二、pytorch_simple_fullynet

dataset is MNIST

# Imports
import torch
import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
import torch.nn.functional as F # All functions that don't have any parameters
from torch.utils.data import DataLoader # Gives easier dataset management and creates mini batches
import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
import torchvision.transforms as transforms # Transformations we can perform on our dataset


# Create Simple Fully Connected Neural Network
class NN(nn.Module):
    def __init__(self,input_size,num_classes):
        super(NN, self).__init__()

        self.fc1 = nn.Linear(input_size,50)
        self.fc2 = nn.Linear(50,num_classes)


    def forward(self,x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# model = NN(784,10)
# x = torch.randn(64,784)
# print(model(x).shape)

# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hyperparameters
input_size = 784 # 28*28
num_classes = 10
learning_rate = 1e-3
batch_size = 64
num_epochs = 1


# Load Data
train_dataset = datasets.MNIST("../",train=True,transform=transforms.ToTensor(),download=True)
test_dataset = datasets.MNIST("../",train=False,transform=transforms.ToTensor(),download=True)
train_loader = DataLoader(dataset=train_dataset,batch_size=batch_size,shuffle=True)
test_loader = DataLoader(dataset=test_dataset,batch_size=batch_size,shuffle=True)


# Initialize network
model = NN(input_size=input_size,num_classes=num_classes).to(device=device)


# Initialize network
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(params=model.parameters(),lr=learning_rate)


# Train Network
for epoch in range(num_epochs):
    for batch_idx,(data,targets) in enumerate(train_loader):
        # Get data to cuda if possible
        data = data.to(device)
        targets = targets.to(device)
        # print(data.shape)

        # Get to correct shape
        data = data.reshape(data.shape[0],-1)

        # forward
        scores = model(data)
        loss = criterion(scores,targets)

        # backward
        optimizer.zero_grad()
        loss.backward()

        # gradient descent or adam step
        optimizer.step()

# Check accuracy on training & test to see how good our model

def check_accuracy(loader,model):
    if loader.dataset.train:
        print("Checking accuracy on training data")
    else:
        print("Checking accuracy on testing data")

    num_correct = 0
    num_samples = 0
    model.eval()

    with torch.no_grad():
        for x,y in loader:
            x = x.to(device)
            y = y.to(device)
            x = x.reshape(x.shape[0],-1)

            scores = model(x)
            _, predictions = scores.max(1)
            num_correct += (predictions==y).sum()
            num_samples += predictions.size(0)
        print(f'Got {
      
      num_correct}/{
      
      num_samples} with accuracy {
      
      float(num_correct)/float(num_samples)*100:.2f}')

    model.train()


check_accuracy(train_loader,model)
check_accuracy(test_loader,model)