{"cells":[{"cell_type":"markdown","source":["To run the Jupyter notebook on Google Colab:\n","1) Open Google Colab:\n","https://colab.research.google.com\n","2) In the pop-up window, click the \"Upload\" tab\n","3) Click \"Choose file\" → select your .ipynb file\n","4) Colab will open your notebook immediately.\n","5) In Colab in the top-right corner click \"Connect\"\n","6) In Colab in the bottom-right corner click \"T4(Python 3)\", select \"Change runtime type\" and make sure you use GPU\n","\n","No need to install any python packages, everything is preinstalled in Colab"],"metadata":{"id":"iA4VpINKcFwo"}},{"cell_type":"markdown","source":["To run the Jupyter notebook locally on your laptop you need to install all the packages (pytorch, matplotlib):\n","1) pip install --upgrade pip\n","2) pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu\n","3) pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126\n","4) pip install matplotlib\n","5) pip install numpy\n","6) pip install scikit-learn\n","\n"],"metadata":{"id":"R21IKO9QcOKz"}},{"cell_type":"markdown","metadata":{"id":"G9izkKnAuKp7"},"source":["# Import all the necessary libraries"]},{"cell_type":"code","execution_count":47,"metadata":{"id":"tVyBJYPZuKp8","executionInfo":{"status":"ok","timestamp":1763145452049,"user_tz":480,"elapsed":23,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","from torchvision import datasets, transforms\n","from torch.utils.data import DataLoader\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.metrics import confusion_matrix\n","import numpy as np\n","from matplotlib.colors import LogNorm\n"]},{"cell_type":"markdown","metadata":{"id":"xNcwYQvBuKp9"},"source":["# Load MNIST dataset"]},{"cell_type":"markdown","metadata":{"id":"nRurFljAuKp9"},"source":["##### Compose multiple dataset preprocessing steps and load the dataset:\n","transforms.ToTensor() - converts to pytorch tensor, brings scale from 0-255 to 0-1\n","\n","transforms.Normalize((0.1307,), (0.3081,)) - normalizes by mean and variance"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"zMQx0F49uKp9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1763142966265,"user_tz":480,"elapsed":3255,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"e9c538c4-64f0-46ab-8d22-1f794b1289d5"},"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 9.91M/9.91M [00:00<00:00, 19.8MB/s]\n","100%|██████████| 28.9k/28.9k [00:00<00:00, 236kB/s]\n","100%|██████████| 1.65M/1.65M [00:00<00:00, 4.50MB/s]\n","100%|██████████| 4.54k/4.54k [00:00<00:00, 10.5MB/s]\n"]}],"source":["transform = transforms.Compose([\n","    transforms.ToTensor(),\n","    transforms.Normalize((0.1307,), (0.3081,))\n","])\n","\n","train_data = datasets.MNIST(root=\"./data\", train=True, download=True, transform=transform)\n","validation_data  = datasets.MNIST(root=\"./data\", train=False, download=True, transform=transform)"]},{"cell_type":"markdown","metadata":{"id":"USGQUXUauKp8"},"source":["# Define LeNet-5 model architecture\n","##### Model takes as an input images of 28x28 pixels grayscale (single channel)\n","<div style=\"background:white; padding:10px; display:inline-block;\">\n","  <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/3/35/LeNet-5_architecture.svg/1200px-LeNet-5_architecture.svg.png\" width=\"800\">\n","</div>"]},{"cell_type":"markdown","source":["You can make changes to the architecture. Make sure layer parameters match"],"metadata":{"id":"KfTmSKezgunE"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"l2dFZYW3uKp9","executionInfo":{"status":"ok","timestamp":1763142971889,"user_tz":480,"elapsed":9,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["class LeNet5(nn.Module):\n","    def __init__(self):\n","        super(LeNet5, self).