{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"1cf71a12"},"source":["**FILL IN YOUR NAME HERE**: [LASTNAME] [FIRSTNAME]"]},{"cell_type":"markdown","metadata":{"id":"65323689"},"source":["# Machine Learning Midterm Exam\n","Spring 2026 - 30 March 2026"]},{"cell_type":"markdown","metadata":{"id":"7207e6ad"},"source":["---\n","\n","## Exam Rules and Conduct\n","\n","- You must use **Colab** with **all AI assistance turned off**\n","\n","### Allowed\n","- Built-in IDE documentation\n","- Standard autocomplete (e.g., showing available functions when typing `torch.nn`)\n","- Use of `help()` and `dir()` functions\n","- Locally saved files (including course materials, assignments, PDFs, summaries, books, etc.)\n","- PDF editor/viewer\n","- A locally installed dictionary app (English language with no translations)\n","\n","### Not Allowed\n","- **No AI coding assistance**: no chatbots/LLMs, code generation, or explanation tools\n","- **No external help**: no internet, communication apps, translation apps, or additional browser tabs\n","\n","---\n","\n","## Timing\n","\n","- You have **5 minutes before** the start to download the file and **5 minutes after** the end to upload it\n","- **Late submissions** will not be accepted without a documented reason\n","\n","---\n","\n","## Answer Format\n","\n","- Answer directly below each question\n","  - Use **Markdown cells** for text (in English)\n","  - Use **code cells** for programming\n","- **0 points** for incorrect or incomplete answers\n","  - Partial credit only if the core idea is correct **and** instructions are followed\n","  - Ignoring instructions = **0 points**, regardless of effort\n","- Be clear and concise—extra detail doesn’t earn more points\n","\n","---\n","\n","## Coding Guidelines\n","\n","- Use **only packages covered in the course and assignments**\n","- Follow dataset instructions. Using a different dataset than instructed will result in **0 points** for that question\n","- Code that **does not run** = **50% deduction**\n","- Code quality (correctness, clarity, efficiency) counts\n","\n","---\n","\n","## Academic Integrity\n","\n","Violations (e.g., using AI tools or outside help) will result in:\n","- A **failing grade (1.0)**\n","- A **disciplinary report** to the Faculty of Science and Medicine\n","\n","---\n","\n","## **Good luck! 🍀**\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b952307b"},"outputs":[],"source":["points = 64"]},{"cell_type":"markdown","source":["# 1. Theory (20 points)\n","Complete the quiz provided on Moodle.\n","\n","For each topic, identify which statements are TRUE ✅ and which are FALSE ❌. Each question may have 1, 2, 3, or 4 correct answers.\n","\n","- 4 correct answers: 2 points\n","- 3 correct answers: 1 point\n","- 2 or fewer correct answers: 0 points"],"metadata":{"id":"rzBG4UGk8Nq1"}},{"cell_type":"markdown","source":["---\n","# 2. Hands-On (20 points)\n","You may use Python as a calculator but all calculations must be shown in $\\LaTeX$"],"metadata":{"id":"m03ByxIZ8ff3"}},{"cell_type":"markdown","source":["## 2.1 **(2pts)** Which pair of vectors are orthogonal/perpendicular?\n","\n","Let:\n","\n","$\n","\\mathbf{a}=\n","\\begin{pmatrix}\n","5\\\\\n","2\n","\\end{pmatrix},\n","\\quad\n","\\mathbf{b}=\n","\\begin{pmatrix}\n","4\\\\\n","-10\n","\\end{pmatrix},\n","\\quad\n","\\mathbf{c}=\n","\\begin{pmatrix}\n","4\\\\\n","-12\n","\\end{pmatrix},\n","\\quad\n","\\mathbf{d}=\n","\\begin{pmatrix}\n","1\\\\\n","3\n","\\end{pmatrix}\n","$"],"metadata":{"id":"V4nn34V09shq"}},{"cell_type":"markdown","source":["2.1.1 **(1pt)** **Perpendicular vectors:** $\\mathbf{a}$ & $\\mathbf{b}$"],"metadata":{"id":"RulRavZXEl_u"}},{"cell_type":"markdown","source":["2.1.2 **(1pt)** **Calculation with $\\LaTeX$**:\n","\n","$\n","\\begin{pmatrix}\n","5\\\\\n","2\n","\\end{pmatrix}\n","\\cdot\n","\\begin{pmatrix}\n","4\\\\\n","-10\n","\\end{pmatrix} = 20 - 20 = 0\n","$"],"metadata":{"id":"VF3VufMbRjiB"}},{"cell_type":"markdown","source":["## 2.2 **(5pts)** Give an example of two 2D vectors whose cosine similarity is `-1`.\n","- Each vector should contain only the values `1` and `-1`.