{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14774,"databundleVersionId":875431,"sourceType":"competition"}],"dockerImageVersionId":31041,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import pandas as pd #For reading csv files.\nimport numpy as np \nimport matplotlib.pyplot as plt #For plotting.\n\nimport PIL.Image as Image #For working with image files.\n\n#Importing torch\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom torch.utils.data import Dataset,DataLoader #For working with data.\n\nfrom torchvision import models,transforms #For pretrained models,image transformations.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:56:41.100958Z","iopub.execute_input":"2025-06-25T01:56:41.101199Z","iopub.status.idle":"2025-06-25T01:56:48.296392Z","shell.execute_reply.started":"2025-06-25T01:56:41.101182Z","shell.execute_reply":"2025-06-25T01:56:48.295793Z"}},"outputs":[],"execution_count":1},{"cell_type":"code","source":"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') #Use GPU if it's available or else use CPU.\nprint(device) #Prints the device we're using.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:56:56.880178Z","iopub.execute_input":"2025-06-25T01:56:56.881099Z","iopub.status.idle":"2025-06-25T01:56:56.942981Z","shell.execute_reply.started":"2025-06-25T01:56:56.881065Z","shell.execute_reply":"2025-06-25T01:56:56.94212Z"}},"outputs":[{"name":"stdout","text":"cuda\n","output_type":"stream"}],"execution_count":2},{"cell_type":"code","source":"path = \"/kaggle/input/aptos2019-blindness-detection/\"\n\ntrain_df = pd.read_csv(f\"{path}train.csv\")\nprint(f'No.of.training_samples: {len(train_df)}')\n\ntest_df = pd.read_csv(f'{path}test.csv')\nprint(f'No.of.testing_samples: {len(test_df)}')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:57:12.53118Z","iopub.execute_input":"2025-06-25T01:57:12.531942Z","iopub.status.idle":"2025-06-25T01:57:12.557337Z","shell.execute_reply.started":"2025-06-25T01:57:12.531916Z","shell.execute_reply":"2025-06-25T01:57:12.556615Z"}},"outputs":[{"name":"stdout","text":"No.of.training_samples: 3662\nNo.of.testing_samples: 1928\n","output_type":"stream"}],"execution_count":3},{"cell_type":"code","source":"#Histogram of label counts.\ntrain_df.diagnosis.hist()\nplt.xticks([0,1,2,3,4])\nplt.grid(False)\nplt.show() ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:57:24.606159Z","iopub.execute_input":"2025-06-25T01:57:24.606769Z","iopub.status.idle":"2025-06-25T01:57:24.827622Z","shell.execute_reply.started":"2025-06-25T01:57:24.606742Z","shell.execute_reply":"2025-06-25T01:57:24.826837Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"#As you can see,the data is imbalanced.\n#So we've to calculate weights for each class,which can be used in calculating loss.\n\nfrom sklearn.utils import class_weight #For calculating weights for each class.\nclass_weights = class_weight.compute_class_weight(class_weight='balanced',classes=np.array([0,1,2,3,4]),y=train_df['diagnosis'].values)\nclass_weights = torch.tensor(class_weights,dtype=torch.float).to(device)\n \nprint(class_weights) #Prints the calculated weights for the classes.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:57:38.150563Z","iopub.execute_input":"2025-06-25T01:57:38.150834Z","iopub.status.idle":"2025-06-25T01:57:39.016243Z","shell.execute_reply.started":"2025-06-25T01:57:38.150808Z","shell.execute_reply":"2025-06-25T01:57:39.015439Z"}},"outputs":[{"name":"stdout","text":"tensor([0.4058, 1.9795, 0.7331, 3.7948, 2.4827], device='cuda:0')\n","output_type":"stream"}],"execution_count":5},{"cell_type":"code","source":"#For getting a random image from our training set.\nnum = int(np.random.randint(0,len(train_df)-1,(1,))) #Picks a random number.\nsample_image = (f'{path}train_images/{train_df[\"id_code\"][num]}.png')#Image file.\nsample_image = Image.open(sample_image) \nplt.imshow(sample_image)\nplt.axis('off')\nplt.title(f'Class: {train_df[\"diagnosis\"][num]}') #Class of the random image.\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:57:52.941347Z","iopub.execute_input":"2025-06-25T01:57:52.942167Z","iopub.status.idle":"2025-06-25T01:57:53.154865Z","shell.execute_reply.started":"2025-06-25T01:57:52.942141Z","shell.execute_reply":"2025-06-25T01:57:53.154206Z"}},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_35/3038677276.