__init__()\n","        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)  # 28x28 -> 24x24 -> 12x12 after pooling\n","        self.conv2 = nn.Conv2d(6, 16, kernel_size=5) # 12x12 -> 8x8 -> 4x4 after pooling\n","\n","        self.fc1 = nn.Linear(16*4*4, 120)\n","        self.fc2 = nn.Linear(120, 84)\n","        self.fc3 = nn.Linear(84, 10)\n","\n","        self.relu = nn.ReLU() # nonlinearity is ReLU\n","        self.pool = nn.AvgPool2d(kernel_size=2, stride=2)\n","\n","    def forward(self, x):\n","        x = self.pool(self.relu(self.conv1(x))) # (N, 1, 28, 28) -> (N, 6, 12, 12)\n","        x = self.pool(self.relu(self.conv2(x))) # (N, 6, 12, 12) -> (N, 16, 4, 4)\n","\n","        x = x.view(x.size(0), -1)               # flatten\n","        x = self.relu(self.fc1(x))\n","        x = self.relu(self.fc2(x))\n","        x = self.fc3(x)\n","        return x"]},{"cell_type":"markdown","metadata":{"id":"jrtv57DPuKp-"},"source":["choosing a device \"cuda\" for GPU if available else \"cpu\""]},{"cell_type":"code","execution_count":5,"metadata":{"id":"2KgukIn7uKp-","executionInfo":{"status":"ok","timestamp":1763142976211,"user_tz":480,"elapsed":333,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","model = LeNet5().to(device) # create an instance of LeNet-5 on the chosen device"]},{"cell_type":"markdown","metadata":{"id":"ZLPlEsO3uKp-"},"source":["function to show number of trainable parameters and shape of the input per layer in LeNet"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"I4J41sAQuKp-","executionInfo":{"status":"ok","timestamp":1763142978611,"user_tz":480,"elapsed":1,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["def count_params(model):\n","    print(f\"{'Layer':30} {'Shape':25} {'Params'}\")\n","    print(\"-\" * 70)\n","    total = 0\n","    for name, param in model.named_parameters():\n","        if param.requires_grad:\n","            params = param.numel()\n","            total += params\n","            shape = list(param.shape)\n","            print(f\"{name:30} {str(shape):25} {params}\")\n","    print(\"-\" * 70)\n","    print(f\"Total Trainable Params: {total:,}\")"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mpHvuGMKuKp-","executionInfo":{"status":"ok","timestamp":1763142979433,"user_tz":480,"elapsed":12,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"bc7859d2-dcb1-4164-fc01-01148ba874a3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Layer                          Shape                     Params\n","----------------------------------------------------------------------\n","conv1.weight                   [6, 1, 5, 5]              150\n","conv1.bias                     [6]                       6\n","conv2.weight                   [16, 6, 5, 5]             2400\n","conv2.bias                     [16]                      16\n","fc1.weight                     [120, 256]                30720\n","fc1.bias                       [120]                     120\n","fc2.weight                     [84, 120]                 10080\n","fc2.bias                       [84]                      84\n","fc3.weight                     [10, 84]                  840\n","fc3.bias                       [10]                      10\n","----------------------------------------------------------------------\n","Total Trainable Params: 44,426\n"]}],"source":["count_params(model)"]},{"cell_type":"markdown","source":["# Objective function"],"metadata":{"id":"OBkvoHzQgRW-"}},{"cell_type":"markdown","metadata":{"id":"FBanzzX9uKp-"},"source":["Next, we choose a loss function (Cross Entropy loss)"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"UU_ZkEEMuKp-","executionInfo":{"status":"ok","timestamp":1763143142548,"user_tz":480,"elapsed":4,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["criterion = nn.CrossEntropyLoss() # loss function"]},{"cell_type":"markdown","metadata":{"id":"SrMtkHXMuKp-"},"source":["# Train"]},{"cell_type":"markdown","metadata":{"id":"YptuV77xuKp9"},"source":["### Prepare DataLoader\n","Dataloader allows batching, shuffling and parallel data loading\n","\n","batch size is set to 256, you can change