{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3362,"databundleVersionId":31148,"sourceType":"competition"}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-15T18:01:43.840785Z","iopub.execute_input":"2024-05-15T18:01:43.841192Z","iopub.status.idle":"2024-05-15T18:01:44.196283Z","shell.execute_reply.started":"2024-05-15T18:01:43.841153Z","shell.execute_reply":"2024-05-15T18:01:44.195404Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Assignment: fit a CNN\n\nIn this notebook we load an image classification task: classify images of cats and dogs correctly.\n\nYour task to adapt this code to this dataset to fit a working cat/dog prediction models. You are asked to shortly describe what part of the code you changed into what, and why that was necessary. ","metadata":{}},{"cell_type":"code","source":"!unzip -qq /kaggle/input/dogs-vs-cats/train.zip \n!unzip -qq /kaggle/input/dogs-vs-cats/test1.zip","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:01:44.198307Z","iopub.execute_input":"2024-05-15T18:01:44.199127Z","iopub.status.idle":"2024-05-15T18:02:01.252004Z","shell.execute_reply.started":"2024-05-15T18:01:44.199092Z","shell.execute_reply":"2024-05-15T18:02:01.250818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nimport torch\nfrom torch import nn\n\n# Import torchvision\nimport torchvision\nfrom torchvision.transforms import ToTensor, Resize\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader\n\ntorch.manual_seed(42)\n\nprint(f\"torch version: {torch.__version__}\")\nprint(f\"torchvision version: {torchvision.__version__}\")","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:01.253489Z","iopub.execute_input":"2024-05-15T18:02:01.253833Z","iopub.status.idle":"2024-05-15T18:02:07.959368Z","shell.execute_reply.started":"2024-05-15T18:02:01.253804Z","shell.execute_reply":"2024-05-15T18:02:07.958402Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\nprint(device)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:07.960668Z","iopub.execute_input":"2024-05-15T18:02:07.961827Z","iopub.status.idle":"2024-05-15T18:02:08.013271Z","shell.execute_reply.started":"2024-05-15T18:02:07.961785Z","shell.execute_reply":"2024-05-15T18:02:08.012529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"base_dir = \"/kaggle/working/\"","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.016350Z","iopub.execute_input":"2024-05-15T18:02:08.016659Z","iopub.status.idle":"2024-05-15T18:02:08.023386Z","shell.execute_reply.started":"2024-05-15T18:02:08.016622Z","shell.execute_reply":"2024-05-15T18:02:08.022626Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Prepairing the Data","metadata":{}},{"cell_type":"markdown","source":"**Change:** To the CatDogDataloader we add an additional condition: 'test_mode' which will load the index and the image instead of the label and the image. This allows us later to add our prediction to the appropiate image so that the rader knows which predicted label comes from which image in the test set.    ","metadata":{}},{"cell_type":"code","source":"from PIL import Image\n\nclass CatDogDataset(Dataset):   \n    def __init__(self, data_dir, transform=None, test_mode=False): # add test_mode\n        self.data_dir = data_dir \n        self.transform = transform  \n        self.images = os.listdir(data_dir)  \n        self.test_mode = test_mode  \n\n    def __len__(self):\n        return len(self.images)\n\n    def __getitem__(self, index):\n        image_path = os.path.join(self.data_dir, self.images[index])\n        image = Image.open(image_path)\n\n        if self.transform:\n            image = self.transform(image)\n\n# added code            \n        if self.test_mode: # define test mode\n            # In test mode, return image and ID\n            image_id = self.images[index].split('.')[0]\n            return image, image_id\n        else:\n            # Extract label from the filename \n            label = self.images[index].split(\".\")[0]\n            label = 0 if label == 'cat' else 1\n            return image, label","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.024537Z","iopub.execute_input":"2024-05-15T18:02:08.024874Z","iopub.status.idle":"2024-05-15T18:02:08.033526Z","shell.execute_reply.started":"2024-05-15T18:02:08.024847Z","shell.execute_reply":"2024-05-15T18:02:08.032762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Note*: this code will also transform the test dataset which is probably not ideal. ","metadata":{}},{"cell_type":"markdown","source":"**Perfromance Change:**  To prevent overfitting, we will apply transformations such as horizontal and vertical flips, as well as rotations and zooms ranging from 80-120% to the images. This means that each time the training data is loaded, the images are altered, ensuring the network rarely sees the exact same image twice. Instead, it encounters various versions, which helps it learn to distinguish the essential features of cats and dogs from background elements. this technique is reffered to as data augmentation. ","metadata":{}},{"cell_type":"markdown","source":"**Ease of use Change:**\nWe have modified the image size from 100 