[0.3412, 0.3412, 0.3373, ..., 0.3098, 0.3059, 0.3294\]\], \[\[0.5922, 0.5922, 0.5922, ..., 0.5961, 0.5882, 0.5765\], \[0.5961, 0.5961, 0.5961, ..., 0.5961, 0.5882, 0.5765\], \[0.6000, 0.6000, 0.6000, ..., 0.5922, 0.5882, 0.5804\], ..., \[0.6275, 0.6275, 0.6235, ..., 0.3608, 0.6196, 0.6157\], \[0.6275, 0.6275, 0.6235, ..., 0.5765, 0.6275, 0.5961\], \[0.6275, 0.6275, 0.6235, ..., 0.6275, 0.6235, 0.6314\]\], \[\[0.9137, 0.9137, 0.9137, ..., 0.9176, 0.9098, 0.8980\], \[0.9176, 0.9176, 0.9176, ..., 0.9176, 0.9098, 0.8980\], \[0.9216, 0.9216, 0.9216, ..., 0.9137, 0.9098, 0.9020\], ..., \[0.9294, 0.9294, 0.9255, ..., 0.5529, 0.9216, 0.8941\], \[0.9294, 0.9294, 0.9255, ..., 0.8863, 1.0000, 0.9137\], \[0.9294, 0.9294, 0.9255, ..., 0.9490, 0.9804, 0.9137\]\]\])    常见的Transforms |----|---------|--------------| | 输入 | PIL | Image.open() | | 输出 | tensor | ToTensor() | | 作用 | narrays | Cv.imread() | call的使用  __call__Hello zhangsan Hellolisi   归一化：Normalize output\[channel\] = (input\[channel\] - mean\[channel\]) / std\[channel
<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x333 at 0x232A68BC488>
tensor(0.8863)
tensor(0.7725)
Resize()的使用
(500, 333)
tensor([[[0.8863, 0.8824, 0.8745, ..., 0.8392, 0.8392, 0.8392],
0.8824, 0.8784, 0.8706, ..., 0.8392, 0.8392, 0.8392\], \[0.8784, 0.8745, 0.8667, ..., 0.8353, 0.8353, 0.8353\], ..., \[1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314\], \[1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314\], \[1.0000, 1.0000, 1.0000, ..., 0.8314, 0.8314, 0.8314\]\], \[\[0.1451, 0.1412, 0.1333, ..., 0.8471, 0.8471, 0.8471\], \[0.1451, 0.1412, 0.1333, ..., 0.8471, 0.8471, 0.8471\], \[0.1490, 0.1451, 0.1373, ..., 0.8431, 0.8431, 0.8431\], ..., \[0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549\], \[0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549\], \[0.8275, 0.8275, 0.8275, ..., 0.8549, 0.8549, 0.8549\]\], \[\[0.1686, 0.1647, 0.1569, ..., 0.4275, 0.4275, 0.4275\], \[0.1686, 0.1647, 0.1569, ..., 0.4275, 0.4275, 0.4275\], \[0.1686, 0.1647, 0.1569, ..., 0.4235, 0.4235, 0.4235\], ..., \[0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235\], \[0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235\], \[0.0078, 0.0078, 0.0078, ..., 0.4235, 0.4235, 0.4235\]\]\])  Compose()用法 数据需要时transforms类型，所以得到Compose(\[transforms参数1,transforms参数2,...\])    RandomCrop    关注输入和输出类型 多看官方文档 关注方法需要什么参数 不知道返回值的时候 \*print \*print(type()) \*debug DataLoader的使用 例： Dataloader(batch_size=4) Img0,target0 = dataset\[0
Img1,target1 = dataset[1]
Img2,target2 = dataset[2]
Img3,target3 = dataset[3]
Getitem():
Return img,target
神经网络的搭建nn.Module