it"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"WL8udzY9uKp9","executionInfo":{"status":"ok","timestamp":1763143177945,"user_tz":480,"elapsed":41,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["batch_size = 256 # batch size can be changed here\n","\n","train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True) # train data is prepared in batches\n","test_loader  = DataLoader(validation_data, batch_size=10000, shuffle=False) # test data is load fully as 1 batch"]},{"cell_type":"markdown","metadata":{"id":"KEDuJYiFuKp-"},"source":["### choosing learning rate"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"ZyJ5B0zGuKp-","executionInfo":{"status":"ok","timestamp":1763143180182,"user_tz":480,"elapsed":17,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["learning_rate = 1e-2 # you can change learning rate here"]},{"cell_type":"markdown","source":["### Optimization algorithm\n","Next, we choose an optimization algorithm (Stochastic Gradient Descend).\n","\n","model.parameters() returns all trainable parameters of the Neural Network. Every nn.Module (like nn.Linear, nn.Conv2d, etc.) registers its trainable parameters automatically"],"metadata":{"id":"neiXla_RhM9G"}},{"cell_type":"code","source":["optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9) # optimization algorithm"],"metadata":{"id":"WmA_IMVohLM6","executionInfo":{"status":"ok","timestamp":1763143181991,"user_tz":480,"elapsed":11,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"execution_count":12,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1Z4Com8LuKp-"},"source":["### Function for 1 epoch of the training loop"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"poWGyeITuKp-","executionInfo":{"status":"ok","timestamp":1763143184442,"user_tz":480,"elapsed":4,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["def train(epoch):\n","    model.train() # sets model to train mode\n","    total_loss = 0\n","    correct = 0\n","    total = 0\n","    for batch_idx, (data, target) in enumerate(train_loader): # Loads one mini-batch of images and labels\n","        data, target = data.to(device), target.to(device) # Sends tensors to GPU if available, otherwise CPU\n","\n","        optimizer.zero_grad() # Clears previous gradients stored in .grad. Necessary because PyTorch accumulates gradients\n","        output = model(data) # Model predicts class scores for the batch\n","        preds = output.argmax(dim=1) # Picks index of highest score per sample → predicted class\n","        correct += (preds == target).sum().item() # count correct predictions\n","        loss = criterion(output, target) # calculates the chosen loss (cross-entropy, squared error, etc.)\n","        loss.backward() # Computes gradients of loss w.r.t. model weights\n","        optimizer.step() # Applies gradient update using chosen algorithm (SGD, Adam, etc.)\n","\n","        total += target.size(0)\n","        total_loss += loss.item() # Stores average loss across the epoch for printing\n","\n","    print(f\"Epoch {epoch+1}: loss = {total_loss / len(train_loader):.4f}\")\n","    return total_loss / len(train_loader), 1 - correct / total"]},{"cell_type":"markdown","metadata":{"id":"a1E51lEVuKp_"},"source":["### Function for inference of the NN on the validation set"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"EPzRoC5SuKp_","executionInfo":{"status":"ok","timestamp":1763143186583,"user_tz":480,"elapsed":11,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["def validation():\n","    model.eval() # Puts network in evaluation mode\n","    total_loss = 0\n","    correct = 0\n","    total = 0\n","    with torch.no_grad(): # Disables gradient calculation. Makes testing faster & uses less memory\n","        for data, target in test_loader:\n","            data, target = data.to(device), target.to(device)\n","            output = model(data)\n","            loss = criterion(output, target)\n","            preds = output.argmax(dim=1) # Picks index of highest score per sample → predicted