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gperdrizet merged 2 commits into from
Jan 29, 2026
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Added lesson 29 materials #196

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gperdrizet Jan 27, 2026
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Added lesson 29 materials
gperdrizet Jan 29, 2026
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11 changes: 10 additions & 1 deletion notebooks/notebooks.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -257,4 +257,13 @@ units:
- name: "Activity"
file: "Lesson_28_activity.ipynb"
- name: "Activity solution"
file: "Lesson_28_activity_solution.ipynb"
file: "Lesson_28_activity_solution.ipynb"

- number: "29"
title: "PyTorch"
topics: "Basic deep learning with PyTorch"
notebooks:
- name: "In-class demo, part 1"
file: "Lesson_29_demo_part1.ipynb"
- name: "Activity part 1"
file: "Lesson_29_activity_part1.ipynb "
370 changes: 370 additions & 0 deletions notebooks/unit4/lesson_29/Lesson_29_activity_part1.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "50f51079",
"metadata": {},
"source": [
"# Lesson 29: PyTorch training loop activity\n",
"\n",
"In this activity, you will modify the basic PyTorch training loop from the lesson 29 demo to add:\n",
"\n",
"1. **Batching** - Process the training data in mini-batches instead of all at once\n",
"2. **Validation** - Track model performance on a held-out validation set during training\n",
"\n",
"## Notebook set-up\n",
"\n",
"### Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13c76bb6",
"metadata": {},
"outputs": [],
"source": [
"# Third party imports\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"\n",
"# Set random seeds for reproducibility\n",
"torch.manual_seed(315)\n",
"np.random.seed(315)\n",
"\n",
"# Check for GPU availability\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f'Using device: {device}')"
]
},
{
"cell_type": "markdown",
"id": "aaa50a53",
"metadata": {},
"source": [
"## 1. Load preprocessed data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9304299e",
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_pickle('https://gperdrizet.github.io/FSA_devops/assets/data/unit4/preprocessed_housing_data.pkl')\n",
"\n",
"training_df = data['training_df']\n",
"testing_df = data['testing_df']\n",
"features = data['features']\n",
"label = data['label']\n",
"\n",
"print(f'Training samples: {len(training_df)}')\n",
"print(f'Testing samples: {len(testing_df)}')\n",
"print(f'Features: {features}')\n",
"print(f'Label: {label}')"
]
},
{
"cell_type": "markdown",
"id": "3660a060",
"metadata": {},
"source": [
"## 2. Prepare PyTorch tensors and DataLoaders\n",
"\n",
"### Task 1: Add batching and validation split\n",
"\n",
"Currently, the code below creates tensors for training and testing. Your task is to:\n",
"\n",
"1. **Add imports** for `TensorDataset` and `DataLoader` from `torch.utils.data`\n",
"2. **Split training data** into train and validation sets (e.g., 80/20 split)\n",
"3. **Create DataLoaders** for batched training\n",
"\n",
"**Hints:**\n",
"- Use `torch.randperm()` to shuffle indices for the split\n",
"- Use `TensorDataset(X, y)` to combine feature and label tensors\n",
"- Use `DataLoader(dataset, batch_size=32, shuffle=True)` to create batches\n",
"- Create separate DataLoaders for training and validation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a7bdc99c",
"metadata": {},
"outputs": [],
"source": [
"# Convert dataframes to PyTorch tensors and move to device\n",
"X_train = torch.tensor(training_df[features].values, dtype=torch.float32).to(device)\n",
"y_train = torch.tensor(training_df[label].values, dtype=torch.float32).unsqueeze(1).to(device)\n",
"X_test = torch.tensor(testing_df[features].values, dtype=torch.float32).to(device)\n",
"y_test = torch.tensor(testing_df[label].values, dtype=torch.float32).unsqueeze(1).to(device)\n",
"\n",
"print(f'X_train shape: {X_train.shape}')\n",
"print(f'y_train shape: {y_train.shape}')\n",
"print(f'X_test shape: {X_test.shape}')\n",
"print(f'y_test shape: {y_test.shape}')"
]
},
{
"cell_type": "markdown",
"id": "219abc95",
"metadata": {},
"source": [
"## 3. Build model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfb7fbbe",
"metadata": {},
"outputs": [],
"source": [
"model = nn.Sequential(\n",
" nn.Linear(8, 64), # Fully connected layer (similar to tf.keras.layers.Dense)\n",
" nn.ReLU(),\n",
" nn.Dropout(0.2),\n",
" nn.Linear(64, 32),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.2),\n",
" nn.Linear(32, 1)\n",
").to(device)\n",
"\n",
"# Define loss function and optimizer\n",
"criterion = nn.MSELoss()\n",
"optimizer = optim.Adam(model.parameters(), lr=1e-2)\n",
"\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"id": "5823b5be",
"metadata": {},
"source": [
"## 4. Training function\n",
"\n",
"### Task 2: Update training loop for batching and validation\n",
"\n",
"The current training loop processes all training data at once. Your task is to modify this function to:\n",
"\n",
"1. **Accept DataLoaders** instead of raw tensors\n",
