Pytorch Tutorial Notes

1. Input Pipeline

   # dataset => loader
   # torchvision.datasets => torch.utils.data.DataLoader

2. Train && Val Diff

   #train = true of false
   train_dataset = torchvision.datasets.MNIST(root='../data/',
                                              train=True,
                                              transform=transforms.ToTensor(),
                                              download=True)
   val_dataset = torchvision.datasets.MNIST(root='../data/',
                                            train=False,
                                            transform=transforms.ToTensor())

3. torch.max ‘s return : first is the max value,second is the index

4. 计算准确率

   correct += (predicted == labels).sum().item()

5. Output size of Conv

   $$\dfrac {W-F+2p}{S}+1$$

1. Summary.

    To summarize, the Conv Layer:
   

   • Accepts a volume of size

     $$W_1 \times H_1 \times D_1$$

   • Requires four hyperparameters:

     • Number of filters K,
     • their spatial extent F,
     • the stride S,
     • the amount of zero padding P.

   • Produces a volume of sizeW₂×H₂×D₂

     where:

     • $$W_2 = (W_1 - F + 2P)/S + 1$$
     • $$H_2 = (H_1 - F + 2P)/S + 1$$

     • D₂=K

   • With parameter sharing, it introduces

     $$F \cdot F \cdot D_1$$

     weights per filter, for a total of

     $$(F \cdot F \cdot D_1) \cdot K$$

     weights and K biases.

   • In the output volume, the d-th depth slice (of size W₂×H₂) is the result of performing a valid convolution of the d-th filter over the input volume with a stride of S, and then offset by d-th bias.