{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**If you like my notebook, I would really appreciate an upvote !**","metadata":{}},{"cell_type":"markdown","source":"# Introduction:\n\nThe CIFAR-10 dataset contains 60,000 color images of 32 x 32 pixels in 3 channels divided into 10 classes. Each class contains 6,000 images. The training set contains 50,000 images, while the test sets provides 10,000 images. This image taken from the CIFAR repository ( https://www.cs.toronto.edu/~kriz/cifar.html ). This is a classification problem with 10 classes(muti-label classification). We can take a view on this image for more comprehension of the dataset.","metadata":{}},{"cell_type":"markdown","source":"**Label Encoding is as follows:**\n\n| **Label** | **Type** |\n| -: | :-: |\n| 0 | airplane |\n| 1 | automobile |\n| 2 | bird |\n| 3 | cat |\n| 4 | deer |\n| 5 | dog |\n| 6 | frog |\n| 7 | horse |\n| 8 | ship |\n| 9 | truck |","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport pickle\nfrom PIL import Image\nimport torchvision.datasets as dsets\nimport torchvision.transforms as transforms\nimport torch\nimport torch.nn as nn\nimport pickle\nimport os\nimport torch.nn.functional as F\nimport requests\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport seaborn as sn\nimport pandas as pd\ntorch.manual_seed(1) # Set manual seed\nfrom sklearn import preprocessing\nfrom torch.utils.data import DataLoader, Dataset, Subset\n\n# Define GPU device - We will run our model on GPU\nmps_device = torch.device(\"mps\")","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:34.141640Z","start_time":"2023-01-12T03:35:31.219551Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Data Preparation\n\n\nThe idea here is to pass as much as different transformed images to our model so that our model learns complex patterns and restricts itself to overfit.\n\nThe following steps have been implemented for the same\n\n- *Image resizing* : As the image size from source is 32X32 we apply resize function just to make sure every images passed to our process is similar in terms of size\n\n\n- *Image transformation*: We apply different transformation to the image. Details mentioned below. Here one point to note is that we can randomly apply every type of transformation as this might lead to underfitting. The reason behind this is that the resolution of the images is already very low and applying some critical transfortions like centercrop, vertical flip and other might decay model performance\n\n\n- *Apply normalization to image*: The mean and standard deviation is taken from different studies researchers have implemented and have them to converge faster. You can experiment with different image size and standard deviations as well\n\n\n- *Apply Random Erasing*: It helps in randomly adding some noise to the image, so that the model can learn to generalize better.","metadata":{}},{"cell_type":"markdown","source":"**Define the image size and transformation pipeline for training and validation**","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = 32\n\nmean, std = [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]\n# These values are mostly used by researchers as found to very useful in fast convergence\n\n\n# https://pytorch.org/vision/stable/transforms.html\n# We can try various transformation for good generalization of model\ncomposed_train = transforms.Compose([transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), # Resize the image in a 32X32 shape\n                                     transforms.RandomRotation(20), # Randomly rotate some images by 20 degrees\n                                     transforms.RandomHorizontalFlip(0.1), # Randomly horizontal flip the images\n                                     transforms.ColorJitter(brightness = 0.1, # Randomly adjust color jitter of the images\n                                                            contrast = 0.1, \n                                                            saturation = 0.1), \n                                     transforms.RandomAdjustSharpness(sharpness_factor = 2,\n                                                                      p = 0.1), # Randomly adjust sharpness\n                                     transforms.ToTensor(),   # Converting image to tensor\n                                     transforms.Normalize(mean, std), # Normalizing with standard mean and standard deviation\n                                     transforms.RandomErasing(p=0.75,scale=(0.02, 0.1),value=1.0, inplace=False)])\n\n\ncomposed_test = transforms.Compose([transforms.Resize((IMAGE_SIZE,IMAGE_SIZE)),\n                                    transforms.ToTensor(),\n                                    transforms.Normalize(mean, std)])\n","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:34.154114Z","start_time":"2023-01-12T03:35:34.149023Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Load train and validation dataset**","metadata":{}},{"cell_type":"code","source":"# Load the data and transform the dataset\ntrain_dataset =  dsets.CIFAR10(root='./data', train=True, download=True, transform = composed_train)\nvalidation_dataset = dsets.CIFAR10(root='./data', train=False, download=True, transform = composed_test)\n\n# Create train and validation batch for training\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100)\nvalidation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=100)","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:35.774839Z","start_time":"2023-01-12T03:35:34.161393Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Some important functions to print the images and image convertor from tensor to numpy**","metadata":{}},{"cell_type":"code","source":"def show_data(img):\n    try:\n        plt.imshow(img[0])\n    except Exception as e:\n        print(e)\n    print(img[0].shape, img[0].permute(1,2,0).shape)\n    plt.imshow(img[0].permute(1,2,0))\n    plt.title('y = '+ str(img[1]))\n    plt.show()\n    \n# We need to convert the images to numpy arrays as tensors are not compatible with matplotlib.