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MNIST_FNN
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import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(dataset = train_dataset, batch_size = 64, shuffle = True, num_workers = 2)
test_loader = DataLoader(dataset = test_dataset, batch_size = 64, shuffle = False, num_workers = 2)
class FNN(nn.Module):
def __init__(self):
super(FNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
model = FNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = 0.001)
num_epochs = 5
for epoch in range(num_epochs):
for image, labels in train_loader:
optimizer.zero_grad()
output = model(image)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
print(f'Epoch[{epoch + 1}/{num_epochs}, Loss: {loss.item():.4f}')
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
output = model(images)
_, predicted = torch.max(output.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Test Accuracy: {correct * 100/ total}%')
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=transform)
train_loader = DataLoader(dataset = train_dataset, batch_size = 64, shuffle = True, num_workers = 2)
test_loader = DataLoader(dataset = test_dataset, batch_size = 64, shuffle = False, num_workers = 2)
class FNN(nn.Module):
def __init__(self):
super(FNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 64)
self.fc3 = nn.Linear(64, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
model = FNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr = 0.001)
num_epochs = 5
for epoch in range(num_epochs):
for image, labels in train_loader:
optimizer.zero_grad()
output = model(image)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
print(f'Epoch[{epoch + 1}/{num_epochs}, Loss: {loss.item():.4f}')
correct = 0
total = 0
with torch.no_grad():
for images, labels in test_loader:
output = model(images)
_, predicted = torch.max(output.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Test Accuracy: {correct * 100/ total}%')