to a power of 2, specifically to 128. This adjustment allows for the seamless integration of multiple max pooling layers in our convolutional neural network. Max pooling generally halves the input dimensions, which aligns perfectly with our consistent use of 2x2 kernel sizes, as you will see later. This change ensures that the image dimensions remain compatible with successive pooling layers without the need for repeated resizing. ","metadata":{}},{"cell_type":"code","source":"transform = transforms.Compose([\n# data augmentation:\n    transforms.RandomHorizontalFlip(p = 0.5),# Randomly flip images horizontally a chance of 50%\n    transforms.RandomVerticalFlip(p = 0.2), # Randomly flip images vertically with a chance of 20%\n    transforms.RandomAffine(degrees= 30, scale=(0.8, 1.2)),  # Scale images from 80% to 120%\n    \n    ToTensor(),\n    Resize((128,128), antialias=True) # size is changed: ease of use and antialias was added to get rid of error notifications\n])","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.034642Z","iopub.execute_input":"2024-05-15T18:02:08.034903Z","iopub.status.idle":"2024-05-15T18:02:08.048434Z","shell.execute_reply.started":"2024-05-15T18:02:08.034876Z","shell.execute_reply":"2024-05-15T18:02:08.047493Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We load the data: ","metadata":{}},{"cell_type":"code","source":"# load in the train dataset with the transformations defined in CatDogDataset()\ntrain = CatDogDataset(data_dir = base_dir + '/train', transform = transform)\n\n# load in the test dataset with the transformations defined in CatDogDataset()\ntest = CatDogDataset(data_dir = base_dir + '/test1', transform = transform, test_mode = True)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.049453Z","iopub.execute_input":"2024-05-15T18:02:08.049733Z","iopub.status.idle":"2024-05-15T18:02:08.081055Z","shell.execute_reply.started":"2024-05-15T18:02:08.049704Z","shell.execute_reply":"2024-05-15T18:02:08.080261Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Lets look at some images: ","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef show_image(tensor):\n    # The tensor is expected to be in the format C x H x W\n    # Convert it to the format H x W x C for Matplotlib\n    if isinstance(tensor, torch.Tensor):  # These lines check if the input tensor is a PyTorch tensor\n        image = tensor.numpy()  # convert Pytorch tensor to a NumPy array if true. \n    image = np.transpose(image, (1, 2, 0)) # Rearrange the dimensions of the image using np.transpose. \n                                           # The original format of the image is C x H x W (channels, height, width). \n                                           # This line changes it to H x W x C (height, width, channels), \n                                           # which is the format expected by Matplotlib for displaying images. \n    plt.imshow(image) # displays the image\n    plt.axis('off')  # Turn off axis numbers and ticks\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.082391Z","iopub.execute_input":"2024-05-15T18:02:08.082817Z","iopub.status.idle":"2024-05-15T18:02:08.088508Z","shell.execute_reply.started":"2024-05-15T18:02:08.082789Z","shell.execute_reply":"2024-05-15T18:02:08.087710Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('label: ', train[10][1]) # print the label of the image which is the second object in the 10th object of the list \nshow_image(train[10][0]) # show the picture.","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.089514Z","iopub.execute_input":"2024-05-15T18:02:08.089790Z","iopub.status.idle":"2024-05-15T18:02:08.442601Z","shell.execute_reply.started":"2024-05-15T18:02:08.089769Z","shell.execute_reply":"2024-05-15T18:02:08.441637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('id: ', test[10][1]) # print the label of the image which is the second object in the 10th object of the list \nshow_image(test[10][0]) # show the picture. ","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.443851Z","iopub.execute_input":"2024-05-15T18:02:08.444501Z","iopub.status.idle":"2024-05-15T18:02:08.670789Z","shell.execute_reply.started":"2024-05-15T18:02:08.444465Z","shell.execute_reply":"2024-05-15T18:02:08.669943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(train), len(test)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.672133Z","iopub.execute_input":"2024-05-15T18:02:08.672523Z","iopub.status.idle":"2024-05-15T18:02:08.678721Z","shell.execute_reply.started":"2024-05-15T18:02:08.672491Z","shell.execute_reply":"2024-05-15T18:02:08.677921Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Change**:  We adjust the train/validation split based on the available data to maintain an 80/20 ratio, ensuring a balanced distribution between training and validation sets. ","metadata":{}},{"cell_type":"code","source":"# the train data will be split with a 80/20 split for train and validation set\ntrain_data, validation_data = torch.utils.data.random_split(train, [20000, 