\ndef im_convert(tensor):\n    #Lets\n    img = tensor.cpu().clone().detach().numpy() #\n    img = img.transpose(1, 2, 0)\n    img = img * np.array(tuple(mean)) + np.array(tuple(std))\n    img = img.clip(0, 1) # Clipping the size to print the images later\n    return img","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:35.789356Z","start_time":"2023-01-12T03:35:35.785250Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Lets print some random datasets**","metadata":{}},{"cell_type":"code","source":"show_data(train_dataset[8])","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:35.921770Z","start_time":"2023-01-12T03:35:35.797086Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Different classes in CIPHAR 10 dataset. \nclasses = ('airplane', \n           'automobile', \n           'bird',\n           'cat',\n           'deer',\n           'dog', \n           'frog', \n           'horse', \n           'ship',\n           'truck')\n\n# Define an iterable on the data\ndata_iterable = iter(train_loader) # converting our train_dataloader to iterable so that we can iter through it. \nimages, labels = next(data_iterable) #going from 1st batch of 100 images to the next batch\nfig = plt.figure(figsize=(20, 10)) \n\n# Lets plot 50 images from our train_dataset\nfor idx in np.arange(10):\n    ax = fig.add_subplot(2, 5, idx+1, xticks=[], yticks=[])\n    \n    # Note: imshow cant print tensor !\n    # Lets convert tensor image to numpy using im_convert function for imshow to print the image\n    plt.imshow(im_convert(images[idx]))\n    ax.set_title(classes[labels[idx].item()])","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:46:12.464082Z","start_time":"2023-01-12T06:46:12.211329Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Convolution Neural Network Implementation","metadata":{}},{"cell_type":"markdown","source":"Steps followed for the modelling process:\n\n1) Define a rough model architecture to follow (credits to IBM for image)\n\n2) Starting with 2 Convolution layer and 1 fully connected layer (fc)\n\n3) Keep adding hidden layers to the above model and check of validation accuracy improves.\n\n4) Note if we increase model layers and neurons this might lead to slower training and increase chances of overfitting. Also increase complexitu might led to getting stuck at saddle point or local minima. Hence to cater all this potential problems, experimented with momentum, dropout & He weight initialization","metadata":{}},{"cell_type":"markdown","source":"**i) Define common model training module**","metadata":{}},{"cell_type":"code","source":"def train_model(model, train_loader, validation_loader, optimizer, n_epochs = 20):\n    \n    # Global variable\n    N_test = len(validation_dataset)\n    accuracy_list = []\n    train_loss_list = []\n    model = model.to(mps_device)\n    train_cost_list = []\n    val_cost_list = []\n    \n    for epoch in range(n_epochs):\n        train_COST = 0\n        for x,y in train_loader:\n            x = x.to(mps_device)\n            y = y.to(mps_device)\n            model.train()\n            optimizer.zero_grad()\n            z = model(x)\n            loss = criterion(z,y)\n            loss.backward()\n            optimizer.step()\n            train_COST+=loss.item()\n            \n        train_COST = train_COST/len(train_loader)\n        train_cost_list.append(train_COST)\n        correct = 0\n        \n        # Perform the prediction on the validation data\n        val_COST = 0\n        for x_test, y_test in validation_loader:\n            model.eval()\n            x_test = x_test.to(mps_device)\n            y_test = y_test.to(mps_device)\n            z = model(x_test)\n            val_loss = criterion(z, y_test)\n            _, yhat = torch.max(z.data, 1)\n            correct += (yhat==y_test).sum().item()\n            val_COST+=val_loss.item()\n        \n        val_COST = val_COST/ len(validation_loader)\n        val_cost_list.append(val_COST)\n            \n        accuracy = correct / N_test\n        accuracy_list.append(accuracy)\n        \n        print(\"--> Epoch Number : {}\".format(epoch + 1),\n              \" | Training Loss : {}\".format(round(train_COST,4)),\n              \" | Validation Loss : {}\".format(round(val_COST,4)),\n              \" | Validation Accuracy : {}%\".format(round(accuracy * 100, 2)))\n        \n    return accuracy_list, train_cost_list, val_cost_list","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:35:38.374700Z","start_time":"2023-01-12T03:35:38.355133Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**ii) CNN Model V1:**","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Image Credits : IBM","metadata":{}},{"cell_type":"code","source":"class CNN(nn.Module):\n    '''\n    CNN Model V1: \n    1. 2 convolution + max pool layers \n    2. 1 fully connected layers\n    3. Default runtime using 0 momentum and 0 dropout value\n    '''\n    \n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, number_of_classes = 10):\n        super(CNN, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        \n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        self.fc1 = nn.Linear(out_2 * 8 * 8, number_of_classes)\n        # Calculation of how we got 8*8 is mentioned in the below comment\n        \n    # Prediction\n    def forward(self, x):\n        x = self.cnn1(x)\n        x = torch.relu(x)\n        x = self.maxpool1(x)\n        x = self.cnn2(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-05T20:51:45.556990Z","start_time":"2023-01-05T20:51:45.538520Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"This is how we get the 8x8 image size after cnn1, maxpool1, cnn2 and maxpool2:\n\n   1. 