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n  num = int(np.random.randint(0,len(train_df)-1,(1,))) #Picks a random number.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"class dataset(Dataset): # Inherits from the Dataset class.\n    '''\n    dataset class overloads the __init__, __len__, __getitem__ methods of the Dataset class. \n    \n    Attributes :\n        df:  DataFrame object for the csv file.\n        data_path: Location of the dataset.\n        image_transform: Transformations to apply to the image.\n        train: A boolean indicating whether it is a training_set or not.\n    '''\n    \n    def __init__(self,df,data_path,image_transform=None,train=True): # Constructor.\n        super(Dataset,self).__init__() #Calls the constructor of the Dataset class.\n        self.df = df\n        self.data_path = data_path\n        self.image_transform = image_transform\n        self.train = train\n        \n    def __len__(self):\n        return len(self.df) #Returns the number of samples in the dataset.\n    \n    def __getitem__(self,index):\n        image_id = self.df['id_code'][index]\n        image = Image.open(f'{self.data_path}/{image_id}.png') #Image.\n        if self.image_transform :\n            image = self.image_transform(image) #Applies transformation to the image.\n        \n        if self.train :\n            label = self.df['diagnosis'][index] #Label.\n            return image,label #If train == True, return image & label.\n        \n        else:\n            return image #If train != True, return image.\n            ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:58:13.750805Z","iopub.execute_input":"2025-06-25T01:58:13.751066Z","iopub.status.idle":"2025-06-25T01:58:13.756776Z","shell.execute_reply.started":"2025-06-25T01:58:13.751048Z","shell.execute_reply":"2025-06-25T01:58:13.756103Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"image_transform = transforms.Compose([transforms.Resize([512,512]),\n                                      transforms.ToTensor(),\n                                      transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))]) #Transformations to apply to the image.\ndata_set = dataset(train_df,f'{path}train_images',image_transform=image_transform)\n\n#Split the data_set so that valid_set contains 0.1 samples of the data_set. \ntrain_set,valid_set = torch.utils.data.random_split(data_set,[3302,360])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:58:30.321886Z","iopub.execute_input":"2025-06-25T01:58:30.322175Z","iopub.status.idle":"2025-06-25T01:58:30.328517Z","shell.execute_reply.started":"2025-06-25T01:58:30.322152Z","shell.execute_reply":"2025-06-25T01:58:30.327934Z"}},"outputs":[],"execution_count":8},{"cell_type":"code","source":"train_dataloader = DataLoader(train_set,batch_size=32,shuffle=True) #DataLoader for train_set.\nvalid_dataloader = DataLoader(valid_set,batch_size=32,shuffle=False) #DataLoader for validation_set.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:58:45.940774Z","iopub.execute_input":"2025-06-25T01:58:45.941259Z","iopub.status.idle":"2025-06-25T01:58:45.945247Z","shell.execute_reply.started":"2025-06-25T01:58:45.941238Z","shell.execute_reply":"2025-06-25T01:58:45.944429Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"#Since we've less data, we'll use Transfer learning.\nmodel = models.resnet34(pretrained=True) #Downloads the resnet18 model which is pretrained on Imagenet dataset.\n\n#Replace the Final layer of pretrained resnet18 with 4 new layers.\nmodel.fc = nn.Sequential(nn.Linear(512,256),nn.Linear(256,128),nn.Linear(128,64),nn.Linear(64,5))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:58:57.856418Z","iopub.execute_input":"2025-06-25T01:58:57.856683Z","iopub.status.idle":"2025-06-25T01:58:58.818862Z","shell.execute_reply.started":"2025-06-25T01:58:57.856664Z","shell.execute_reply":"2025-06-25T01:58:58.818088Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n  warnings.warn(\n/usr/local/lib/python3.11/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet34_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet34_Weights.DEFAULT` to get the most up-to-date weights.