5000])","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.679813Z","iopub.execute_input":"2024-05-15T18:02:08.680082Z","iopub.status.idle":"2024-05-15T18:02:08.693741Z","shell.execute_reply.started":"2024-05-15T18:02:08.680060Z","shell.execute_reply":"2024-05-15T18:02:08.693034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Performance Change**: We are increasing the batch size from 32 to 64, which may help in reducing the noisiness of the gradients during training and could enhance the model's training effectiveness. Typically, smaller batch sizes lead to noisier gradients because they consist of fewer data points, limiting the ability to average out the variability and randomness found in sample selection. With a small batch, the gradients are calculated from a less representative sample of the data, resulting in more unstable updates in both direction and magnitude, and more susceptibility to data outliers. Consequently, the learning trajectory can become erratic, affecting the smoothness of training. However, this variability can also be advantageous by helping the model escape local minima and potentially achieving better generalization. In our case, given the high dimensionality of our model's parameters, it is less likely to be trapped in local minima. \n\n**Performance Change**: Setting *num_workers = 4* specifies that four separate worker processes should be used to load the data. This parallelization is used to speed up the data loading process, which can be a bottleneck, especially when dealing with large datasets or when performing complex preprocessing operations on the data before it is fed into the model. By utilizing multiple workers, the DataLoader can simultaneously prepare multiple batches of data, effectively reducing the waiting time for the training process to receive new data to process. This helps in making better use of the GPU resources by minimizing idle times, as the training loop doesn't have to pause and wait for the next batch of data to be ready. ","metadata":{}},{"cell_type":"code","source":"# DataLoader makes using batches realy easy\nfrom torch.utils.data import DataLoader\n\nBATCH_SIZE = 64 # can be changed later (hyperparameter)\n\ntrain_dataloader = DataLoader(train_data,\n    batch_size=BATCH_SIZE,\n    shuffle=True, \n    num_workers = 4 # add number of workers\n)\n\nvalidation_dataloader = DataLoader(validation_data,\n    batch_size=BATCH_SIZE,\n    shuffle=False, \n    num_workers = 4 # add number of workers\n)\n\ntest_dataloader = DataLoader(test,\n    batch_size=BATCH_SIZE,\n    shuffle=False, \n    num_workers = 4 # add number of workers\n)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.696882Z","iopub.execute_input":"2024-05-15T18:02:08.697124Z","iopub.status.idle":"2024-05-15T18:02:08.703207Z","shell.execute_reply.started":"2024-05-15T18:02:08.697104Z","shell.execute_reply":"2024-05-15T18:02:08.702304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is a check to see if the batches load properly:","metadata":{}},{"cell_type":"code","source":"# Quick check to fetch one batch from each DataLoader\ntry:\n    train_images, train_labels = next(iter(train_dataloader))\n    print(\"Train batch loaded successfully!\")\nexcept Exception as e:\n    print(f\"Error loading train batch: {e}\")\n\ntry:\n    val_images, val_labels = next(iter(validation_dataloader))\n    print(\"Validation batch loaded successfully!\")\nexcept Exception as e:\n    print(f\"Error loading validation batch: {e}\")","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:08.704248Z","iopub.execute_input":"2024-05-15T18:02:08.704530Z","iopub.status.idle":"2024-05-15T18:02:10.684908Z","shell.execute_reply.started":"2024-05-15T18:02:08.704507Z","shell.execute_reply":"2024-05-15T18:02:10.683768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. Define a Train & Test loop","metadata":{}},{"cell_type":"markdown","source":"**Practical Change**: The code was updated to include a plot that illustrates the changes in training and validation loss over time. This visual aid makes it easier to observe the learning progress of the model and facilitates the identification of learning stagnation and overfitting. By visually comparing these two loss metrics, one can quickly discern whether the model is improving, if it has stopped learning, or if it is fitting too closely to the training data but not generalizing well to new data.","metadata":{}},{"cell_type":"markdown","source":"**Changes**: To prevent errors, I made adjustments by applying the squeeze() function to some matrices and changing the data types of certain objects to .float. This ensured that the matrices were of the correct dimensions and that all objects were compatible for their respective operations. The squeeze() function helps remove single-dimensional entries from the shape of an array, which is necessary for ensuring that tensor operations do not fail due to unexpected tensor ranks. Meanwhile, converting object types to .float ensures that data types are consistent for numerical operations. Additionally I removed the softmax function as we will perform binary classification, which makes it no longer neicessary. these changes are for the train_loop as well as for the