\n","- Then, show your work by computing the dot product, the magnitude of each vector, and the cosine similarity to verify that the result is `-1`."],"metadata":{"id":"HQR1a5rALeRv"}},{"cell_type":"markdown","source":["2.2.1 **(1pt)** Two vectors:\n","\n","$\n","\\mathbf{u}=\n","\\begin{pmatrix}\n","1\\\\\n","1\n","\\end{pmatrix},\n","\\quad\n","\\mathbf{v}=\n","\\begin{pmatrix}\n","-1\\\\\n","-1\n","\\end{pmatrix}\n","$"],"metadata":{"id":"zPzReiRjLyhE"}},{"cell_type":"markdown","source":["2.2.2 **(4pt)** Cosine Similarity Calculation with $\\LaTeX$:\n","\n","$\n","\\begin{aligned}\n","\\mathbf{u} \\cdot \\mathbf{v} &=\n","\\begin{pmatrix}\n","1 \\\\\n","1\n","\\end{pmatrix} \\cdot\n","\\begin{pmatrix}\n","-1 \\\\\n","-1\n","\\end{pmatrix}\n","= 1 \\cdot -1 + 1 \\cdot -1 = -1 -1 = -2\n","\\end{aligned}\n","$\n","\n","$\\begin{aligned}\n","\\parallel \\mathbf{u} \\parallel &= \\sqrt{1^2 + 1^2} = \\sqrt{2}\n","\\\\\n","\\parallel \\mathbf{v} \\parallel &= \\sqrt{(-1)^2 + (-1)^2} = \\sqrt{2}\n","\\end{aligned}\n","$\n","\n","$\\cos(\\theta) = \\frac{-2}{\\sqrt{2} \\cdot \\sqrt{2}} = \\frac{-2}{2} = -1\n","$"],"metadata":{"id":"TaqIRv18RgyF"}},{"cell_type":"markdown","source":["## 2.3 **(6pts)** Compute one step of gradient descent.\n","\n","Let: $f(x,y)=2x^2+y^2$\n","\n","Suppose we start at $(x_0, y_0) = (2, 1)$ with learning rate $\\eta = 0.1$"],"metadata":{"id":"cKmj8X7SQW01"}},{"cell_type":"markdown","source":["2.3.1. **(1pt)** Determine the partial derivatives\n","\n","Partial derivatives: $\\frac{\\partial f}{\\partial x} = 4x, \\quad \\frac{\\partial f}{\\partial y} = 2y$"],"metadata":{"id":"QWpoB-I7RJV8"}},{"cell_type":"markdown","source":["2.3.2 **(1pt)** Write down the gradient at $(2, 1)$\n","\n","So, $\\nabla f(2,1) =\n","\\begin{pmatrix}\n","4 \\cdot 2 \\\\\n","2 \\cdot 1\n","\\end{pmatrix} =\n","\\begin{pmatrix}\n","8 \\\\\n","2\n","\\end{pmatrix}\n","$\n"],"metadata":{"id":"k9-5VKhuSxEu"}},{"cell_type":"markdown","source":["2.3.3 **(1pt)** Compute one step of gradient descent\n","\n","$(x_1,y_1) = (2,1) - 0.1 \\cdot (8,2) = (1.2,0.8)$\n"],"metadata":{"id":"PARwKrM1R7m2"}},{"cell_type":"markdown","source":["2.3.4 **(2pts)** Compute the function value before and after the step (plug the original and first step $(x,y)$ into the function)\n","\n","$f(2,1) = 2(2)^2 + (1)^2 = 8 + 1 = 9$\n","\n","$f(1.2,0.8) = 2(1.2)^2 + (0.8)^2 = 2.88 + (0.64) = 3.52$"],"metadata":{"id":"RAgdGg9RR8lt"}},{"cell_type":"markdown","source":["2.3.5 **(1pt)** Interpret the change in function values by reporting the initial value, the new value after one gradient descent step, and the direction of the change\n","\n","The function value decreases from $9$ to $3.52$, meaning the step moved downhill"],"metadata":{"id":"-G7bEkbcR929"}},{"cell_type":"markdown","metadata":{"id":"9c2026b2"},"source":["## 2.4 **(6pts)** Activate this single-layer Perceptron with a LeakyReLU activation function.\n","You are working with a single-layer perceptron that has:\n","- 3 input units (features)\n","- 1 output unit (neuron)\n","\n","These are your parameters:\n","- $x=[2.0, -2.0, 1.0]$\n","- $w=[1.0, -1.0, 1.0]$\n","- $b=-2$\n","- $\\alpha$ = 0.01\n"]},{"cell_type":"markdown","metadata":{"id":"744c9753"},"source":["2.4.1 **(2pts)** Write the formula for the **weighted sum** of inputs.\n","\n","$z = \\sum_{i=1}^n w_ix_i + b$"]},{"cell_type":"markdown","metadata":{"id":"8b178fd1"},"source":["2.4.2 **(2pts)** Show your calculations and give the final answer rounded to 1 decimal point.\n","\n","$\n","\\begin{aligned}\n","z &= (1*2) + (-1*-2) + (1*1) - 2 \\\\\n","z &= 2 + 2 + 1 -2 \\\\\n","z &= 3.0\n","\\end{aligned}$"]},{"cell_type":"markdown","metadata":{"id":"d867c06e"},"source":["2.4.3 **(2pts)** Apply the **LeakyReLU** activation function to determine the final output of the perceptron.\n","\n","Expected answer format $\\sigma(x.x) = x.x$\n","\n","$\\sigma(3.0) = 3.0$"]},{"cell_type":"markdown","source":["# 3. Coding (24 points)"],"metadata":{"id":"wc9-2sKe8wJl"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"B2DEJ7KInHgV","executionInfo":{"status":"ok","timestamp":1774774775167,"user_tz":-120,"elapsed":13031,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}}},"outputs":[],"source":["### DO NOT EDIT THIS CELL ###\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torchvision.transforms as transforms\n","import torchvision\n","from torch.utils.data import Subset, DataLoader, TensorDataset\n","\n","import numpy as np\n","import random\n","import matplotlib.pyplot as plt\n","from tqdm import tqdm\n","\n","\n","# Set random seed\n","def set_seed(seed):\n","    random.seed(seed)\n","    np.random.seed(seed)\n","    torch.manual_seed(seed)\n","    torch.cuda.manual_seed(seed)\n","    torch.cuda.manual_seed_all(seed)\n","    torch.backends.cudnn.deterministic = True\n","    torch.backends.cudnn.benchmark = False\n","    torch.use_deterministic_algorithms(True)\n","\n","set_seed(0)\n","\n","g = torch.Generator().manual_seed(42)\n","\n","device = torch.device(\"cpu\")\n","\n","#############################"]},{"cell_type":"markdown","metadata":{"id":"8cf4182b"},"source":["## 3.1 **(6pts)** Debug the following Neural Network initialization.