'out_2' is a given parameter when  the cnn2 was defined, it is the output of cnn2 equal to 32\n\n   2. 8x8 is the result of the 'image size' after cnn1, maxpool1, cnn2 and maxpool2:\n\n2.a. Input size is 32x32\n\n2.b. after cnn1, size per channel (total of 16 channels) is (32 + 2*2 -5)/1 + 1 = 32x32\n\n2.c after maxpool 1, size per channel (still total of 32 channels) is (32 + 2*0 - 2)/2 + 1 (stride size is equal to kernel size of 2) = 16x16 \n\n2.c after cnn2, size per channel is (16+ 2*2 -5)/1 + 1 =16x16\n\n2.d. after maxpool2, size per channel is (16  + 2*0 -2)/2 + 1= 8x8","metadata":{}},{"cell_type":"markdown","source":"**Define loss function, optimizer, dataset loader**","metadata":{}},{"cell_type":"code","source":"# Define the model\nmodel = CNN(out_1=32, out_2=64, number_of_classes = 10)\n\n# Define model training hyperparameters\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)\n\n# Train the model\naccuracy_list_normal, train_cost_list, val_cost_list = train_model(model=model,n_epochs=20,train_loader=train_loader,validation_loader=validation_loader,optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-05T20:51:25.373580Z","start_time":"2023-01-05T20:51:19.471493Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The above with momentum 0 gave less accurate results hence sticking back to momentum = 0.2","metadata":{}},{"cell_type":"markdown","source":"**iii) CNN Model V1 with momentum:**","metadata":{}},{"cell_type":"code","source":"# Define the model\nmodel_mmt = CNN(out_1=32, out_2=64, number_of_classes = 10)\n\n# Define the model learning hyperparameters\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmt.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normal_mmt, train_cost_list_mmt, val_cost_list_mmt=train_model(model=model_mmt,n_epochs=20,train_loader=train_loader,validation_loader=validation_loader,optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T16:57:27.523485Z","start_time":"2023-01-03T16:48:57.592468Z"},"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Save model:\npickle.dump(model, open('./model/CNN_momentum_V1.pkl', 'wb'))\n\n#CNN_momentum_V1 - 2 CNN Layers, Momentum = 0.2, \n# model_mmt = pickle.load(open('./model/CNN_momentum_V1.pkl', 'rb'))","metadata":{"ExecuteTime":{"end_time":"2023-01-03T17:02:35.023674Z","start_time":"2023-01-03T17:02:35.014772Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**iv) CNN Model V2:**\n\nAdded one more hidden layer in the Fully connected segment of the model.\nUsed momentum = 0.2 and dropout value = 0.5","metadata":{}},{"cell_type":"code","source":"class CNN_V2(nn.Module):    \n    '''\n    CNN Model V2: \n    1. 2 convolution & max pool layers \n    2. 2 fully connected layers\n    3. Default runtime using 0.2 momentum and dropout value p = 0.5\n    '''\n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, number_of_classes = 10, p = 0):\n        super(CNN_V2, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        \n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        self.fc1 = nn.Linear(out_2 * 8 * 8, 1000) # Roughly taken seein the input and the output\n        self.drop = nn.Dropout(p=p)\n        self.fc2 = nn.Linear(1000, number_of_classes)\n        \n    # Prediction\n    def forward(self, x):\n        x = self.cnn1(x)\n        x = torch.relu(x)\n        x = self.maxpool1(x)\n        x = self.cnn2(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        x = F.relu(self.drop(x))\n        x = self.fc2(x)\n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T17:40:03.314323Z","start_time":"2023-01-03T17:40:03.293794Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define the model\nmodel_mmtv2 = CNN_V2(out_1=32, out_2=64, number_of_classes = 10, p=0.5)\n\n# Define model learning hyperparamters\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv2.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normal, train_cost_list, val_cost_list=train_model(model=model_mmtv2,n_epochs=20,train_loader=train_loader,validation_loader=validation_loader,optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T17:49:05.958617Z","start_time":"2023-01-03T17:40:05.444982Z"},"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Takeaway*: The accuracy improved by some bits using momentum, dropout and additional fc layer combo hence we will keep momentum & dropouts intact for other models as well","metadata":{}},{"cell_type":"code","source":"# Save model\nmodel_mmtv2 = model_mmtv2.to(torch.device(\"cpu\"))\npickle.dump(model_mmtv2, open('./model/CNN_momentum_V2.pkl', 'wb'))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**v) CNN Model V3 :**\n\nAdded one more hidden layer in the Fully connected segment of the model.\nUsed momentum = 0.2 and dropout value = 0.5","metadata":{}},{"cell_type":"code","source":"class CNN_V3(nn.Module):\n    '''\n    CNN Model V3: \n    1. 2 convolution & max pool layers \n    2. 