class\n","            correct += (preds == target).sum().item() # count correct predictions\n","            total += target.size(0)\n","            total_loss += loss.item()\n","\n","    print(f\"Test accuracy: {correct * 100.0 / total:.2f}%\")\n","    return total_loss / len(test_loader), 1 - correct / total"]},{"cell_type":"markdown","source":["### Function to calculate confusion matrix"],"metadata":{"id":"MK8sR7VAm44I"}},{"cell_type":"code","source":["def get_confusion_matrix(model, dataloader, device):\n","    model.eval()\n","    all_preds = []\n","    all_labels = []\n","\n","    with torch.no_grad():\n","        for x, y in dataloader:\n","            x, y = x.to(device), y.to(device)\n","            outputs = model(x)\n","            preds = outputs.argmax(dim=1)\n","\n","            all_preds.append(preds.cpu().numpy())\n","            all_labels.append(y.cpu().numpy())\n","\n","    all_preds = np.concatenate(all_preds)\n","    all_labels = np.concatenate(all_labels)\n","\n","    return confusion_matrix(all_labels, all_preds)"],"metadata":{"id":"hcZVLKQRmiHv","executionInfo":{"status":"ok","timestamp":1763144720192,"user_tz":480,"elapsed":3,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"execution_count":24,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vqfvsrGTuKp_"},"source":["### The Actual training loop"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"88FkC-6tuKp_","executionInfo":{"status":"ok","timestamp":1763145033762,"user_tz":480,"elapsed":309390,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"1833e76d-c3c5-41d9-8657-59d09b5c7284"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1: loss = 1.3068\n","Test accuracy: 90.04%\n","Epoch 2: loss = 0.2330\n","Test accuracy: 95.20%\n","Epoch 3: loss = 0.1363\n","Test accuracy: 96.74%\n","Epoch 4: loss = 0.0967\n","Test accuracy: 97.55%\n","Epoch 5: loss = 0.0790\n","Test accuracy: 97.68%\n","Epoch 6: loss = 0.0663\n","Test accuracy: 98.08%\n","Epoch 7: loss = 0.0578\n","Test accuracy: 98.31%\n","Epoch 8: loss = 0.0516\n","Test accuracy: 98.20%\n","Epoch 9: loss = 0.0466\n","Test accuracy: 98.43%\n","Epoch 10: loss = 0.0441\n","Test accuracy: 98.54%\n"]}],"source":["def reset_weights(m): #reseting weights so the results of the last training loop are erased.\n","    if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):\n","        m.reset_parameters()\n","\n","model.apply(reset_weights)\n","\n","epochs = 10\n","\n","train_losses = []\n","test_losses = []\n","train_errors = []\n","test_errors = []\n","\n","train_cm_history = []\n","test_cm_history = []\n","\n","for epoch in range(epochs):\n","    train_ls, train_err = train(epoch)\n","    test_ls, test_err = validation()\n","\n","    train_losses.append(train_ls)\n","    train_errors.append(100*train_err)\n","    test_losses.append(test_ls)\n","    test_errors.append(100*test_err)\n","\n","    train_cm = get_confusion_matrix(model, train_loader, device)\n","    test_cm  = get_confusion_matrix(model, test_loader, device)\n","    train_cm_history.append(train_cm)\n","    test_cm_history.append(test_cm)"]},{"cell_type":"markdown","metadata":{"id":"ZZtKF5BYuKp_"},"source":["Plotting loss function for both train and test"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":407},"id":"q56qljtfuKp_","executionInfo":{"status":"ok","timestamp":1763145037931,"user_tz":480,"elapsed":197,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"327d50da-a2b5-4d70-f6a4-c26e8b6b755f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n","\n","# Left: Loss\n","axes[0].plot(range(1, epochs+1), train_losses)\n","axes[0].plot(range(1, epochs+1), test_losses)\n","axes[0].set_xlabel(\"Epoch\")\n","axes[0].set_ylabel(\"Cross-entropy loss\")\n","axes[0].set_ylim(0, 0.3)\n","# axes[0].set_title(\"Train/Test Loss\")\n","axes[0].legend([\"Train\", \"Validation\"])\n","\n","# Right: Error (1 - acc)\n","axes[1].plot(range(1, epochs+1), train_errors)\n","axes[1].plot(range(1, epochs+1), test_errors)\n","axes[1].set_xlabel(\"Epoch\")\n","axes[1].set_ylabel(\"0-1 