test_loop.","metadata":{}},{"cell_type":"code","source":"def train_loop(train_dataloader,validation_dataloader,num_epochs=10):\n    # Prepare lists to store loss values for plotting\n    train_losses = []\n    val_losses = []\n        \n    # Set up plot for dynamic updating\n    plt.figure(figsize=(10, 8))\n    plt.ion()  # Turn on interactive mode\n        \n    for epoch in range(num_epochs):\n        train_loss = 0\n        for batch, (X, y) in enumerate(train_dataloader): # for loop for every batch in an epoch\n            X = X.to(device)\n            y = y.to(device).float()\n            # forward pass\n            predictions_train = model(X).squeeze()\n            loss = loss_func(predictions_train, y)\n            train_loss += loss\n\n            # Backward pass \n            optimizer.zero_grad() \n            loss.backward() \n            optimizer.step()\n        \n        # now we have predictions and the cost of a full epoch\n        # take the average loss of the epoch\n        train_loss /= len(train_dataloader)\n        train_losses.append(train_loss.item())\n        \n        model.eval()\n        val_loss = 0\n        with torch.inference_mode(): # check the performance on the validation set. \n            for X, y in validation_dataloader:\n                X = X.to(device)\n                y = y.to(device).float()\n\n                # 1. Forward pass\n                y_pred_logits = model(X).squeeze()\n                \n                # 2. Calculate loss (accumatively)\n                val_loss += loss_func(y_pred_logits, y) # accumulatively add up the loss per epoch\n\n                # 3. Calculate accuracy (preds need to be same as y_true)\n                metric.update(y_pred_logits, y)\n                \n            # now we have predictions on the validation set of a full epoch \n            # calculate the average loss\n            val_loss /= len(validation_dataloader)\n            val_losses.append(val_loss.item())    \n        val_acc = metric.compute()\n        metric.reset()\n\n        ## Print out what's happening\n        print(f\"Train loss: {train_loss:.5f} | Validation loss: {val_loss:.5f}, Validation acc: {val_acc:.2f}%\")\n        model.train()\n    \n    # Update the plot\n    plt.plot(train_losses, label='Train Loss', color='blue')\n    plt.plot(val_losses, label='Validation Loss', color='red')\n    plt.xlabel('Epochs')\n    plt.ylabel('Loss')\n    plt.title('Training and Validation Loss Over Epochs')\n    plt.xticks(range(num_epochs + 1))  # Set x-ticks to show every epoch\n    plt.ylim(0, 0.7)  # Set y-axis limits from 0 to 0.7\n    plt.legend()\n    plt.draw()\n    plt.pause(0.001)\n        \n    plt.ioff()  # Turn off interactive mode\n    plt.show()\n\n        ","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:10.686728Z","iopub.execute_input":"2024-05-15T18:02:10.687017Z","iopub.status.idle":"2024-05-15T18:02:10.700878Z","shell.execute_reply.started":"2024-05-15T18:02:10.686990Z","shell.execute_reply":"2024-05-15T18:02:10.699954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def test_loop(test_dataloader):\n    model.eval()\n    test_acc = 0\n    with torch.inference_mode():\n        for X, y in test_dataloader:\n            X = X.to(device)\n            y = y.to(device).float()\n\n            # 1. Forward pass\n            y_pred_logits = model(X).squeeze()\n            #y_pred = torch.softmax(y_pred_logits.squeeze(), dim=0)\n\n            # 3. Calculate accuracy (preds need to be same as y_true)\n            metric.update(y_pred_logits, y)\n\n    test_acc = metric.compute()\n    metric.reset()\n\n    ## Print out what's happening\n    print(f\"\\nTest acc: {test_acc:.2f}%\\n\")\n    model.train()","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:10.702196Z","iopub.execute_input":"2024-05-15T18:02:10.702983Z","iopub.status.idle":"2024-05-15T18:02:10.714431Z","shell.execute_reply.started":"2024-05-15T18:02:10.702957Z","shell.execute_reply":"2024-05-15T18:02:10.713516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Change**: we change the metric to 'task = \"binary\"', because we are doing a binary classifiactions task instead of a multiclass classification task. ","metadata":{}},{"cell_type":"code","source":"import torchmetrics\n\nmetric = torchmetrics.Accuracy(task=\"binary\").to(device) # task changed to \"binary\"","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:10.715510Z","iopub.execute_input":"2024-05-15T18:02:10.715935Z","iopub.status.idle":"2024-05-15T18:02:13.437298Z","shell.execute_reply.started":"2024-05-15T18:02:10.715911Z","shell.execute_reply":"2024-05-15T18:02:13.436412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Model Building","metadata":{}},{"cell_type":"markdown","source":"#### **Neicessary Changes:** \nThe original neural network came form a multiclass classification task and the problem we are facing here is a binairy classification task so to addres this we change our output layer to one node and add a sigmoid activation function so the output values of the model are between 1 and 0. We also need to change the dimensions our dense layer will receive which is neicarry for the model to know what the dimension of its model parameter need to be. To define this we need to go through a forward pass in the network and see how the input dimensions change for every layer of the network. After some math i identified that the dimensions of each layer are transformed by this formula: \n- For convolutional  layers:\n    - Dimension = ((Dimension + Padding x 2 - kernel_size)/stride) + 1\n- Max Pool:\n    - Dimension = ((Dimension - kernel_size)/stride) + 1\n\nwith kernels_size = 3, Padding = 1, stride = 1, we do not change the dimensions as the components cancel eachother out.