examples/digit-recognizer.py

import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, TensorDataset
from torchvision import transforms

# Load the data
train_df = pd.read_csv("./input/train.csv")

# Prepare the data
X = train_df.drop("label", axis=1).values.reshape(-1, 1, 28, 28).astype("float32")
y = train_df["label"].values
X /= 255.0  # Normalize to [0, 1]

# Split into training and validation sets
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert to PyTorch tensors
X_train_tensor = torch.tensor(X_train)
y_train_tensor = torch.tensor(y_train, dtype=torch.long)
X_val_tensor = torch.tensor(X_val)
y_val_tensor = torch.tensor(y_val, dtype=torch.long)

# Create datasets and dataloaders
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
val_dataset = TensorDataset(X_val_tensor, y_val_tensor)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)


# Define the CNN model
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3)
        self.fc1 = nn.Linear(64 * 5 * 5, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 64 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


# Initialize the model, loss function, and optimizer
model = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

# Train the model
num_epochs = 5
for epoch in range(num_epochs):
    model.train()
    for data, target in train_loader:
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

# Evaluate the model
model.eval()
correct = 0
with torch.no_grad():
    for data, target in val_loader:
        output = model(data)
        pred = output.argmax(dim=1, keepdim=True)
        correct += pred.eq(target.view_as(pred)).sum().item()

accuracy = correct / len(val_loader.dataset)
print(f"Validation Accuracy: {accuracy:.4f}")