3 fully connected layers\n    3. Default runtime using 0.2 momentum and dropout value p = 0.5\n    '''\n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, number_of_classes = 10, p = 0):\n        super(CNN_V3, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        \n        # Hidden layer 1\n        self.fc1 = nn.Linear(out_2 * 8 * 8, 1000) # Roughly taken seein the input and the output\n        self.drop = nn.Dropout(p=p)\n        \n        # Hidden layer 2\n        self.fc2 = nn.Linear(1000, 1000)\n        \n        # Final layer\n        self.fc3 = nn.Linear(1000, 10)\n        \n    # Predictiona\n    def forward(self, x):\n        x = self.cnn1(x)\n        x = torch.relu(x)\n        x = self.maxpool1(x)\n        x = self.cnn2(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        x = F.relu(self.drop(x))\n        x = self.fc2(x)\n        x = F.relu(self.drop(x))\n        x = self.fc3(x)\n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T19:09:15.507392Z","start_time":"2023-01-03T19:09:15.488167Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define the model\nmodel_mmtv3 = CNN_V3(out_1=32, out_2=64, number_of_classes = 10, p=0.5)\n\n# Define the model training hyperparameters\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv3.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normal, loss_list_normal=train_model(model=model_mmtv3,n_epochs=20,train_loader=train_loader,validation_loader=validation_loader,optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T19:18:26.296322Z","start_time":"2023-01-03T19:09:17.656088Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Takeaway*: The accuracy improved by some perctange on further addition of the hidden layer. Lets test some more models","metadata":{}},{"cell_type":"code","source":"# Save model\nmodel_mmtv3 = model_mmtv3.to(torch.device(\"cpu\"))\npickle.dump(model_mmtv3, open('./model/CNN_momentum_V3.pkl', 'wb'))\n#CNN_mom/entum_V3 - 2 CNN Layers, 2 hidden layers, Momentum = 0.2","metadata":{"ExecuteTime":{"end_time":"2023-01-03T19:19:45.882814Z","start_time":"2023-01-03T19:19:45.812243Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**vi) CNN Model V3_V2 :**\n\nAdding one more hidden layer & one more convolution layer","metadata":{}},{"cell_type":"code","source":"class CNN_V3_V2(nn.Module):\n    \n    '''\n    CNN Model V3-V2: \n    1. 3 convolution & max pool layers \n    2. 3 fully connected layers\n    3. Default runtime using 0.2 momentum and dropout value p = 0.5\n    '''\n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, out_3 = 128, number_of_classes = 10, p = 0):\n        super(CNN_V3_V2, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        \n        self.cnn3 = nn.Conv2d(in_channels = out_2, out_channels = out_3, kernel_size = 5, padding = 2)\n        self.maxpool3 = nn.MaxPool2d(kernel_size = 2)\n        \n        # Hidden layer 1\n        self.fc1 = nn.Linear(out_3 * 4 * 4, 1000) \n        # 8x8 will change to 4x4 as we added a convolution & max pool layer refer calculation comment above\n        self.drop = nn.Dropout(p=p)\n        \n        # Hidden layer 2\n        self.fc2 = nn.Linear(1000, 1000)\n        \n        # Final layer\n        self.fc3 = nn.Linear(1000, 10)\n        \n    # Predictiona\n    def forward(self, x):\n        \n        x = self.cnn1(x)\n        x = torch.relu(x)\n        x = self.maxpool1(x)\n        \n        x = self.cnn2(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        \n        x = self.cnn3(x)\n        x = torch.relu(x)\n        x = self.maxpool3(x)\n        \n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc2(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc3(x)\n        \n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-04T02:18:35.444033Z","start_time":"2023-01-04T02:18:35.424730Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define model\nmodel_mmtv4 = CNN_V3_V2(out_1=32, out_2=64, out_3 =128, number_of_classes = 10, p=0.5)\n\n# Define the model hyperparameters\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv4.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normalv4, loss_list_normalv4=train_model(model=model_mmtv4, n_epochs=20, train_loader=train_loader, validation_loader=validation_loader, optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T19:39:37.840168Z","start_time":"2023-01-03T19:30:06.323184Z"},"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Takeaway*: The accuracy again improved by some perctange on further addition of the FC layer and the convolution and max pool layer. Now lets see if we can converge faster with similar performance using He intialization","metadata":{}},{"cell_type":"code","source":"model_mmtv4 = model_mmtv4.to(torch.device(\"cpu\"))\npickle.dump(model_mmtv4, open('./model/CNN_momentum_V4.pkl', 'wb'))\n#CNN_momentum_V4 - 3 CNN Layers, 2 hidden layers, Momentum = 0.2","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**vii) CNN Model V3-V3 :**\n\nAdding one more fully connected layer and initializing weights using He initialization","metadata":{}},{"cell_type":"code","source":"class CNN_V3_V3(nn.Module):\n    '''\n    CNN Model V3-V2: \n    1. 3 convolution & max pool layers \n    2. 4 fully connected layers\n    3. Default runtime using 0.2 momentum and dropout value p = 0.5\n    4. Weights initialized using He weight initialization\n    '''\n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, out_3 = 128, number_of_classes = 10, p = 0):\n        super(CNN_V3_V3, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        \n        self.cnn3 = nn.Conv2d(in_channels = out_2, out_channels = out_3, kernel_size = 5, padding = 2)\n        self.maxpool3 = nn.MaxPool2d(kernel_size = 2)\n        \n        # Hidden layer 1\n        self.fc1 = nn.Linear(out_3 * 4 * 4, 1000) \n        torch.nn.init.kaiming_uniform_(self.fc1.weight, nonlinearity='relu')\n        self.drop = nn.Dropout(p=p)\n        \n        # Hidden layer 2\n        self.fc2 = nn.Linear(1000, 1000)\n        torch.nn.init.kaiming_uniform_(self.fc2.weight, nonlinearity='relu') # He weight initialization\n\n        # Hidden layer 3\n        self.fc3 = nn.Linear(1000, 1000)\n        torch.nn.init.kaiming_uniform_(self.fc3.weight, nonlinearity='relu') # He weight initialization\n        \n        # Final layer\n        self.fc4 = nn.Linear(1000, 10)\n        torch.nn.init.kaiming_uniform_(self.fc4.weight, nonlinearity='relu') # He weight initialization\n        \n    # Predictiona\n    def forward(self, x):\n        \n        x = self.cnn1(x)\n        x = torch.relu(x)\n        x = self.maxpool1(x)\n        \n        x = self.cnn2(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        \n        x = self.cnn3(x)\n        x = torch.relu(x)\n        x = self.maxpool3(x)\n        \n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc2(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc3(x)\n\n        x = F.relu(self.drop(x))\n        x = self.fc4(x)\n        \n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T23:20:22.265220Z","start_time":"2023-01-03T23:20:22.242576Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define the model\nmodel_mmtv5 = CNN_V3_V3(out_1=32, out_2=64, out_3 =128, number_of_classes = 10, p=0.5)\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv5.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normalv5, loss_list_normalv5=train_model(model=model_mmtv5, n_epochs=20, train_loader=train_loader, validation_loader=validation_loader, optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-03T20:01:39.212984Z","start_time":"2023-01-03T19:51:48.109994Z"},"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Takeaway*: The accuracy reduced drastically using He weight initialization. Lets drop this approach as we couldnt find global minima through this approach. Lets use traditional random weight initialization with the above architecture","metadata":{}},{"cell_type":"code","source":"model_mmtv5 = model_mmtv5.to(torch.device(\"cpu\"))\npickle.dump(model_mmtv5, open('./model/CNN_momentum_V5.pkl', 'wb'))\n#CNN_momentum_V5 - 3 CNN Layers, 2 hidden layers, Momentum = 0.2, He Weight initialization","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**viii) CNN Model V3-V4 - This resulted as champion model** \n![image-4.png](attachment:image-4.png)\n\nUSed similar architecture as CNN_V3_V3. The only difference is additon of more more fc layer and removal of He weight initialization.\n\n**Below is the rough architecture which my model follows:**","metadata":{},"attachments":{"image-4.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"![image.png](attachment:image.png)\n\n\nCredits:\nhttps://developersbreach.com/convolution-neural-network-deep-learning/","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"**Extra additions to model apart from additional convolution layer**\n1. Lets add dropout to convolution layers with p=0.2 as high value might lead to underfit as these are the initial layers from which the model extract useful features from the image.\n2. Added Batch normalization as this will help us to converge faster","metadata":{}},{"cell_type":"code","source":"class CNN_V3_V4(nn.Module):\n    \"\"\"\n    Adding one more hidden layer & dropout value & one more convolution layer\n    Total 3 hidden layers, 3 convolution layer & Batch Normalization\n    \"\"\"\n    # Constructor\n    def __init__(self, out_1 = 32, out_2 = 64, out_3 = 128, number_of_classes = 10, p = 0):\n        super(CNN_V3_V4, self).__init__()\n        self.cnn1 = nn.Conv2d(in_channels = 3, out_channels = out_1, kernel_size = 5, padding = 2)\n        self.maxpool1 = nn.MaxPool2d(kernel_size = 2)\n        self.conv1_bn = nn.BatchNorm2d(out_1)\n        self.drop_conv = nn.Dropout(p=0.2)\n        \n        self.cnn2 = nn.Conv2d(in_channels = out_1, out_channels = out_2, kernel_size = 5, padding = 2)\n        self.maxpool2 = nn.MaxPool2d(kernel_size = 2)\n        self.conv2_bn = nn.BatchNorm2d(out_2)\n        \n        self.cnn3 = nn.Conv2d(in_channels = out_2, out_channels = out_3, kernel_size = 5, padding = 2)\n        self.maxpool3 = nn.MaxPool2d(kernel_size = 2)\n        self.conv3_bn = nn.BatchNorm2d(out_3)\n        \n        # Hidden layer 1\n        self.fc1 = nn.Linear(out_3 * 4 * 4, 1000) \n        self.drop = nn.Dropout(p=p)\n        self.fc1_bn = nn.BatchNorm1d(1000)\n        \n        # Hidden layer 2\n        self.fc2 = nn.Linear(1000, 1000)\n        self.fc2_bn = nn.BatchNorm1d(1000)\n\n        # Hidden layer 3\n        self.fc3 = nn.Linear(1000, 1000)\n        self.fc3_bn = nn.BatchNorm1d(1000)\n        \n        # Hidden layer 4\n        self.fc4 = nn.Linear(1000, 1000)\n        self.fc4_bn = nn.BatchNorm1d(1000)\n        \n        # Final layer\n        self.fc5 = nn.Linear(1000, 10)\n        self.fc5_bn = nn.BatchNorm1d(10)\n        \n    # Predictiona\n    def forward(self, x):\n        \n        x = self.cnn1(x)\n        x = self.conv1_bn(x)\n        x = self.maxpool1(x)\n        x = self.drop_conv(x)\n        \n        x = self.cnn2(x)\n        x = self.conv2_bn(x)\n        x = torch.relu(x)\n        x = self.maxpool2(x)\n        x = self.drop_conv(x)\n        \n        x = self.cnn3(x)\n        x = self.conv3_bn(x)\n        x = torch.relu(x)\n        x = self.maxpool3(x)\n        x = self.drop_conv(x)\n        \n        x = x.view(x.size(0), -1)\n        x = self.fc1(x)\n        x = self.fc1_bn(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc2(x)\n        x = self.fc2_bn(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc3(x)\n        x = self.fc3_bn(x)\n        \n        x = F.relu(self.drop(x))\n        x = self.fc4(x)\n        x = self.fc4_bn(x)\n\n        x = F.relu(self.drop(x))\n        x = self.fc5(x)\n        x = self.fc5_bn(x)\n        \n        return(x)","metadata":{"ExecuteTime":{"end_time":"2023-01-12T03:50:32.798589Z","start_time":"2023-01-12T03:50:32.775876Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_mmtv5 = CNN_V3_V4(out_1=32, out_2=64, out_3 =128, number_of_classes = 10, p=0.5)\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv5.parameters(), lr = learning_rate, momentum = 0.2)\n\n# Train the model\naccuracy_list_normalv5, train_cost_listv5, val_cost_listv5=train_model(model=model_mmtv5, n_epochs=200, train_loader=train_loader, validation_loader=validation_loader, optimizer=optimizer)","metadata":{"ExecuteTime":{"end_time":"2023-01-12T05:58:45.697658Z","start_time":"2023-01-12T03:50:33.230875Z"},"_kg_hide-output":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Save the model\nmodel_mmtv5 = model_mmtv5.to(torch.device(\"cpu\"))\npickle.dump(model_mmtv5, open('./model/CNN_momentum_V8_12_01_23.pkl', 'wb'))\n#CNN_momentum_V5 - 3 CNN Layers, 3 hidden layers, Momentum = 0.2\n\n# model_mmtv5 = pd.read_pickle('./model/CNN_momentum_V7.pkl')\n# model_mmtv5 = model_mmtv5.to(mps_device)","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:42:21.981961Z","start_time":"2023-01-12T06:42:21.773265Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Model Performance Report & Error Analysis","metadata":{}},{"cell_type":"markdown","source":"**i) Printing model training loss vs accuracy:**","metadata":{}},{"cell_type":"code","source":"fig, ax1 = plt.subplots()\ncolor = 'tab:red'\nax1.plot(train_cost_listv5,color=color)\nax1.set_xlabel('epoch',color=color)\nax1.set_ylabel('total loss',color=color)\nax1.tick_params(axis='y', color=color)\n    \nax2 = ax1.twinx()  \ncolor = 'tab:blue'\nax2.set_ylabel('accuracy', color=color)  \nax2.plot( accuracy_list_normalv5, color=color)\nax2.tick_params(axis='y', labelcolor=color)\nfig.tight_layout()","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:01:27.104742Z","start_time":"2023-01-12T06:01:26.903829Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Takeaway**\n\nAs we can see that there is no sort of anomolous behavior in model traning and model converges to minima smoothly","metadata":{}},{"cell_type":"markdown","source":"**ii) Printing training and validation loss:**","metadata":{}},{"cell_type":"code","source":"plt.plot(train_cost_listv5, 'r', label='Training Loss')\nplt.plot(val_cost_listv5,  'g',  label='Validation Loss')\nplt.xlabel(\"Iteration\")\nplt.title(\"Loss\")\nplt.legend()","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:01:30.115149Z","start_time":"2023-01-12T06:01:30.001415Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Takeaways :** \n\nWe can see that the training loss and validation loss are close to each other. This gives a sense that we did a decent job in **avoiding our model to overfit.**","metadata":{}},{"cell_type":"markdown","source":"**iii) Error Analysis: Printing Confusion Matrix & Classification Report :**","metadata":{}},{"cell_type":"code","source":"y_pred = []\ny_true = []\n\n# iterate over test data\nfor x, y in torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=1):\n    \n    #print('iter val', i)\n    x = x.to(mps_device)\n    y = y.to(mps_device)\n    z = model_mmtv5(x)\n    _, yhat = torch.max(z, 1)\n    pred = yhat.data.cpu().numpy()\n    y_pred.extend(pred) # Save Prediction\n\n    labels = y.data.cpu().numpy()\n    y_true.extend(labels) # Save Truth\n\n# Build confusion matrix\ncf_matrix = confusion_matrix(y_true, y_pred)\ndf_cm = pd.DataFrame(cf_matrix/np.sum(cf_matrix) *10, index = [i for i in classes],\n                     columns = [i for i in classes])\nplt.figure(figsize = (12,7))\nsn.heatmap(df_cm, annot=True)\nplt.savefig('output.png')","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:02:30.943667Z","start_time":"2023-01-12T06:01:33.045037Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_true, y_pred))","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:04:05.364903Z","start_time":"2023-01-12T06:04:05.331539Z"},"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot the mis-classified samples\ncount = 0\ni = 0\nfor x, y in torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=1):\n    #print('iter val', i)\n    x = x.to(mps_device)\n    y = y.to(mps_device)\n    z = model_mmtv5(x)\n    _, yhat = torch.max(z, 1)\n    if yhat != y:\n        show_data(validation_dataset[i])\n        plt.show()\n        print(\"yhat: \",yhat)\n        count += 1\n    if count >= 3:\n        break\n    i+=1","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:04:49.989669Z","start_time":"2023-01-12T06:04:49.526144Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Takeaway**\n\n1) Model is not able to perform well in predicting bird, cats, dogs as compared to other class.\n\n2) Overall model accuracy is decent. If we are able to get more data for cats and dogs we can improve our model performance\n\n3) As we can see in the above image model gets confused in predicting airplane an bird, which is fair enough as the underlying matrix might resemble very close to each other. Hence if we can get more training data or we can specifically add more oversampling of data for model to learn better, there is chance to further boost the accuracy.","metadata":{}},{"cell_type":"markdown","source":"**iv) Printing how out model performs on the first 20 images:**","metadata":{}},{"cell_type":"code","source":"data_iterable = iter(validation_loader)\nimages, labels = next(data_iterable)\nimages = images.to(mps_device)\nlabels = labels.to(mps_device)\noutput = model_mmtv5(images)\n_, preds = torch.max(output, 1)\n\nfig = plt.figure(figsize=(25, 5))\n\nfor idx in np.arange(20):\n    ax = fig.add_subplot(2, 10, idx+1, xticks=[], yticks=[])\n    plt.imshow(im_convert(images[idx]))\n    ax.set_title(\"{} ({})\".format(str(classes[preds[idx].item()]), str(classes[labels[idx].item()])), color=(\"green\" if preds[idx]==labels[idx] else \"red\"))","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:04:55.151415Z","start_time":"2023-01-12T06:04:54.492810Z"},"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Performance on Hold-Out Set","metadata":{}},{"cell_type":"markdown","source":"**Randomly test our model performance on random image taken from internet and see how our model performs on totally unseen data**","metadata":{}},{"cell_type":"code","source":"# Ex1\n\nurl = 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRsa3ISspmdRq4nDC9M6pfoNh1TvukFHBzGuA&usqp=CAU'\nresponse = requests.get(url, stream = True)\nimg = Image.open(response.raw)\nplt.imshow(img)\n\n# Image cleared to reduce size of ipynb file","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:05:00.130844Z","start_time":"2023-01-12T06:04:59.783618Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Convert holdout image to testing format\nimg = composed_test(img)  \nplt.imshow(im_convert(img)) \n\nimage = img.to(mps_device).unsqueeze(0) \noutput = model_mmtv5(image)\n_, pred = torch.max(output, 1)\nprint(\"Prediction by Model:\", (classes[pred.item()]))","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:05:02.515820Z","start_time":"2023-01-12T06:05:02.423782Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Ex2\n\nurl = 'https://cdn.pixabay.com/photo/2016/02/19/15/46/labrador-retriever-1210559__480.jpg'\nresponse = requests.get(url, stream = True)\nimg = Image.open(response.raw)\nplt.imshow(img)\n\n# Image cleared to reduce size of ipynb file","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:05:04.362706Z","start_time":"2023-01-12T06:05:04.096454Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Convert holdout image to testing format\nimg = composed_test(img)\nplt.imshow(im_convert(img)) \nimage = img.to(mps_device).unsqueeze(0)\noutput = model_mmtv5(image)\n_, pred = torch.max(output, 1)\nprint(\"Prediction by Model:\", (classes[pred.item()]))","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:05:05.999176Z","start_time":"2023-01-12T06:05:05.934020Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. Prediction on Test","metadata":{}},{"cell_type":"code","source":"class TestImageDataset(Dataset):\n    def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):\n        self.img_labels = pd.read_csv(annotations_file)\n        self.img_dir = img_dir\n        self.transform = transform\n        self.target_transform = target_transform\n\n    def __len__(self):\n        return len(self.img_labels)\n\n    def __getitem__(self, idx):\n        # Define the path where images are stored by yourself.\n        # Remember, the first image is ./train/1.png\n        img_path = self.img_dir + '/' + str(self.img_labels.iloc[idx, 0]) +'.png'\n\n        # Read image. Recall how you converted an image to numpy array in the previous step.\n        image = np.asarray(Image.open(img_path).convert('RGB'))\n\n        label = self.img_labels.iloc[idx, 1]\n        if self.transform:\n            image = self.transform(image)\n        if self.target_transform:\n            label = self.target_transform(label)\n        return image, label","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:05:08.731504Z","start_time":"2023-01-12T06:05:08.716616Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Creating transform pipeline for test data\ncomposed_test_check = transforms.Compose([transforms.ToPILImage(), \n                                          transforms.Resize((IMAGE_SIZE,IMAGE_SIZE)),\n                                          transforms.ToTensor(),\n                                          transforms.Normalize(mean, std)])\n\nSUBMISSION = os.path.join(os.getcwd(),\"data/sampleSubmission.csv\")\ntest_dataset = TestImageDataset(annotations_file=SUBMISSION, img_dir='./data/test', transform=composed_test_check)\ntest_loader = DataLoader(dataset=test_dataset, batch_size=500, shuffle=False)\n\n# Load the submission file\nsubmit_df = pd.read_csv(SUBMISSION)\n\n# Define an encoder\nlabel_encoder = preprocessing.LabelEncoder()   \n# Fit the encoder\nlabel_encoder.fit_transform(list(classes))\n\n# Iteratively predict the target and update the submission file\nmodel_mmtv5 = model_mmtv5.to(mps_device)\ni = 0\ny_test_pred_list = []\nfor X_test, _ in torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1000):\n\n    X_test = X_test.to(mps_device) # Move X_train to the GPU\n    model_mmtv5.eval()\n\n    _, y_test_pred = torch.max(model_mmtv5(X_test), 1) # Pick the most probable label\n\n    # Move the predicted values to CPU.\n    y_test_pred = y_test_pred.cpu()   \n\n    # As submission expects actual category names such as cat, the predicted values have to be transformed. \n    y_test_pred = label_encoder.inverse_transform(y_test_pred)\n    y_test_pred_list.extend(y_test_pred)\n    \n# Add labels to submission file\nsubmit_df['label'] = y_test_pred_list","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:36:54.879279Z","start_time":"2023-01-12T06:05:24.677915Z"}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Save submission\nsubmit_df.to_csv('submission_CNNV3V4_01_2023_V2.csv', index = False)","metadata":{"ExecuteTime":{"end_time":"2023-01-12T06:39:27.558688Z","start_time":"2023-01-12T06:39:27.272608Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:image.