loss (%)\")\n","# axes[1].set_title(\"Train/Test Error\")\n","axes[1].legend([\"Train\", \"Validation\"])\n","axes[1].set_ylim(0, 10)\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","source":["### Plotting confusion matrix"],"metadata":{"id":"wOOE2OC9nAX1"}},{"cell_type":"code","source":["def plot_confusion_matrices_grid(cm_history, title_prefix=\"Epoch\"):\n","    num_epochs = len(cm_history)\n","\n","    cols = int(np.ceil(np.sqrt(num_epochs)))\n","    rows = int(np.ceil(num_epochs / cols))\n","\n","    fig, axes = plt.subplots(rows, cols, figsize=(cols*4, rows*4))\n","    axes = axes.flatten()\n","\n","    for i, cm in enumerate(cm_history):\n","        cm_safe = cm.copy()\n","        cm_safe[cm_safe == 0] = 1  # needed for LogNorm\n","\n","        sns.heatmap(\n","            cm_safe,\n","            cmap=\"magma\",\n","            norm=LogNorm(vmin=1, vmax=cm_safe.max()),   # This makes small values visible\n","            annot=False,\n","            fmt=\"d\",\n","            cbar=True,\n","            ax=axes[i]\n","        )\n","        axes[i].set_title(f\"{title_prefix} {i+1}\")\n","        axes[i].set_xlabel(\"Predicted\")\n","        axes[i].set_ylabel(\"True\")\n","\n","    # Hide leftover unused cells\n","    for j in range(i+1, len(axes)):\n","        axes[j].axis(\"off\")\n","\n","    fig.tight_layout()\n","    plt.show()\n"],"metadata":{"id":"7Ffc3GuSpyYd","executionInfo":{"status":"ok","timestamp":1763145437207,"user_tz":480,"elapsed":40,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":["Confusion matrix on train data"],"metadata":{"id":"p_MWj_Run0o2"}},{"cell_type":"code","source":["plot_confusion_matrices_grid(train_cm_history, title_prefix=\"Train Epoch\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1QT2TV56nyPt","executionInfo":{"status":"ok","timestamp":1763145444475,"user_tz":480,"elapsed":6445,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"6a2e38b7-01b8-4de2-fcd9-63f39de85c11"},"execution_count":45,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x1200 with 22 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matrix on test data"],"metadata":{"id":"DQfeLNjJn45o"}},{"cell_type":"code","source":["plot_confusion_matrices_grid(test_cm_history, title_prefix=\"Test Epoch\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"jKRKCtVYnw48","executionInfo":{"status":"ok","timestamp":1763145452025,"user_tz":480,"elapsed":7548,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"c202a5f1-6a4d-4e7f-c037-07c898dd745d"},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x1200 with 22 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to do:\n","1) Try different hidden layer sizes [Go to section](#scrollTo=l2dFZYW3uKp9)\n","\n","2) Try different learning rates [Go to section](#scrollTo=ZyJ5B0zGuKp-)\n","\n","3) Try different batch sizes [Go to section](#scrollTo=WL8udzY9uKp9)\n","\n","When making changes to the selected cell run all the cells below"]},{"cell_type":"markdown","metadata":{"id":"zyu9LkaluKp_"},"source":["# Plotting convolutional filters\n","In filter visualization:\n","\n","blue are values above 0\n","\n","white are values equal to 0\n","\n","red are values bigger than 0"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"5defOCFzuKp_","executionInfo":{"status":"ok","timestamp":1763145869030,"user_tz":480,"elapsed":41,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}}},"outputs":[],"source":["def visualize_conv_filters(model):\n","    for name, layer in model.named_modules():\n","        if isinstance(layer, torch.nn.Conv2d):\n","            W = layer.weight.data.clone()\n","\n","            # Normalize weights to [0,1]\n","            # W = (W - W.min()) / (W.max() - W.min())\n","\n","            out_channels = W.shape[0]\n","            in_channels = W.shape[1]\n","\n","            print(f\"\\nVisualizing {name}: {out_channels} filters, each with {in_channels} channel