\n\nFor the max pooling we just devide by 2, as again the other foctors cancel each other out. We apply MaxPool 5 times so we need to do: 128:2:2:2:2:2 = 4 ","metadata":{}},{"cell_type":"markdown","source":"#### **Performance Changes:**\n- **Model Achitecture:** \n    - The existing architecture of our model includes four convolutional layers and three maxpool layers, with each convolutional layer having an equal number of nodes, designated as hidden_units. However, I propose that it would be advantageous to incrementally increase the number of units in each convolutional layer as we progress deeper into the network. This adjustment is based on the observation that the initial layers, with their simpler filters, primarily detect basic features such as edges. As we delve deeper, subsequent layers utilize these basic features to discern more complex patterns. By increasing the number of units in deeper layers, we equip the network with more filters to handle this escalating complexity, while maintaining fewer units in earlier layers that address simpler features. This strategy aims to enhance the model's efficiency by optimizing resource allocation throughout the network. this is why I opt for a 32, 64, 128, 256, 512. \n    - I added an extra convolutional layer because when trainging simpler models I reached a limit in training loss which signals that the model is not complex enough to fit all the data properly, hence the increase of the layers. \n    - Finally I also added an extra dense layer between the convolutial layers and the output layer (a single node with sigmoid activation). Dense layers after convolutional layers in a neural network integrate local features into a global context, essential for complex decision-making like object recognition. They add necessary non-linearity, enhancing the model’s ability to handle complex patterns. These layers bridge the gap between feature detection and final predictions also providing some more flexidility and adapability during learning.   \n\n- **Batch Normalisation:** \n    - Batch normalization enhances the stability of the learning process by ensuring that the inputs to each layer are normalized to have zero mean and unit variance. This technique addresses the problem of internal covariate shift, where shifts in the distribution of layer inputs during training can lead to slower convergence. This occurs because deeper layers must continuously adjust to changes in preceding layers. By integrating batch normalization, we aim to accelerate the training process, which was previously slow, ultimately achieving a notable improvement in training speed.\n\n- **Drop Out:**\n    - Dropout serves as a regularization strategy in CNNs to counteract overfitting. It operates by randomly disabling a subset of neurons throughout training, mimicking the training of diverse neural network configurations. This strategy reduces reliance on particular neurons, fostering the creation of durable and broadly applicable features, thus boosting model efficacy on unseen data. We employ dropout as an adjustable hyperparameter, increasing its rate if we observe signs of overfitting. Overfitting is typically indicated when training loss continues to decline while validation loss either plateaus or increases. By adjusting the dropout rate, we provide additional safeguards against overfitting.\n    - We typically avoid applying dropout to the first convolutional layers in a CNN because these layers are responsible for capturing basic, essential features like edges and textures from the input images. Removing neurons randomly from these foundational layers could lead to a significant loss of critical information necessary for the subsequent layers to perform more complex feature extractions. Therefore, preserving the integrity of these initial feature detectors is crucial for maintaining the overall effectiveness and depth of feature representation in deeper layers of the network.","metadata":{}},{"cell_type":"code","source":"class CNN(nn.Module): \n    def __init__(self, input_shape: int, output_shape: int = 1, dropout_rate = 0.2): # dropoout is for now deactivated\n        super().