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Reached greater than 85% accuracy on private test data. Due to late submission it is not accepting it to be posted on private leaderboard.","metadata":{}},{"cell_type":"markdown","source":"Till now we tried different customer architectures by manually experiementing around different models. We are avle to cross 85% accuracy mark. Can we do even better? \n\nThe answer is yes we can: Resnet comes to rescue. Lets read about it below.","metadata":{}},{"cell_type":"markdown","source":"# Resnet -34 The Deep Killer","metadata":{}},{"cell_type":"markdown","source":"So what is Resnet: \n\nResnet34 is a state-of-the-art image classification model, structured as a 34 layer convolutional neural network and defined in \"Deep Residual Learning for Image Recognition\". Restnet34 is pre-trained on the ImageNet dataset which contains 100,000+ images across 200 different classes. This gives a good starting point for weights initialization for our CIFAR dataset. \n\nNote this is a pre-trained model but I have manually attempted few architectures in the above code to build a model with decent accuracy. \n\nBelow code is just a overview given competition spirit and accuracy war people hold ;) Kidding its just for learning.","metadata":{}},{"cell_type":"markdown","source":"You can find more about Restnet-34 here : \n\nhttps://roboflow.com/model/resnet-34#:~:text=What%20is%20Resnet34%3F,images%20across%20200%20different%20classes.\n        ","metadata":{}},{"cell_type":"markdown","source":"![Screen Shot 2023-01-29 at 12.34.20 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"}}},{"cell_type":"markdown","source":"**Define Image Size & similarly get the training and validation data**","metadata":{}},{"cell_type":"code","source":"IMAGE_SIZE = 224 # We need to resize the images given resnet takes input of image size >= 224\n\nmean, std = [0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261]\n# These values are mostly used by researchers as found to very useful in fast convergence\n\n\n# https://pytorch.org/vision/stable/transforms.html\n# We can try various transformation for good generalization of model\ncomposed_train = transforms.Compose([transforms.Resize((IMAGE_SIZE, IMAGE_SIZE)), # Resize the image in a 32X32 shape\n                                     transforms.RandomRotation(20), # Randomly rotate some images by 20 degrees\n                                     transforms.RandomHorizontalFlip(0.1), # Randomly horizontal flip the images\n                                     transforms.ColorJitter(brightness = 0.1, # Randomly adjust color jitter of the images\n                                                            contrast = 0.1, \n                                                            saturation = 0.1), \n                                     transforms.RandomAdjustSharpness(sharpness_factor = 2,\n                                                                      p = 0.1), # Randomly adjust sharpness\n                                     transforms.ToTensor(),   # Converting image to tensor\n                                     transforms.Normalize(mean, std), # Normalizing with standard mean and standard deviation\n                                     transforms.RandomErasing(p=0.75,scale=(0.02, 0.1),value=1.0, inplace=False)])\n\n\ncomposed_test = transforms.Compose([transforms.Resize((IMAGE_SIZE,IMAGE_SIZE)),\n                                    transforms.ToTensor(),\n                                    transforms.Normalize(mean, std)])\n\n\n# Load the data and transform the dataset\ntrain_dataset =  dsets.CIFAR10(root='./data', \n                               train=True, \n                               download=True, \n                               transform = composed_train)\nvalidation_dataset = dsets.CIFAR10(root='./data', \n                                   train=False, \n                                   download=True, \n                                   transform = composed_test)\n\n# Create train and validation batch for training\ntrain_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100)\nvalidation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=100)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The below code was run on google colab because of GPU restrictions, I have just stiched notebooks, let me know in the comments for any doubts**","metadata":{}},{"cell_type":"code","source":"def resnet_34():\n    # Define the resnet model\n    resnet = torchvision.models.resnet34(pretrained=True)\n    \n    # Update the fully connected layer of resnet with our current target of 10 desired outputs\n    resnet.fc = torch.nn.Linear(resnet.fc.in_features, 10)\n    \n    # Initialize with xavier uniform\n    torch.nn.init.xavier_uniform_(resnet.fc.weight)\n    return resnet\n\nmodel_mmtv6 = resnet_34()\n\ncriterion = nn.CrossEntropyLoss()\nlearning_rate = 0.1\noptimizer = torch.optim.SGD(model_mmtv5.parameters(), \n                            lr = learning_rate, \n                            momentum = 0.2)\n\n# Define the Scheduler\nscheduler = ReduceLROnPlateau(optimizer, 'min')\n\n# Train the model\naccuracy_list_normalv5, train_cost_listv5, val_cost_listv5=train_model(model=model_mmtv5, \n                                                                       n_epochs=25, \n                                                                       train_loader=train_loader, \n                                                                       validation_loader=validation_loader, \n                                                                       optimizer=optimizer, \n                                                                       scheduler = scheduler)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Screen Shot 2023-01-29 at 12.10.30 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"}}},{"cell_type":"markdown","source":"Final Result: **97.15%** isn't it dope. Thats why we say more the data better accuracy we can achieve over the same.","metadata":{}},{"cell_type":"markdown","source":"I hope you liked my approach. Please give upvote which help me remain motivated and add more inutitive notebook for the Data Science Community","metadata":{}}]}