kernels\")\n","\n","            # For each filter (output channel)\n","            for f in range(out_channels):\n","                fig, axes = plt.subplots(1, in_channels, figsize=(in_channels * 2, 2))\n","                fig.suptitle(f\"{name} — Filter {f}\")\n","\n","                # If only 1 channel, matplotlib gives a single axis, wrap it\n","                if in_channels == 1:\n","                    axes = [axes]\n","\n","                for c in range(in_channels):\n","                    w = W[f, c].cpu().numpy()\n","                    vmax = np.abs(w).max()  # symmetric range\n","                    vmin = -vmax\n","                    axes[c].imshow(w, cmap='bwr', vmin=vmin, vmax=vmax)\n","                    axes[c].axis('off')\n","\n","                plt.tight_layout()\n","                plt.show()\n"]},{"cell_type":"code","execution_count":57,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"1VPpYgjFuKp_","executionInfo":{"status":"ok","timestamp":1763145871958,"user_tz":480,"elapsed":2364,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"cec5d601-1bba-4af2-9a6a-c8b49e8621ca"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Visualizing conv1: 6 filters, each with 1 channel kernels\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 200x200 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 200x200 with 1 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size 200x200 with 1 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size 200x200 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Visualizing conv2: 16 filters, each with 6 channel kernels\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x200 with 6 Axes>"],"image/png":"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size 1200x200 with 6 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size 1200x200 with 6 Axes>"],"image/png":"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Visualization of the feature map output it produces for an MNIST digit. In filter visualization:\n","\n","blue are values above 0\n","\n","white are values equal to 0\n","\n","red are values bigger than 0"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":608},"id":"X6WevNsMuKp_","executionInfo":{"status":"ok","timestamp":1763145654922,"user_tz":480,"elapsed":833,"user":{"displayName":"Rasul Kairgeldin","userId":"08252840589446120769"}},"outputId":"dc3a040d-e174-45bc-b3e9-fe09dcae341b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 18 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Pick a sample MNIST image from test set\n","sample_img, sample_label = validation_data[0]  # first test image\n","input_img = sample_img.unsqueeze(0).to(device)  # add batch dimension\n","\n","# Get conv1 weights and forward pass\n","model.eval()\n","with torch.no_grad():\n","    conv1_output = model.conv1(input_img)  # shape: [1,6,H,W]\n","\n","num_filters = conv1_output.shape[1]\n","\n","# Plot\n","fig, axes = plt.subplots(3, num_filters, figsize=(num_filters*2, 6))\n","\n","for i in range(num_filters):\n","    # Original image (top row)\n","    axes[0, i].imshow(sample_img[0].cpu().numpy(), cmap='gray')\n","    axes[0, i].axis('off')\n","    if i == 0:\n","        axes[0, i].set_title(f\"Original Image\")\n","\n","    # Conv1 filter weights (middle row)\n","    filter_w = model.conv1.weight.data[i, 0].cpu().numpy()  # [5,5]\n","    vmax = np.abs(filter_w).max()  # symmetric range\n","    vmin = -vmax\n","    axes[1, i].imshow(filter_w, cmap='bwr', vmin=vmin, vmax=vmax)\n","    axes[1, i].axis('off')\n","    axes[1, i].set_title(f\"Filter {i}\")\n","\n","    # Feature map (bottom row)\n","    feature_map = conv1_output[0, i].cpu().numpy()\n","    axes[2, i].imshow(feature_map, cmap='gray')\n","    axes[2, i].axis('off')\n","    axes[2, i].set_title(f\"Activation {i}\")\n","\n","plt.tight_layout()\n","plt.show()\n"]}],"metadata":{"language_info":{"name":"python"},"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}