"2. **Iterate over batches** in an inner loop within each epoch\n",
"3. **Compute validation metrics** after each training epoch\n",
"\n",
"**Hints:**\n",
"- Change function signature to accept `train_loader` and `val_loader` instead of `X_train`, `y_train`\n",
"- Add an inner `for X_batch, y_batch in train_loader:` loop\n",
"- Accumulate loss across batches, then average for reporting\n",
"- Use `model.eval()` and `torch.no_grad()` for validation\n",
"- Track `val_loss` and `val_r2` in the history dictionary"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d821d6cb",
"metadata": {},
"outputs": [],
"source": [
"def train_model(\n",
" model: nn.Module,\n",
" X_train: torch.Tensor,\n",
" y_train: torch.Tensor,\n",
" criterion: nn.Module,\n",
" optimizer: optim.Optimizer,\n",
" epochs: int = 50,\n",
" print_every: int = 5\n",
") -> dict[str, list[float]]:\n",
" '''Basic training loop for PyTorch model.\n",
" \n",
" TODO: Modify this function to add:\n",
" 1. Batching - process data in mini-batches\n",
" 2. Validation - track performance on a validation set\n",
" '''\n",
" \n",
" history = {'loss': [], 'r2': []}\n",
" \n",
" for epoch in range(epochs):\n",
" # Set model to training mode\n",
" model.train()\n",
" \n",
" # Zero the gradients\n",
" optimizer.zero_grad()\n",
" \n",
" # Forward pass\n",
" predictions = model(X_train)\n",
" \n",
" # Calculate loss\n",
" loss = criterion(predictions, y_train)\n",
" \n",
" # Backward pass\n",
" loss.backward()\n",
" \n",
" # Update weights\n",
" optimizer.step()\n",
" \n",
" # Calculate R²\n",
" with torch.no_grad():\n",
" ss_res = torch.sum((y_train - predictions) ** 2)\n",
" ss_tot = torch.sum((y_train - torch.mean(y_train)) ** 2)\n",
" r2 = 1 - (ss_res / ss_tot)\n",
" \n",
" # Record metrics\n",
" history['loss'].append(loss.item())\n",
" history['r2'].append(r2.item())\n",
" \n",
" # Print progress\n",
" if (epoch + 1) % print_every == 0 or epoch == 0:\n",
" print(f'Epoch {epoch+1}/{epochs} - loss: {loss.item():.4f} - R²: {r2.item():.4f}')\n",
" \n",
" print('\\nTraining complete.')\n",
" return history"
]
},
{
"cell_type": "markdown",
"id": "bf715640",
"metadata": {},
"source": [
"## 5. Train model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dee08328",
"metadata": {},
"outputs": [],
"source": [
"history = train_model(\n",
" model=model,\n",
" X_train=X_train,\n",
" y_train=y_train,\n",
" criterion=criterion,\n",
" optimizer=optimizer,\n",
" epochs=100,\n",
" print_every=10\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d3b088b5",
"metadata": {},
"source": [
"## 6. Learning curves\n",
"\n",
"**Note:** Once you add validation, update these plots to show both training and validation metrics."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abf68740",
"metadata": {},
"outputs": [],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
"\n",
"axes[0].set_title('Training loss')\n",
"axes[0].plot(history['loss'])\n",
"axes[0].set_xlabel('Epoch')\n",
"axes[0].set_ylabel('Loss (MSE)')\n",
"\n",
"axes[1].set_title('Training R²')\n",
"axes[1].plot(history['r2'])\n",
"axes[1].set_xlabel('Epoch')\n",
"axes[1].set_ylabel('R²')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ffcd44a5",
"metadata": {},
"source": [
"## 7. Test set evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b6adfed",
"metadata": {},
"outputs": [],
"source": [
"# Set model to evaluation mode\n",
"model.eval()\n",
"\n",
"# Make predictions (no gradient calculation needed)\n",
"with torch.no_grad():\n",
" predictions = model(X_test).cpu().numpy().flatten()\n",
"\n",
"# Calculate R²\n",
"ss_res = np.sum((testing_df[label].values - predictions) ** 2)\n",
"ss_tot = np.sum((testing_df[label].values - np.mean(testing_df[label].values)) ** 2)\n",
"rsquared = 1 - (ss_res / ss_tot)\n",
"\n",
"print(f'Model R² on test set: {rsquared:.4f}')"
]
},
{
"cell_type": "markdown",
"id": "0bdf9c3d",
"metadata": {},
"source": [
"## 8. Performance analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "04a3a765",
"metadata": {},
"outputs": [],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
"\n",
"axes[0].set_title('Model predictions')\n",
"axes[0].scatter(\n",
" testing_df[label], predictions,\n",
" c='black', s=0.5, alpha=0.5\n",
")\n",
"axes[0].plot(\n",
" [testing_df[label].min(), testing_df[label].max()],\n",
" [testing_df[label].min(), testing_df[label].max()],\n",
" color='red', linestyle='--'\n",
")\n",
"axes[0].set_xlabel('True values (standardized)')\n",
"axes[0].set_ylabel('Predicted values (standardized)')\n",
"\n",
"axes[1].set_title('Residuals vs predicted values')\n",
"axes[1].scatter(\n",
" predictions, testing_df[label] - predictions,\n",
" c='black', s=0.5, alpha=0.5\n",
")\n",
"axes[1].axhline(0, color='red', linestyle='--')\n",
"axes[1].set_xlabel('Predicted values (standardized)')\n",
"axes[1].set_ylabel('Residuals (standardized)')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
}
],
"metadata": {
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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