\n","The following code defines and trains a simple neural network with 3 linear layers, an `__init__` and `forward` method. However, **it contains six syntax errors** that cause the code to crash during training. There are no structural problems so please **do not edit the general structure**.\n","\n","Your task is to:\n","\n","- Identify all 6 mistakes\n","- Fix each one and briefly explain why the change was necessary as a code comment\n","\n","Once the script is error-free, the script will print `✅ Success!`."]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":30,"status":"ok","timestamp":1774775142452,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"},"user_tz":-120},"id":"ctFJSqRUTwjW","outputId":"1f05731d-9565-48fe-85f1-d032be495767"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["NeuralNetworkBroken(\n","  (model): Sequential(\n","    (0): Linear(in_features=10, out_features=8, bias=True)\n","    (1): Linear(in_features=8, out_features=8, bias=True)\n","    (2): Linear(in_features=8, out_features=2, bias=True)\n","  )\n",")"]},"metadata":{},"execution_count":22}],"source":["class NeuralNetworkBroken(nn.Module):\n","    def __init__(self, input_dim, hidden_dim, output_dim):\n","        super().__init__()\n","        self.model = nn.Sequential(\n","            nn.Linear(input_dim, hidden_dim), # 2) input_din -> input_dim, 3) add hidden_dim\n","            nn.Linear(hidden_dim, hidden_dim), # 1) add comma, 6) correct output dimension\n","            nn.Linear(hidden_dim, output_dim),\n","        )\n","\n","    def forward(self, x):\n","        return self.model(x) # 5) change from sigmoid -> x\n","\n","device = device\n","model = NeuralNetworkBroken(input_dim=10, hidden_dim=8, output_dim=2)\n","model.to(device) # 4) move to device"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":37,"status":"ok","timestamp":1774775143726,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"},"user_tz":-120},"id":"9e49b08a","outputId":"38acf34f-227d-467d-f71c-6dc0c72efa1c"},"outputs":[{"output_type":"stream","name":"stdout","text":["✅ Success!\n"]}],"source":["### DO NOT EDIT THIS CELL ###\n","\n","X = torch.randn(32, 10)\n","y = torch.randint(0, 2, (32,))\n","\n","dataset = TensorDataset(X, y)\n","dataloader = DataLoader(dataset, batch_size=32)\n","\n","# Model setup\n","loss_fn = nn.CrossEntropyLoss()\n","optimizer = torch.optim.Adam(model.parameters())\n","\n","# Minimal training loop to trigger crash\n","model.train()\n","\n","for inputs, targets in dataloader:\n","    outputs = model(inputs)\n","    loss = loss_fn(outputs, targets)\n","    break\n","\n","print('✅ Success!')\n","\n","#############################"]},{"cell_type":"markdown","source":["## 3.2 **(12pts)** Implement an MLP for Random Data in PyTorch.\n","You are given training and validation datasets for a 10-class image classification problem. Each sample is a randomly generated grayscale image of shape 1x28x28 and a randomly assigned label in `{0,1,...,9}`."],"metadata":{"id":"mf7OYIyzZZiR"}},{"cell_type":"code","source":["## DO NOT EDIT THIS CELL\n","# number of samples\n","n_train = 100\n","n_val = 100\n","\n","# define random training images (28x28) and labels (between 0-9)\n","X_train = torch.randn(n_train, 1, 28, 28)\n","y_train = torch.randint(0, 10, (n_train,))\n","\n","# define random validation images (28x28) and labels (between 0-9)\n","X_val = torch.randn(n_val, 1, 28, 28)\n","y_val = torch.randint(0, 10, (n_val,))\n","\n","# create datasets\n","train_dataset = TensorDataset(X_train, y_train)\n","val_dataset = TensorDataset(X_train, y_train)\n","\n","# create data loaders\n","train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=10, shuffle=False)"],"metadata":{"id":"Yh0aQ_xeat-6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3.2.1 **(4 pts)** Implement the following MLP in PyTorch:\n","- Use a sequential model with:\n","  - Flatten\n","  - Linear(28x28 → 128), ReLU\n","  - Linear(128 → 64), ReLU\n","  - Linear(64 → 10)\n","- Define the `forward` function"],"metadata":{"id":"jQzWKbzsoklV"}},{"cell_type":"code","source":["class MLP(nn.Module):\n","  def __init__(self):\n","      super().