Refactor training loop from script to class

README.md

@@ -1 +1,55 @@
-# evals
+# evals
+
+This project now includes a new class `MNISTTrainer` which is used to train a model on the MNIST dataset.
+
+## MNISTTrainer
+
+The `MNISTTrainer` class is defined in `src/main.py`. It includes methods for loading and preprocessing the MNIST dataset, defining the model architecture, and training the model.
+
+### Usage
+
+An instance of `MNISTTrainer` is created and then its methods are used to load the data, define the model, and train the model. Here is an example:
+
+```python
+# Create an instance of MNISTTrainer
+trainer = MNISTTrainer()
+
+# Load the data
+trainloader = trainer.load_data()
+
+# Define the model
+Net = trainer.define_model()
+model = Net(trainloader)
+
+# Train the model
+optimizer = optim.SGD(model.parameters(), lr=0.01)
+criterion = nn.NLLLoss()
+
+# Training loop
+epochs = 3
+for epoch in range(epochs):
+    for images, labels in model.trainloader:
+        optimizer.zero_grad()
+        output = model(images)
+        loss = criterion(output, labels)
+        loss.backward()
+        optimizer.step()
+```
+
+The trained model is then saved and can be loaded in `src/api.py` using the `MNISTTrainer` class in a similar way:
+
+```python
+# Create an instance of MNISTTrainer
+trainer = MNISTTrainer()
+
+# Load the data
+trainloader = trainer.load_data()
+
+# Define the model
+Net = trainer.define_model()
+model = Net(trainloader)
+
+# Load the model's state from the saved file
+model.load_state_dict(torch.load("mnist_model.pth"))
+model.eval()
+```

src/api.py

@@ -2,10 +2,19 @@
 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
 
-# Load the model
-model = Net()
+# Create an instance of MNISTTrainer
+trainer = MNISTTrainer()
+
+# Load the data
+trainloader = trainer.load_data()
+
+# Define the model
+Net = trainer.define_model()
+model = Net(trainloader)
+
+# Load the model's state from the saved file
 model.load_state_dict(torch.load("mnist_model.pth"))
 model.eval()
 

src/main.py

@@ -6,39 +6,54 @@
 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,))
-])
+class MNISTTrainer:
+    def __init__(self):
+        self.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)
+    def load_data(self):
+        trainset = datasets.MNIST('.', download=True, train=True, transform=self.transform)
+        trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
+        return trainloader
 
-# 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()
+    def define_model(self):
+        class Net(nn.Module):
+            def __init__(self, trainloader):
+                super().__init__()
+                self.trainloader = trainloader
+                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)
+
+        return Net
+
+# Create an instance of MNISTTrainer
+trainer = MNISTTrainer()
+
+# Load the data
+trainloader = trainer.load_data()
+
+# Define the model
+Net = trainer.define_model()
+model = Net(trainloader)
+
+# Train the model
 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:
+    for images, labels in model.trainloader:
         optimizer.zero_grad()
         output = model(images)
         loss = criterion(output, labels)