__init__()\n        self.block_1 = nn.Sequential(\n            nn.Conv2d(in_channels=input_shape, out_channels=32, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(32),  # Batch normalization layer after Conv2d\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            \n            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(64),  # Batch normalization layer after Conv2d\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2, stride=2)\n        )\n        self.block_2 = nn.Sequential(\n            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(128),  # Batch normalization layer after Conv2d\n            nn.ReLU(),\n            nn.Dropout(dropout_rate), # Dropout \n            nn.MaxPool2d(kernel_size=2, stride=2),\n            \n            nn.Conv2d(in_channels= 128, out_channels= 256, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(256),  # Batch normalization layer after Conv2d\n            nn.ReLU(),\n            nn.Dropout(dropout_rate), # Dropout \n            nn.MaxPool2d(kernel_size=2, stride=2),\n            \n            nn.Conv2d(in_channels= 256, out_channels= 512, kernel_size=3, stride=1, padding=1),\n            nn.BatchNorm2d(512),  # Batch normalization layer after Conv2d\n            nn.ReLU(),\n            nn.Dropout(dropout_rate), # Dropout \n            nn.MaxPool2d(kernel_size=2, stride=2),\n        )\n        self.classifier = nn.Sequential(\n            nn.Flatten(),\n            nn.Linear(in_features = 512*4*4, out_features = 512), # adjust the input dimensions \n            nn.ReLU(),\n            nn.Linear(in_features=512, out_features = output_shape),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, x: torch.Tensor):\n        x = self.block_1(x)\n        x = self.block_2(x)\n        x = self.classifier(x)\n        return x","metadata":{"execution":{"iopub.status.busy":"2024-05-15T19:00:00.708234Z","iopub.execute_input":"2024-05-15T19:00:00.708786Z","iopub.status.idle":"2024-05-15T19:00:00.722160Z","shell.execute_reply.started":"2024-05-15T19:00:00.708744Z","shell.execute_reply":"2024-05-15T19:00:00.721190Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Change**: Updated the loss function to one that is suited for binary classification.","metadata":{}},{"cell_type":"code","source":"#the loss function\nloss_func = torch.nn.BCELoss() # loss function suited for binary classification","metadata":{"execution":{"iopub.status.busy":"2024-05-15T18:02:13.452491Z","iopub.execute_input":"2024-05-15T18:02:13.452842Z","iopub.status.idle":"2024-05-15T18:02:13.463619Z","shell.execute_reply.started":"2024-05-15T18:02:13.452816Z","shell.execute_reply":"2024-05-15T18:02:13.462992Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Model Training","metadata":{}},{"cell_type":"code","source":"model = CNN(input_shape = 3, # because we have 3 channels: RGB\n            output_shape = 1) # because we have 2 classes, dog or cat\nmodel.to(device)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T19:00:05.724187Z","iopub.execute_input":"2024-05-15T19:00:05.725076Z","iopub.status.idle":"2024-05-15T19:00:05.801312Z","shell.execute_reply.started":"2024-05-15T19:00:05.725043Z","shell.execute_reply":"2024-05-15T19:00:05.800411Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Performance Change**: The Adam optimizer combines elements from both momentum and RMSprop techniques, enhancing its efficiency in training neural networks. Momentum accelerates updates by incorporating a fraction of the previous update vector, effectively gaining speed and reducing oscillations as it progresses. RMSprop adjusts the learning rate by using a rolling average of the squared gradients to scale the updates, helping to manage issues with varying gradient magnitudes. Together in Adam, these methods allow for adaptive learning rate adjustments based on both the direction and variability of the gradients, leading to faster and more stable convergence by optimally scaling updates and maintaining directional momentum. ","metadata":{}},{"cell_type":"code","source":"learning_rate = 0.001 # Popular choice for Adam\n\n#the optimizer\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # optimizer changed to Adam (ADAptive Momentum estimation). ","metadata":{"execution":{"iopub.status.busy":"2024-05-15T19:00:09.912390Z","iopub.execute_input":"2024-05-15T19:00:09.913065Z","iopub.status.idle":"2024-05-15T19:00:09.918534Z","shell.execute_reply.started":"2024-05-15T19:00:09.913027Z","shell.execute_reply":"2024-05-15T19:00:09.917594Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_loop(train_dataloader, validation_dataloader, 40)","metadata":{"execution":{"iopub.status.busy":"2024-05-15T19:00:11.042692Z","iopub.execute_input":"2024-05-15T19:00:11.043061Z","iopub.status.idle":"2024-05-15T19:11:29.432333Z","shell.execute_reply.started":"2024-05-15T19:00:11.043032Z","shell.execute_reply":"2024-05-15T19:11:29.430544Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Train loss: 0.70186 | Validation loss: 0.57417, Validation acc: 0.70%\n\nTrain loss: 0.52441 | Validation loss: 0.57949, Validation acc: 0.72%\n\nTrain loss: 0.46061 | Validation loss: 0.52789, Validation acc: 0.71%\n\nTrain loss: 0.40336 | Validation loss: 0.39977, Validation acc: 0.81%\n\nTrain loss: 0.36361 | Validation loss: 0.38854, Validation acc: 0.83%\n\nTrain loss: 0.32868 | Validation loss: 0.51089, Validation acc: 0.75%\n\nTrain loss: 0.30089 | Validation loss: 0.35129, Validation acc: 0.84%\n\nTrain