__init__()\n","\n","      self.model = nn.Sequential(\n","          nn.Flatten(),\n","          nn.Linear(28*28, 128),\n","          nn.ReLU(),\n","          nn.Linear(128, 64),\n","          nn.ReLU(),\n","          nn.Linear(64, 10)\n","      )\n","\n","  def forward(self, x):\n","    return self.model(x)"],"metadata":{"id":"RQDPF5N3aQrJ"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3.2.2 **(3pts)** Instantiate the model, move it to the `device`, and print the model."],"metadata":{"id":"mutVol-DpWN3"}},{"cell_type":"code","source":["model = MLP()\n","model.to(device)\n","\n","print(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"N0A2nJvzaYiw","executionInfo":{"status":"ok","timestamp":1774458151343,"user_tz":-60,"elapsed":14,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"93fd3034-42b2-464d-e146-22378a3538ce"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MLP(\n","  (model): Sequential(\n","    (0): Flatten(start_dim=1, end_dim=-1)\n","    (1): Linear(in_features=784, out_features=128, bias=True)\n","    (2): ReLU()\n","    (3): Linear(in_features=128, out_features=64, bias=True)\n","    (4): ReLU()\n","    (5): Linear(in_features=64, out_features=10, bias=True)\n","  )\n",")\n"]}]},{"cell_type":"code","source":["### GRADING TOOL ###\n","# paste printed output here\n","stu_model=\"\"\"\n","CNNModel(\n","  (features): Sequential(\n","    (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n","    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (2): ReLU()\n","    (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","    (4): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2))\n","    (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (6): ReLU()\n","    (7): Flatten(start_dim=1, end_dim=-1)\n","  )\n","  (classifier): Sequential(\n","    (0): LazyLinear(in_features=0, out_features=128, bias=True)\n","    (1): ReLU()\n","    (2): Dropout(p=0.5, inplace=False)\n","    (3): Linear(in_features=128, out_features=10, bias=True)\n","  )\n",")\n","\"\"\"\n","\n","# reference to compare to\n","ref_model = \"\"\"\n","MLP(\n","  (model): Sequential(\n","    (0): Flatten(start_dim=1, end_dim=-1)\n","    (1): Linear(in_features=784, out_features=128, bias=True)\n","    (2): ReLU()\n","    (3): Linear(in_features=128, out_features=64, bias=True)\n","    (4): ReLU()\n","    (5): Linear(in_features=64, out_features=10, bias=True)\n","  )\n",")\n","\"\"\"\n","\n","def compare_model_strings(stu, ref):\n","    if ref.strip() == stu.strip():\n","        print('✅ Correct')\n","    else:\n","        print('❌ Incorrect')\n","        # Optional: show the first point of difference\n","        import difflib\n","        diff = difflib.unified_diff(\n","            ref.strip().splitlines(),\n","            stu.strip().splitlines(),\n","            fromfile='Reference',\n","            tofile='Student',\n","            lineterm=''\n","        )\n","        print(\"\\n\".join(diff))\n","\n","# Run the comparison\n","compare_model_strings(stu_model, ref_model)"],"metadata":{"id":"VGOjBqMX_y0M"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3.2.3 **(4pts)** Define the hyperparameters.\n","- Use:\n","    - The cross entropy loss function\n","    - `Adam` optimizer with learning rate `0.001`\n","    - 20 training epochs"],"metadata":{"id":"mnQ1GW90ppQU"}},{"cell_type":"code","source":["criterion = nn.CrossEntropyLoss()\n","optimizer = optim.Adam(model.parameters(), lr=0.001)\n","n_epochs = 20"],"metadata":{"id":"jtQU-CYJfWR9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3.2.4 **(1pt)** Train, plot, and evaluate the model."],"metadata":{"id":"OsHAWwZTqJ9x"}},{"cell_type":"code","source":["### DO NOT EDIT THIS CELL ###\n","\n","def train_epoch(model, train_dataloader, optimizer, loss_fn):\n","    losses = []\n","    correct_predictions = 0\n","    # Iterate mini batches over training dataset\n","    for