\n  warnings.warn(msg)\nDownloading: \"https://download.pytorch.org/models/resnet34-b627a593.pth\" to /root/.cache/torch/hub/checkpoints/resnet34-b627a593.pth\n100%|██████████| 83.3M/83.3M [00:00<00:00, 183MB/s]\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"model = model.to(device) #Moves the model to the device.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:59:10.937005Z","iopub.execute_input":"2025-06-25T01:59:10.93757Z","iopub.status.idle":"2025-06-25T01:59:10.978872Z","shell.execute_reply.started":"2025-06-25T01:59:10.937547Z","shell.execute_reply":"2025-06-25T01:59:10.978068Z"}},"outputs":[],"execution_count":11},{"cell_type":"code","source":"def train(dataloader,model,loss_fn,optimizer):\n    '''\n    train function updates the weights of the model based on the\n    loss using the optimizer in order to get a lower loss.\n    \n    Args :\n         dataloader: Iterator for the batches in the data_set.\n         model: Given an input produces an output by multiplying the input with the model weights.\n         loss_fn: Calculates the discrepancy between the label & the model's predictions.\n         optimizer: Updates the model weights.\n         \n    Returns :\n         Average loss per batch which is calculated by dividing the losses for all the batches\n         with the number of batches.\n    '''\n\n    model.train() #Sets the model for training.\n    \n    total = 0\n    correct = 0\n    running_loss = 0\n    \n    for batch,(x,y) in enumerate(dataloader): #Iterates through the batches.\n        \n        output = model(x.to(device)) #model's predictions.\n        loss   = loss_fn(output,y.to(device)) #loss calculation.\n       \n        running_loss += loss.item()\n        \n        total        += y.size(0)\n        predictions   = output.argmax(dim=1).cpu().detach() #Index for the highest score for all the samples in the batch.\n        correct      += (predictions == y.cpu().detach()).sum().item() #No.of.cases where model's predictions are equal to the label.\n        \n        optimizer.zero_grad() #Gradient values are set to zero.\n        loss.backward() #Calculates the gradients.\n        optimizer.step() #Updates the model weights.\n             \n    \n    avg_loss = running_loss/len(dataloader) # Average loss for a single batch\n    \n    print(f'\\nTraining Loss per batch = {avg_loss:.6f}',end='\\t')\n    print(f'Accuracy on Training set = {100*(correct/total):.6f}% [{correct}/{total}]') #Prints the Accuracy.\n    \n    return avg_loss","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:59:31.677447Z","iopub.execute_input":"2025-06-25T01:59:31.678003Z","iopub.status.idle":"2025-06-25T01:59:31.683717Z","shell.execute_reply.started":"2025-06-25T01:59:31.677982Z","shell.execute_reply":"2025-06-25T01:59:31.682993Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"def validate(dataloader,model,loss_fn):\n    '''\n    validate function calculates the average loss per batch and the accuracy of the model's predictions.\n    \n    Args :\n         dataloader: Iterator for the batches in the data_set.\n         model: Given an input produces an output by multiplying the input with the model weights.\n         loss_fn: Calculates the discrepancy between the label & the model's predictions.\n    \n    Returns :\n         Average loss per batch which is calculated by dividing the losses for all the batches\n         with the number of batches.\n    '''\n    \n    model.eval() #Sets the model for evaluation.\n    \n    total = 0\n    correct = 0\n    running_loss = 0\n    \n    with torch.no_grad(): #No need to calculate the gradients.\n        \n        for x,y in dataloader:\n            \n            output        = model(x.to(device)) #model's output.\n            loss          = loss_fn(output,y.to(device)).item() #loss calculation.\n            running_loss += loss\n            \n            total        += y.size(0)\n            predictions   = output.argmax(dim=1).cpu().detach()\n            correct      += (predictions == y.cpu().detach()).sum().item()\n            \n    avg_loss = running_loss/len(dataloader) #Average loss per batch.      \n    \n    print(f'\\nValidation Loss per batch = {avg_loss:.6f}',end='\\t')\n    print(f'Accuracy on Validation set = {100*(correct/total):.6f}% [{correct}/{total}]') #Prints the Accuracy.\n    \n    return avg_loss","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T01:59:48.701731Z","iopub.execute_input":"2025-06-25T01:59:48.702539Z","iopub.status.idle":"2025-06-25T01:59:48.708081Z","shell.execute_reply.started":"2025-06-25T01:59:48.702513Z","shell.execute_reply":"2025-06-25T01:59:48.707258Z"}},"outputs":[],"execution_count":13},{"cell_type":"code","source":"def optimize(train_dataloader,valid_dataloader,model,loss_fn,optimizer,nb_epochs):\n    '''\n    optimize function calls the train & validate functions for (nb_epochs) times.\n    \n    Args :\n        train_dataloader: DataLoader for the train_set.\n        valid_dataloader: DataLoader for the valid_set.\n        model: Given an input produces an output by multiplying the input with the model weights.\n        loss_fn: Calculates the discrepancy between the label & the model's predictions.\n        optimizer: Updates the model weights.\n        nb_epochs: Number of epochs.\n        \n    Returns :\n        Tuple of lists containing losses for all the epochs.\n    '''\n    #Lists to store losses for all the epochs.\n    train_losses = []\n    valid_losses = []\n\n    for epoch in range(nb_epochs):\n        print(f'\\nEpoch {epoch+1}/{nb_epochs}')\n        print('-------------------------------')\n        train_loss = train(train_dataloader,model,loss_fn,optimizer) #Calls the train function.\n        train_losses.append(train_loss)\n        valid_loss = validate(valid_dataloader,model,loss_fn) #Calls the validate function.\n        valid_losses.append(valid_loss)\n    \n    print('\\nTraining has completed!')\n    \n    return train_losses,valid_losses","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T02:00:09.193016Z","iopub.execute_input":"2025-06-25T02:00:09.193297Z","iopub.status.idle":"2025-06-25T02:00:09.198458Z","shell.execute_reply.started":"2025-06-25T02:00:09.193259Z","shell.execute_reply":"2025-06-25T02:00:09.197625Z"}},"outputs":[],"execution_count":14},{"cell_type":"code","source":"loss_fn   = nn.CrossEntropyLoss(weight=class_weights) #CrossEntropyLoss with class_weights.\noptimizer = torch.optim.SGD(model.parameters(),lr=0.001) \nnb_epochs = 30\n#Call the optimize function.\ntrain_losses, valid_losses = optimize(train_dataloader,valid_dataloader,model,loss_fn,optimizer,nb_epochs)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T02:00:26.742175Z","iopub.execute_input":"2025-06-25T02:00:26.742895Z","iopub.status.idle":"2025-06-25T05:34:12.059138Z","shell.execute_reply.started":"2025-06-25T02:00:26.74287Z","shell.execute_reply":"2025-06-25T05:34:12.058315Z"}},"outputs":[{"name":"stdout","text":"\nEpoch 1/30\n-------------------------------\n\nTraining Loss per batch = 1.599526\tAccuracy on Training set = 15.838886% [523/3302]\n\nValidation Loss per batch = 1.571344\tAccuracy on Validation set = 46.388889% [167/360]\n\nEpoch 2/30\n-------------------------------\n\nTraining Loss per batch = 1.563235\tAccuracy on Training set = 53.028468% [1751/3302]\n\nValidation Loss per batch = 1.543765\tAccuracy on Validation set = 59.166667% [213/360]\n\nEpoch 3/30\n-------------------------------\n\nTraining Loss per batch = 1.529525\tAccuracy on Training set = 63.113265% [2084/3302]\n\nValidation Loss per batch = 1.506617\tAccuracy on Validation set = 67.222222% [242/360]\n\nEpoch 4/30\n-------------------------------\n\nTraining Loss per batch = 1.486829\tAccuracy on Training set = 66.959419% [2211/3302]\n\nValidation Loss per batch = 1.458021\tAccuracy on Validation set = 68.888889% [248/360]\n\nEpoch 5/30\n-------------------------------\n\nTraining Loss per batch = 1.434681\tAccuracy on Training set = 70.169594% [2317/3302]\n\nValidation Loss per batch = 1.398192\tAccuracy on Validation set = 72.500000% [261/360]\n\nEpoch 6/30\n-------------------------------\n\nTraining Loss per batch = 1.375712\tAccuracy on Training set = 70.956996% [2343/3302]\n\nValidation Loss per batch = 1.337298\tAccuracy