loss: 0.28195 | Validation loss: 0.44090, Validation acc: 0.81%\n\nTrain loss: 0.26395 | Validation loss: 0.27580, Validation acc: 0.88%\n\nTrain loss: 0.24704 | Validation loss: 0.34398, Validation acc: 0.84%\n\nTrain loss: 0.23415 | Validation loss: 0.27452, Validation acc: 0.88%\n\nTrain loss: 0.22351 | Validation loss: 0.27267, Validation acc: 0.88%\n\nTrain loss: 0.21540 | Validation loss: 0.52243, Validation acc: 0.76%\n\nTrain loss: 0.21239 | Validation loss: 0.22401, Validation acc: 0.91%\n\nTrain loss: 0.19935 | Validation loss: 0.31510, Validation acc: 0.85%\n\nTrain loss: 0.19037 | Validation loss: 0.28392, Validation acc: 0.87%\n\nTrain loss: 0.18590 | Validation loss: 0.18866, Validation acc: 0.92%\n\nTrain loss: 0.18095 | Validation loss: 0.24388, Validation acc: 0.90%\n\nTrain loss: 0.17256 | Validation loss: 0.18765, Validation acc: 0.92%\n\nTrain loss: 0.16445 | Validation loss: 0.20542, Validation acc: 0.91%\n\nTrain loss: 0.16753 | Validation loss: 0.21123, Validation acc: 0.91%\n\nTrain loss: 0.16079 | Validation loss: 0.20622, Validation acc: 0.91%\n\nTrain loss: 0.15309 | Validation loss: 0.18000, Validation acc: 0.93%\n\nTrain loss: 0.15425 | Validation loss: 0.18270, Validation acc: 0.93%\n\nTrain loss: 0.15005 | Validation loss: 0.17033, Validation acc: 0.93%\n\nTrain loss: 0.14951 | Validation loss: 0.18098, Validation acc: 0.93%\n\nTrain loss: 0.13851 | Validation loss: 0.16337, Validation acc: 0.94%\n\nTrain loss: 0.14270 | Validation loss: 0.17982, Validation acc: 0.92%\n\nTrain loss: 0.13669 | Validation loss: 0.18983, Validation acc: 0.92%\n\nTrain loss: 0.13150 | Validation loss: 0.14777, Validation acc: 0.94%\n\nTrain loss: 0.12406 | Validation loss: 0.17416, Validation acc: 0.93%\n\nTrain loss: 0.12725 | Validation loss: 0.16154, Validation acc: 0.93%\n\nTrain loss: 0.12257 | Validation loss: 0.14566, Validation acc: 0.94%\n\nTrain loss: 0.11963 | Validation loss: 0.16783, Validation acc: 0.93%\n\nTrain loss: 0.11791 | Validation loss: 0.16335, Validation acc: 0.93%\n\nTrain loss: 0.11327 | Validation loss: 0.14393, Validation acc: 0.94%\n\nTrain loss: 0.11300 | Validation loss: 0.13775, Validation acc: 0.95%\n\nTrain loss: 0.10680 | Validation loss: 0.16513, Validation acc: 0.93%\n\nTrain loss: 0.10865 | Validation loss: 0.17675, Validation acc: 0.93%\n\nTrain loss: 0.11607 | Validation loss: 0.14858, Validation acc: 0.94%\n\n![image.png](attachment:51f63b13-e8e6-47ec-ad02-dc0c894468ff.png)\n\nTrain loss: 0.10706 | Validation loss: 0.14814, Validation acc: 0.94%\n\nTrain loss: 0.10383 | Validation loss: 0.14567, Validation acc: 0.94%\n\nTrain loss: 0.10235 | Validation loss: 0.18165, Validation acc: 0.93%\n\nTrain loss: 0.10516 | Validation loss: 0.13134, Validation acc: 0.94%\n\nTrain loss: 0.09967 | Validation loss: 0.13497, Validation acc: 0.95%\n\nTrain loss: 0.09460 | Validation loss: 0.14613, Validation acc: 0.94%\n\nTrain loss: 0.09223 | Validation loss: 0.14490, Validation acc: 0.95%\n\nTrain loss: 0.09813 | Validation loss: 0.16837, Validation acc: 0.93%\n\nTrain loss: 0.09043 | Validation loss: 0.14868, Validation acc: 0.94%\n\nTrain loss: 0.09193 | Validation loss: 0.12443, Validation acc: 0.95%\n\nTrain loss: 0.08646 | Validation loss: 0.13461, Validation acc: 0.95%\n\nTrain loss: 0.08388 | Validation loss: 0.16227, Validation acc: 0.94%\n\nTrain loss: 0.08604 | Validation loss: 0.14223, Validation acc: 0.94%\n\nTrain loss: 0.08690 | Validation loss: 0.13122, Validation acc: 0.95%\n\nTrain loss: 0.08145 | Validation loss: 0.12594, Validation acc: 0.95%\n\nTrain loss: 0.08212 | Validation loss: 0.17840, Validation acc: 0.93%\n\nTrain loss: 0.08092 | Validation loss: 0.11986, Validation acc: 0.95%\n\nTrain loss: 0.07542 | Validation loss: 0.14185, Validation acc: 0.95%\n\nTrain loss: 0.07492 | Validation loss: 0.17133, Validation acc: 0.93%\n\nTrain loss: 0.07185 | Validation loss: 0.13191, Validation acc: 0.95%\n\nTrain loss: 0.07326 | Validation loss: 0.12466, Validation acc: 0.95%\n\nTrain loss: 0.07769 | Validation loss: 0.14553, Validation acc: 0.94%\n\nTrain loss: 0.06736 | Validation loss: 0.13013, Validation acc: 0.95%\n\nTrain loss: 0.07005 | Validation loss: 0.18428, Validation acc: 0.93%\n\nTrain loss: 0.07175 | Validation loss: 0.15267, Validation acc: 0.94%\n\nTrain loss: 0.06811 | Validation loss: 0.11617, Validation acc: 0.95%\n\nTrain loss: 0.06963 | Validation loss: 0.14188, Validation acc: 0.94%\n\nTrain loss: 0.06658 | Validation loss: 0.14038, Validation acc: 0.95%\n\nTrain loss: 0.06619 | Validation loss: 0.12609, Validation acc: 0.95%\n\nTrain loss: 0.06185 | Validation loss: 0.12175, Validation acc: 0.95%\n\nTrain loss: 0.06609 | Validation loss: 0.14707, Validation acc: 0.94%\n\nTrain loss: 0.06376 | Validation loss: 0.15902, Validation acc: 0.94%\n\nTrain loss: 0.06301 | Validation loss: 0.12243, Validation acc: 0.95%\n\nTrain loss: 0.05980 | Validation loss: 0.13312, Validation acc: 0.95%\n\nTrain loss: 0.06048 | Validation loss: 0.14021, Validation acc: 0.95%\n\nTrain loss: 0.05585 | Validation loss: 0.16578, Validation acc: 0.94%\n\nTrain loss: 0.06021 | Validation loss: 