features, labels in tqdm(train_dataloader):\n","        features = features.to(device)\n","        labels = labels.to(device)\n","        # Run predictions\n","        output = model(features)\n","        # Set gradients to zero\n","        optimizer.zero_grad()\n","        # Compute loss\n","        loss = loss_fn(output, labels)\n","        # Backpropagate (compute gradients)\n","        loss.backward()\n","        # Make an optimization step (update parameters)\n","        optimizer.step()\n","        # Log metrics\n","        losses.append(loss.item())\n","        predicted_labels = output.argmax(dim=1)\n","        correct_predictions += (predicted_labels == labels).sum().item()\n","    accuracy = 100.0 * correct_predictions / len(train_dataloader.dataset)\n","    # Return loss values for each iteration and accuracy\n","    mean_loss = np.array(losses).mean()\n","    return mean_loss, accuracy\n","\n","def evaluate(model, dataloader, loss_fn):\n","    losses = []\n","    correct_predictions = 0\n","    with torch.no_grad():\n","        for features, labels in dataloader:\n","            features = features.to(device)\n","            labels = labels.to(device)\n","            # Run predictions\n","            output = model(features)\n","            # Compute loss\n","            loss = loss_fn(output, labels)\n","            # Save metrics\n","            predicted_labels = output.argmax(dim=1)\n","            correct_predictions += (predicted_labels == labels).sum().item()\n","            losses.append(loss.item())\n","    mean_loss = np.array(losses).mean()\n","    accuracy = 100.0 * correct_predictions / len(dataloader.dataset)\n","    # Return mean loss and accuracy\n","    return mean_loss, accuracy\n","\n","def train(model, train_dataloader, val_dataloader, optimizer, n_epochs, loss_fn):\n","    # We will monitor loss functions as the training progresses\n","    train_losses = []\n","    val_losses = []\n","    train_accuracies = []\n","    val_accuracies = []\n","\n","    for epoch in range(n_epochs):\n","        model.train()\n","        train_loss, train_accuracy = train_epoch(model, train_dataloader, optimizer, loss_fn)\n","        model.eval()\n","        val_loss, val_accuracy = evaluate(model, val_dataloader, loss_fn)\n","        train_losses.append(train_loss)\n","        val_losses.append(val_loss)\n","        train_accuracies.append(train_accuracy)\n","        val_accuracies.append(val_accuracy)\n","        print('Epoch {}/{}: train_loss: {:.4f}, train_accuracy: {:.4f}, val_loss: {:.4f}, val_accuracy: {:.4f}'.format(epoch+1, n_epochs,\n","                                                                                                      train_losses[-1],\n","                                                                                                      train_accuracies[-1],\n","                                                                                                      val_losses[-1],\n","                                                                                                      val_accuracies[-1]))\n","    return train_losses, val_losses, train_accuracies, val_accuracies\n","\n","def plot(train_losses, val_losses, train_accuracies, val_accuracies, title):\n","    plt.figure()\n","    plt.plot(np.arange(len(train_losses)), train_losses)\n","    plt.plot(np.arange(len(val_losses)), val_losses)\n","    plt.legend(['train_loss', 'val_loss'])\n","    plt.xlabel('epoch')\n","    plt.xticks(np.arange(len(train_losses)), np.arange(1, len(train_losses)+1))\n","    plt.ylabel('loss value')\n","    plt.title('{}: Train/val loss'.format(title));\n","\n","    plt.figure()\n","    plt.plot(np.arange(len(train_accuracies)), train_accuracies)\n","    plt.plot(np.arange(len(val_accuracies)), val_accuracies)\n","    plt.legend(['train_acc', 'val_acc'])\n","    plt.xlabel('epoch')\n","    plt.xticks(np.arange(len(train_losses)), np.arange(1, len(train_losses)+1))\n","    plt.ylabel('accuracy')\n","    plt.title('{}: Train/val accuracy'.format(title));\n","\n"],"metadata":{"id":"ykvLWhdOas7F"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# train model\n","train_losses, val_losses, train_acc, val_acc = train(model, train_loader, val_loader, optimizer, n_epochs, criterion)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eKHQbaJDa3vj","executionInfo":{"status":"ok","timestamp":1774458151907,"user_tz":-60,"elapsed":361,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"49e2e3e4-6ead-4cac-f837-fd1b1d603d0b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 48.54it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20: train_loss: 2.3117, train_accuracy: 10.0000, val_loss: 2.0470, val_accuracy: 55.