on Validation set = 71.666667% [258/360]\n\nEpoch 7/30\n-------------------------------\n\nTraining Loss per batch = 1.318776\tAccuracy on Training set = 70.745003% [2336/3302]\n\nValidation Loss per batch = 1.267703\tAccuracy on Validation set = 70.833333% [255/360]\n\nEpoch 8/30\n-------------------------------\n\nTraining Loss per batch = 1.265646\tAccuracy on Training set = 70.260448% [2320/3302]\n\nValidation Loss per batch = 1.232856\tAccuracy on Validation set = 70.555556% [254/360]\n\nEpoch 9/30\n-------------------------------\n\nTraining Loss per batch = 1.217865\tAccuracy on Training set = 70.623864% [2332/3302]\n\nValidation Loss per batch = 1.175353\tAccuracy on Validation set = 70.555556% [254/360]\n\nEpoch 10/30\n-------------------------------\n\nTraining Loss per batch = 1.174233\tAccuracy on Training set = 70.411872% [2325/3302]\n\nValidation Loss per batch = 1.139752\tAccuracy on Validation set = 70.555556% [254/360]\n\nEpoch 11/30\n-------------------------------\n\nTraining Loss per batch = 1.134670\tAccuracy on Training set = 68.594791% [2265/3302]\n\nValidation Loss per batch = 1.087043\tAccuracy on Validation set = 70.277778% [253/360]\n\nEpoch 12/30\n-------------------------------\n\nTraining Loss per batch = 1.090596\tAccuracy on Training set = 68.079952% [2248/3302]\n\nValidation Loss per batch = 1.087108\tAccuracy on Validation set = 70.833333% [255/360]\n\nEpoch 13/30\n-------------------------------\n\nTraining Loss per batch = 1.049644\tAccuracy on Training set = 67.504543% [2229/3302]\n\nValidation Loss per batch = 1.086005\tAccuracy on Validation set = 67.222222% [242/360]\n\nEpoch 14/30\n-------------------------------\n\nTraining Loss per batch = 1.016583\tAccuracy on Training set = 70.199879% [2318/3302]\n\nValidation Loss per batch = 1.007858\tAccuracy on Validation set = 73.333333% [264/360]\n\nEpoch 15/30\n-------------------------------\n\nTraining Loss per batch = 0.975862\tAccuracy on Training set = 71.532405% [2362/3302]\n\nValidation Loss per batch = 1.043942\tAccuracy on Validation set = 67.500000% [243/360]\n\nEpoch 16/30\n-------------------------------\n\nTraining Loss per batch = 0.938678\tAccuracy on Training set = 70.472441% [2327/3302]\n\nValidation Loss per batch = 0.933654\tAccuracy on Validation set = 74.444444% [268/360]\n\nEpoch 17/30\n-------------------------------\n\nTraining Loss per batch = 0.911195\tAccuracy on Training set = 74.015748% [2444/3302]\n\nValidation Loss per batch = 0.963177\tAccuracy on Validation set = 74.722222% [269/360]\n\nEpoch 18/30\n-------------------------------\n\nTraining Loss per batch = 0.892878\tAccuracy on Training set = 73.531193% [2428/3302]\n\nValidation Loss per batch = 0.911572\tAccuracy on Validation set = 74.444444% [268/360]\n\nEpoch 19/30\n-------------------------------\n\nTraining Loss per batch = 0.867665\tAccuracy on Training set = 73.652332% [2432/3302]\n\nValidation Loss per batch = 0.927280\tAccuracy on Validation set = 74.166667% [267/360]\n\nEpoch 20/30\n-------------------------------\n\nTraining Loss per batch = 0.848701\tAccuracy on Training set = 75.166566% [2482/3302]\n\nValidation Loss per batch = 0.901048\tAccuracy on Validation set = 72.222222% [260/360]\n\nEpoch 21/30\n-------------------------------\n\nTraining Loss per batch = 0.835462\tAccuracy on Training set = 75.499697% [2493/3302]\n\nValidation Loss per batch = 0.880633\tAccuracy on Validation set = 73.888889% [266/360]\n\nEpoch 22/30\n-------------------------------\n\nTraining Loss per batch = 0.815989\tAccuracy on Training set = 76.589945% [2529/3302]\n\nValidation Loss per batch = 0.858037\tAccuracy on Validation set = 75.833333% [273/360]\n\nEpoch 23/30\n-------------------------------\n\nTraining Loss per batch = 0.795760\tAccuracy on Training set = 76.226529% [2517/3302]\n\nValidation Loss per batch = 0.881931\tAccuracy on Validation set = 75.555556% [272/360]\n\nEpoch 24/30\n-------------------------------\n\nTraining Loss per batch = 0.760621\tAccuracy on Training set = 77.528770% [2560/3302]\n\nValidation Loss per batch = 0.861398\tAccuracy on Validation set = 74.722222% [269/360]\n\nEpoch 25/30\n-------------------------------\n\nTraining