Sweep (sandbox): src/main.py #95
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📖 Reading FileRunning sandbox for src/main.py. Current Code: Lines 1 to 48 in 7284908 🛠️ Executing SandboxSandbox logs
Updated Code: import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# Step 1: Load MNIST Data and Preprocess
transform = transforms.Compose(
[transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
)
trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
# Step 2: Define the PyTorch Model
class Net(nn.Module):
def __init__(self):
super().__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 = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return nn.functional.log_softmax(x, dim=1)
# Step 3: Train the Model
model = Net()
optimizer = optim.SGD(model.parameters(), lr=0.01)
criterion = nn.NLLLoss()
# Training loop
epochs = 3
for epoch in range(epochs):
for images, labels in trainloader:
optimizer.zero_grad()
output = model(images)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
torch.save(model.state_dict(), "mnist_model.pth")Diff: ---
+++
@@ -1,19 +1,19 @@
-from PIL import Image
+import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
+from PIL import Image
+from torch.utils.data import DataLoader
from torchvision import datasets, transforms
-from torch.utils.data import DataLoader
-import numpy as np
# Step 1: Load MNIST Data and Preprocess
-transform = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize((0.5,), (0.5,))
-])
+transform = transforms.Compose(
+ [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+)
-trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
+trainset = datasets.MNIST(".", download=True, train=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+
# Step 2: Define the PyTorch Model
class Net(nn.Module):
@@ -22,13 +22,14 @@
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 = nn.functional.relu(self.fc1(x))
x = nn.functional.relu(self.fc2(x))
x = self.fc3(x)
return nn.functional.log_softmax(x, dim=1)
+
# Step 3: Train the Model
model = Net()
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