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 361.37it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 2/20: train_loss: 1.9682, train_accuracy: 61.0000, val_loss: 1.7530, val_accuracy: 83.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 527.82it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 3/20: train_loss: 1.6262, train_accuracy: 83.0000, val_loss: 1.3396, val_accuracy: 92.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 481.36it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 4/20: train_loss: 1.1762, train_accuracy: 95.0000, val_loss: 0.8571, val_accuracy: 99.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 560.92it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 5/20: train_loss: 0.7054, train_accuracy: 99.0000, val_loss: 0.4589, val_accuracy: 99.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 547.37it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 6/20: train_loss: 0.3450, train_accuracy: 100.0000, val_loss: 0.2015, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 512.14it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 7/20: train_loss: 0.1437, train_accuracy: 100.0000, val_loss: 0.0839, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 501.06it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 8/20: train_loss: 0.0645, train_accuracy: 100.0000, val_loss: 0.0363, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 498.08it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 9/20: train_loss: 0.0279, train_accuracy: 100.0000, val_loss: 0.0191, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 444.17it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 10/20: train_loss: 0.0159, train_accuracy: 100.0000, val_loss: 0.0121, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 526.41it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 11/20: train_loss: 0.0109, train_accuracy: 100.0000, val_loss: 0.0090, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 550.59it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 12/20: train_loss: 0.0083, train_accuracy: 100.0000, val_loss: 0.0073, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 573.20it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 13/20: train_loss: 0.0069, train_accuracy: 100.0000, val_loss: 0.0062, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 556.41it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 14/20: train_loss: 0.0059, train_accuracy: 100.0000, val_loss: 0.0053, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 569.38it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 15/20: train_loss: 0.0051, train_accuracy: 100.0000, val_loss: 0.0047, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 521.82it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 16/20: train_loss: 0.0045, train_accuracy: 100.0000, val_loss: 0.0042, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 603.51it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 17/20: train_loss: 0.0040, train_accuracy: 100.0000, val_loss: 0.0037, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 515.83it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 18/20: train_loss: 0.0036, train_accuracy: 100.0000, val_loss: 0.0033, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 449.32it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 19/20: train_loss: 0.0032, train_accuracy: 100.0000, val_loss: 0.0030, val_accuracy: 100.