Loss per batch = 0.752376\tAccuracy on Training set = 77.225924% [2550/3302]\n\nValidation Loss per batch = 0.856614\tAccuracy on Validation set = 76.111111% [274/360]\n\nEpoch 26/30\n-------------------------------\n\nTraining Loss per batch = 0.734045\tAccuracy on Training set = 78.740157% [2600/3302]\n\nValidation Loss per batch = 0.887669\tAccuracy on Validation set = 74.444444% [268/360]\n\nEpoch 27/30\n-------------------------------\n\nTraining Loss per batch = 0.702742\tAccuracy on Training set = 80.042399% [2643/3302]\n\nValidation Loss per batch = 0.801416\tAccuracy on Validation set = 78.333333% [282/360]\n\nEpoch 28/30\n-------------------------------\n\nTraining Loss per batch = 0.680404\tAccuracy on Training set = 79.830406% [2636/3302]\n\nValidation Loss per batch = 0.874340\tAccuracy on Validation set = 76.111111% [274/360]\n\nEpoch 29/30\n-------------------------------\n\nTraining Loss per batch = 0.660467\tAccuracy on Training set = 80.375530% [2654/3302]\n\nValidation Loss per batch = 0.879183\tAccuracy on Validation set = 77.777778% [280/360]\n\nEpoch 30/30\n-------------------------------\n\nTraining Loss per batch = 0.644565\tAccuracy on Training set = 81.072078% [2677/3302]\n\nValidation Loss per batch = 0.809626\tAccuracy on Validation set = 75.000000% [270/360]\n\nTraining has completed!\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"#Plot the graph of train_losses & valid_losses against nb_epochs.\nepochs = range(nb_epochs)\nplt.plot(epochs, train_losses, 'g', label='Training loss')\nplt.plot(epochs, valid_losses, 'b', label='validation loss')\nplt.title('Training and Validation loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T05:35:49.332025Z","iopub.execute_input":"2025-06-25T05:35:49.332325Z","iopub.status.idle":"2025-06-25T05:35:49.491306Z","shell.execute_reply.started":"2025-06-25T05:35:49.332306Z","shell.execute_reply":"2025-06-25T05:35:49.490507Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"test_set = dataset(test_df,f'{path}test_images',image_transform = image_transform,train = False )\n\ntest_dataloader = DataLoader(test_set, batch_size=32, shuffle=False) #DataLoader for test_set.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T05:36:10.747159Z","iopub.execute_input":"2025-06-25T05:36:10.74776Z","iopub.status.idle":"2025-06-25T05:36:10.751754Z","shell.execute_reply.started":"2025-06-25T05:36:10.747735Z","shell.execute_reply":"2025-06-25T05:36:10.751037Z"}},"outputs":[],"execution_count":17},{"cell_type":"code","source":"def test(dataloader,model):\n    '''\n    test function predicts the labels given an image batches.\n    \n    Args :\n         dataloader: DataLoader for the test_set.\n         model: Given an input produces an output by multiplying the input with the model weights.\n         \n    Returns :\n         List of predicted labels.\n    '''\n    \n    model.eval() #Sets the model for evaluation.\n    \n    labels = [] #List to store the predicted labels.\n    \n    with torch.no_grad():\n        \n        for batch,x in enumerate(dataloader):\n            \n            output = model(x.to(device))\n            \n            predictions = output.argmax(dim=1).cpu().detach().tolist() #Predicted labels for an image batch.\n            labels.extend(predictions)\n                \n    print('Testing has completed')\n            \n    return labels ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T05:36:31.861935Z","iopub.execute_input":"2025-06-25T05:36:31.862586Z","iopub.status.idle":"2025-06-25T05:36:31.868583Z","shell.execute_reply.started":"2025-06-25T05:36:31.862556Z","shell.execute_reply":"2025-06-25T05:36:31.867797Z"}},"outputs":[],"execution_count":18},{"cell_type":"code","source":"labels = test(test_dataloader,model) #Calls the test function.","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-06-25T05:36:52.262371Z","iopub.execute_input":"2025-06-25T05:36:52.262933Z","iopub.status.idle":"2025-06-25T05:38:50.926536Z","shell.execute_reply.started":"2025-06-25T05:36:52.262912Z","shell.execute_reply":"2025-06-25T05:38:50.925805Z"}},"outputs":[{"name":"stdout","text":"Testing has completed\n","output_type":"stream"}],"execution_count":19}]}