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Refactor training loop from script to class #21

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14 changes: 7 additions & 7 deletions src/api.py
Original file line number Diff line number Diff line change
Expand Up @@ -2,18 +2,18 @@
from PIL import Image
import torch
from torchvision import transforms
from main import Net # Importing Net class from main.py
from main import MNISTTrainer # Importing MNISTTrainer class from main.py

# Create an instance of the MNISTTrainer class
trainer = MNISTTrainer()

# Load the model
model = Net()
model = trainer.model
model.load_state_dict(torch.load("mnist_model.pth"))
model.eval()

# Transform used for preprocessing the image
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# Transform used for preprocessing the image is now inside the MNISTTrainer class
transform = trainer.transform

app = FastAPI()

Expand Down
48 changes: 24 additions & 24 deletions src/main.py
Original file line number Diff line number Diff line change
Expand Up @@ -6,16 +6,7 @@
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,))
])

trainset = datasets.MNIST('.', download=True, train=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)

# Step 2: Define the PyTorch Model
# Define the PyTorch Model
class Net(nn.Module):
def __init__(self):
super().__init__()
Expand All @@ -30,19 +21,28 @@ def forward(self, 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()
class MNISTTrainer:
def __init__(self):
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])

# 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()
def load_data(self):
"""Load and preprocess the MNIST data."""
trainset = datasets.MNIST('.', download=True, train=True, transform=self.transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
return trainloader

torch.save(model.state_dict(), "mnist_model.pth")
def train(self, model, criterion, optimizer):
"""Train the model using the provided criterion and optimizer."""
trainloader = self.load_data()
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")