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 431.87it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 20/20: train_loss: 0.0029, train_accuracy: 100.0000, val_loss: 0.0028, val_accuracy: 100.0000\n"]}]},{"cell_type":"code","source":["# visualize results\n","plot(train_losses, val_losses, train_acc, val_acc, title='MLP')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":927},"id":"wsH1dPEpbLYz","executionInfo":{"status":"ok","timestamp":1774458152463,"user_tz":-60,"elapsed":552,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"a61522f3-ff67-4de1-8c22-5608f4555f6e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# evaluate on last model\n","val_loss, val_accuracy = evaluate(model, val_loader, criterion)\n","print('MLP. Validation loss: {:.2f}, validation accuracy: {:.2f}'.format(val_loss, val_accuracy))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GREFIdABqxX0","executionInfo":{"status":"ok","timestamp":1774458152577,"user_tz":-60,"elapsed":36,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"c2df92bc-a82a-44aa-ac9f-72dc12613b46"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MLP. Validation loss: 0.00, validation accuracy: 100.00\n"]}]},{"cell_type":"markdown","source":["## 3.3 **(6pts)** Interpret, Diagnose, and Fix the above results\n","Keep your answers as short as possible (one-word answers are acceptable; maximum: one sentence per question).\n","\n","If you were unable to complete the previous question, use the provided loss and accuracy curves below for your interpretation instead.\n","\n","![loss.png](data:image/png;base64,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)\n","\n","![acc.png](data:image/png;base64,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)"],"metadata":{"id":"bMw53NCzrcKL"}},{"cell_type":"markdown","source":["3.3.1 **(1pt)** What validation accuracy would you expect by chance for a 10-class problem?\n","\n","~10%\n"],"metadata":{"id":"0Z5xGWmwtvnO"}},{"cell_type":"markdown","source":["3.3.2 **(1pt)** Is your model overfitting, underfitting, or generalizing well?\n","\n","Overfitting\n"],"metadata":{"id":"YmJwbQ9SvwlN"}},{"cell_type":"markdown","source":["3.3.3 **(1pt)** If you believe the results are suspicious, what could be the problem?\n","\n","Data leakage, the validation dataset was created using the training data."],"metadata":{"id":"lNSfhRiwvzBr"}},{"cell_type":"markdown","source":["3.3.4 **(1pt)** Fix the issue by copying the problematic code from above into the cell below and correcting it there. Add a comment indicating exactly where you made the change."],"metadata":{"id":"RY7NT7Rdvjw5"}},{"cell_type":"code","source":["# number of samples\n","n_train = 100\n","n_val = 100\n","\n","# define random training images (28x28) and labels (between 0-9)\n","X_train = torch.randn(n_train, 1, 28, 28)\n","y_train = torch.randint(0, 10, (n_train,))\n","\n","# define random validation images (28x28) and labels (between 0-9)\n","X_val = torch.randn(n_val, 1, 28, 28)\n","y_val = torch.randint(0, 10, (n_val,))\n","\n","# create datasets\n","train_dataset = TensorDataset(X_train, y_train)\n","val_dataset = TensorDataset(X_val, y_val) # data leakage fixed!\n","\n","# create data loaders\n","train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True)\n","val_loader = DataLoader(val_dataset, batch_size=10, shuffle=False)"],"metadata":{"id":"tB3M1uwlvlzw"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["3.3.5 **(1pt)** Retrain, plot, and evaluate the model after the fix using the same model and hyperparameters."],"metadata":{"id":"ziKqqQdVvmGv"}},{"cell_type":"code","source":["# train model\n","train_losses, val_losses, train_acc, val_acc = train(model, train_loader, val_loader, optimizer, n_epochs, criterion)\n","\n","# visualize results\n","plot(train_losses, val_losses, train_acc, val_acc, title='MLP')"],"metadata":{"id":"FUwrzjweweQT","executionInfo":{"status":"ok","timestamp":1774458199858,"user_tz":-60,"elapsed":1394,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"d63f9e48-9674-42df-d636-672290ffde4b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 131.55it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/20: train_loss: 0.0008, train_accuracy: 100.0000, val_loss: 2.9849, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 189.46it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 2/20: train_loss: 0.0008, train_accuracy: 100.0000, val_loss: 2.9878, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 191.51it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 3/20: train_loss: 0.0007, train_accuracy: 100.0000, val_loss: 2.9906, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 247.30it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 4/20: train_loss: 0.0007, train_accuracy: 100.0000, val_loss: 2.9933, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 314.87it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 