0.14601, Validation acc: 0.94%\n\nTrain loss: 0.06000 | Validation loss: 0.13544, Validation acc: 0.95%\n\nTrain loss: 0.06004 | Validation loss: 0.12588, Validation acc: 0.95%\n\nTrain loss: 0.05660 | Validation loss: 0.15015, Validation acc: 0.95%\n\nlower learnrate: 0.0001\n\nTrain loss: 0.04342 | Validation loss: 0.11511, Validation acc: 0.96%\n\nTrain loss: 0.03574 | Validation loss: 0.11550, Validation acc: 0.96%\n\nTrain loss: 0.03349 | Validation loss: 0.14117, Validation acc: 0.96%\n\nTrain loss: 0.03216 | Validation loss: 0.11881, Validation acc: 0.96%\n\nTrain loss: 0.03074 | Validation loss: 0.11863, Validation acc: 0.96%\n\nTrain loss: 0.03023 | Validation loss: 0.11627, Validation acc: 0.96%\n\nTrain loss: 0.02792 | Validation loss: 0.12124, Validation acc: 0.96%\n\nTrain loss: 0.03075 | Validation loss: 0.13342, Validation acc: 0.96%\n\nTrain loss: 0.02862 | Validation loss: 0.11239, Validation acc: 0.97%\n\nTrain loss: 0.02916 | Validation loss: 0.12102, Validation acc: 0.96%\n\nTrain loss: 0.02786 | Validation loss: 0.11119, Validation acc: 0.97%\n\nTrain loss: 0.02815 | Validation loss: 0.11570, Validation acc: 0.96%\n\nTrain loss: 0.02905 | Validation loss: 0.13339, Validation acc: 0.96%\n\nTrain loss: 0.02542 | Validation loss: 0.11558, Validation acc: 0.96%\n\nTrain loss: 0.02599 | Validation loss: 0.14474, Validation acc: 0.96%\n\nTrain loss: 0.02615 | Validation loss: 0.12720, Validation acc: 0.96%\n\nTrain loss: 0.02251 | Validation loss: 0.13685, Validation acc: 0.96%\n\nTrain loss: 0.02459 | Validation loss: 0.12115, Validation acc: 0.96%\n\nTrain loss: 0.02424 | Validation loss: 0.12619, Validation acc: 0.96%\n\nTrain loss: 0.02309 | Validation loss: 0.12706, Validation acc: 0.96%\n\nTrain loss: 0.02306 | Validation loss: 0.12977, Validation acc: 0.96%\n\nTrain loss: 0.02595 | Validation loss: 0.12458, Validation acc: 0.96%\n\nTrain loss: 0.02097 | Validation loss: 0.12518, Validation acc: 0.96%\n\nTrain loss: 0.02387 | Validation loss: 0.12140, Validation acc: 0.97%\n\nTrain loss: 0.02356 | Validation loss: 0.12582, Validation acc: 0.96%\n\nTrain loss: 0.02228 | Validation loss: 0.11950, Validation acc: 0.96%\n\nTrain loss: 0.02889 | Validation loss: 0.12650, Validation acc: 0.96%\n\nTrain loss: 0.02593 | Validation loss: 0.11884, Validation acc: 0.96%\n\nTrain loss: 0.02325 | Validation loss: 0.12172, Validation acc: 0.96%\n\nTrain loss: 0.02113 | Validation loss: 0.12166, Validation acc: 0.96%\n\nTrain loss: 0.02353 | Validation loss: 0.12702, Validation acc: 0.96%\n\nTrain loss: 0.02071 | Validation loss: 0.13710, Validation acc: 0.96%\n\nTrain loss: 0.02361 | Validation loss: 0.11802, Validation acc: 0.96%\n\nTrain loss: 0.02231 | Validation loss: 0.12944, Validation acc: 0.96%\n\nTrain loss: 0.02052 | Validation loss: 0.13900, Validation acc: 0.96%\n\nTrain loss: 0.02058 | Validation loss: 0.12414, Validation acc: 0.96%\n\nTrain loss: 0.02074 | Validation loss: 0.13394, Validation acc: 0.97%\n\nTrain loss: 0.02096 | Validation loss: 0.13021, Validation acc: 0.97%\n\nTrain loss: 0.02155 | Validation loss: 0.12633, Validation acc: 0.96%\n\nTrain loss: 0.01861 | Validation loss: 0.12713, Validation acc: 0.96%\n\nTrain loss: 0.01915 | Validation loss: 0.12559, Validation acc: 0.96%\n\nTrain loss: 0.01828 | Validation loss: 0.15278, Validation acc: 0.96%\n\nTrain loss: 0.02086 | Validation loss: 0.12673, Validation acc: 0.96%\n\nTrain loss: 0.01929 | Validation loss: 0.15713, Validation acc: 0.96%\n\nTrain loss: 0.01689 | Validation loss: 0.15715, Validation acc: 0.96%\n\nTrain loss: 0.01936 | Validation loss: 0.16795, Validation acc: 0.96%\n\nTrain loss: 0.01633 | Validation loss: 0.15260, Validation acc: 0.96%\n\nTrain loss: 0.02099 | Validation loss: 0.14168, Validation acc: 0.96%\n\nTrain loss: 0.01782 | Validation loss: 0.15863, Validation acc: 0.96%\n\nTrain loss: 0.01870 | Validation loss: 0.13163, Validation acc: 0.96%\n\nTrain loss: 0.01651 | Validation loss: 0.14382, Validation acc: 0.96%\n\nTrain loss: 0.01875 | Validation loss: 0.17887, Validation acc: 0.96%\n\nTrain loss: 0.01843 | Validation loss: 0.14439, Validation acc: 0.96%\n\nTrain loss: 0.01751 | Validation loss: 0.13469, Validation acc: 0.96%\n\nTrain loss: 0.01729 | Validation loss: 0.15996, Validation acc: 0.96%\n\nTrain loss: 0.01846 | Validation loss: 0.14988, Validation acc: 0.96%\n\nTrain loss: 0.01671 | Validation loss: 0.16290, Validation acc: 0.96%\n\nTrain loss: 0.01871 | Validation loss: 0.16306, Validation acc: 0.96%\n\nTrain loss: 0.01861 | Validation loss: 0.13421, Validation acc: 0.96%\n\nTrain loss: 0.01919 | Validation loss: 0.17995, Validation acc: 0.96%\n\nTrain loss: 0.01696 | Validation loss: 0.15799, Validation acc: 0.96%\n\nTrain loss: 0.01624 | Validation loss: 0.17124, Validation acc: 0.96%\n\nTrain loss: 0.01766 | Validation loss: 0.16137, Validation acc: 0.97%\n\nTrain loss: 0.01665 | Validation loss: 0.15329, Validation acc: 0.96%\n\nTrain loss: 0.01485 | Validation loss: 0.19141, Validation acc: 0.96%\n\nTrain loss: 0.01546 | Validation loss: 0.13321, Validation acc: 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"}}}]}