5/20: train_loss: 0.0007, train_accuracy: 100.0000, val_loss: 2.9960, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 253.38it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 6/20: train_loss: 0.0007, train_accuracy: 100.0000, val_loss: 2.9987, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 231.38it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 7/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0012, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 315.04it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 8/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0037, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 351.10it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 9/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0063, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 527.42it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 10/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0089, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 521.58it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 11/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0113, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 525.58it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 12/20: train_loss: 0.0006, train_accuracy: 100.0000, val_loss: 3.0139, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 498.42it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 13/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0164, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 512.28it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 14/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0188, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 508.19it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 15/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0211, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 511.06it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 16/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0233, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 420.79it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 17/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0257, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 531.41it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 18/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0280, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 552.12it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 19/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0302, val_accuracy: 11.0000\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 10/10 [00:00<00:00, 403.98it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 20/20: train_loss: 0.0005, train_accuracy: 100.0000, val_loss: 3.0325, val_accuracy: 11.0000\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# evaluate on last model\n","val_loss, val_accuracy = evaluate(model, val_loader, criterion)\n","print('MLP. Validation loss: {:.2f}, validation accuracy: {:.2f}'.format(val_loss, val_accuracy))\n"],"metadata":{"id":"TaABSxS682i6","executionInfo":{"status":"ok","timestamp":1774458203876,"user_tz":-60,"elapsed":81,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"bc5b8a26-df61-4779-da4c-0fe035824432","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["MLP. Validation loss: 3.03, validation accuracy: 11.00\n"]}]},{"cell_type":"markdown","source":["3.3.6 **(1pt)** Are your new results reasonable?\n","\n","Yes, they are as expected."],"metadata":{"id":"i09gpDVXwab4"}},{"cell_type":"markdown","metadata":{"id":"0b08471f"},"source":["---\n","### Before You Submit:\n","\n","Please **Restart Session and Run All** to ensure your notebook runs cleanly from top to bottom without errors.\n","\n","This helps us grade your work fairly and ensures everything is saved correctly.\n","\n","Thank you and congratulations! 🥳"]},{"cell_type":"code","source":["## DO NOT EDIT THIS CELL\n","points"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vz7FTMac8njP","executionInfo":{"status":"ok","timestamp":1774458153574,"user_tz":-60,"elapsed":74,"user":{"displayName":"Olivia Lecomte","userId":"07830699269148802057"}},"outputId":"f9b8fd13-15b9-4949-c099-cb131dc42180"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["64"]},"metadata":{},"execution_count":15}]}]}