{"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":"gpu","dataSources":[{"sourceId":7327,"databundleVersionId":861871,"sourceType":"competition"}],"dockerImageVersionId":30683,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Summary\n\nThis notebook explores a hybrid approach for identifying dog breeds utilizing the feature extraction capabilities of Inception V3 and ResNet50 pretrained models. The model incorporates a classifier layer with a dropout rate of 0.7 to enhance generalization and mitigate overfitting.\n\n## Methodology:\n\n* **Preprocessing:** Input images undergo resizing to ensure uniform dimensions. Data augmentation techniques, such as horizontal and vertical flipping with a probability of 0.2, are applied to increase the diversity of training data and improve model generalization.\n* **Feature Extraction:** Inception V3 and ResNet50 pretrained models are utilized to extract high-level features from the input images.\n* **Classifier Layer:** The extracted features are fed into a classifier layer with a dropout rate of 0.7. \n* **Training:** The model is trained using a SGD optimization algorithm with a learning rate of 0.005, and Cross Entropy Loss. Training involves fine-tuning the classifier layer while keeping the parameters of the pretrained models frozen.\n* **Evaluation:** The model's performance is assessed based on validation accuracy and loss metrics.\n","metadata":{}},{"cell_type":"code","source":"import os\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\nfrom PIL import Image\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import precision_score, recall_score, f1_score\n\n# Visualization\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n# PyTorch\n\nimport torch as T\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import models\nfrom torchvision import transforms","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:44.662957Z","iopub.execute_input":"2024-04-27T19:06:44.663252Z","iopub.status.idle":"2024-04-27T19:06:44.670787Z","shell.execute_reply.started":"2024-04-27T19:06:44.663227Z","shell.execute_reply":"2024-04-27T19:06:44.669861Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"Configure GPU","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:44.676212Z","iopub.execute_input":"2024-04-27T19:06:44.677006Z","iopub.status.idle":"2024-04-27T19:06:44.685111Z","shell.execute_reply.started":"2024-04-27T19:06:44.676974Z","shell.execute_reply":"2024-04-27T19:06:44.683931Z"},"trusted":true},"execution_count":27,"outputs":[{"traceback":["\u001b[0;36m Cell \u001b[0;32mIn[27], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m Configure GPU\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"],"ename":"SyntaxError","evalue":"invalid syntax (1998618575.py, line 1)","output_type":"error"}]},{"cell_type":"code","source":"device = T.device('cuda' if T.cuda.is_available() else 'cpu')","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:44.689729Z","iopub.execute_input":"2024-04-27T19:06:44.690027Z","iopub.status.idle":"2024-04-27T19:06:44.695241Z","shell.execute_reply.started":"2024-04-27T19:06:44.690004Z","shell.execute_reply":"2024-04-27T19:06:44.694217Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"markdown","source":"# Loading dataset","metadata":{}},{"cell_type":"code","source":"# Read csv file\ntrain_data = pd.read_csv(\"/kaggle/input/dog-breed-identification/labels.csv\")\n# Train data shape\nprint(f\"Train dataset shape: {train_data.shape}\")\n# Sample of the train_data DataFrame\ntrain_data.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:44.696712Z","iopub.execute_input":"2024-04-27T19:06:44.697847Z","iopub.status.idle":"2024-04-27T19:06:44.736519Z","shell.execute_reply.started":"2024-04-27T19:06:44.697814Z","shell.execute_reply":"2024-04-27T19:06:44.735629Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"Train dataset shape: (10222, 2)\n","output_type":"stream"},{"execution_count":29,"output_type":"execute_result","data":{"text/plain":" id breed\n0 000bec180eb18c7604dcecc8fe0dba07 boston_bull\n1 001513dfcb2ffafc82cccf4d8bbaba97 dingo\n2 001cdf01b096e06d78e9e5112d419397 pekinese\n3 00214f311d5d2247d5dfe4fe24b2303d bluetick\n4 0021f9ceb3235effd7fcde7f7538ed62 golden_retriever","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idbreed
0000bec180eb18c7604dcecc8fe0dba07boston_bull
1001513dfcb2ffafc82cccf4d8bbaba97dingo
2001cdf01b096e06d78e9e5112d419397pekinese
300214f311d5d2247d5dfe4fe24b2303dbluetick
40021f9ceb3235effd7fcde7f7538ed62golden_retriever
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"## Distribution of the breed classes","metadata":{}},{"cell_type":"code","source":"breed_classes = train_data.breed.value_counts().reset_index()\nplt.figure(figsize=(20,8))\nsns.barplot(breed_classes, x='breed', y='count', palette=\"flare\")\nplt.xticks(rotation=90)\nplt.title(\"Distribution of the breed classes\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:44.738216Z","iopub.execute_input":"2024-04-27T19:06:44.738534Z","iopub.status.idle":"2024-04-27T19:06:46.239344Z","shell.execute_reply.started":"2024-04-27T19:06:44.738504Z","shell.execute_reply":"2024-04-27T19:06:46.236455Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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120.000000\nmean 85.183333\nstd 13.298122\nmin 66.000000\n25% 75.000000\n50% 82.000000\n75% 91.250000\nmax 126.000000\nName: count, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"Confirm the 120 dog breed classes","metadata":{}},{"cell_type":"code","source":"breed_classes['breed'].nunique()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.257194Z","iopub.execute_input":"2024-04-27T19:06:46.257462Z","iopub.status.idle":"2024-04-27T19:06:46.265743Z","shell.execute_reply.started":"2024-04-27T19:06:46.25744Z","shell.execute_reply":"2024-04-27T19:06:46.264667Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"120"},"metadata":{}}]},{"cell_type":"markdown","source":"# Label Encoding","metadata":{}},{"cell_type":"code","source":"le = LabelEncoder()\ntrain_data.loc[:,'breed'] = le.fit_transform(train_data.loc[:,'breed']) ","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.267854Z","iopub.execute_input":"2024-04-27T19:06:46.268443Z","iopub.status.idle":"2024-04-27T19:06:46.278263Z","shell.execute_reply.started":"2024-04-27T19:06:46.268418Z","shell.execute_reply":"2024-04-27T19:06:46.277399Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"train_data.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.279264Z","iopub.execute_input":"2024-04-27T19:06:46.279538Z","iopub.status.idle":"2024-04-27T19:06:46.293272Z","shell.execute_reply.started":"2024-04-27T19:06:46.279516Z","shell.execute_reply":"2024-04-27T19:06:46.292412Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":" id breed\n0 000bec180eb18c7604dcecc8fe0dba07 19\n1 001513dfcb2ffafc82cccf4d8bbaba97 37\n2 001cdf01b096e06d78e9e5112d419397 85\n3 00214f311d5d2247d5dfe4fe24b2303d 15\n4 0021f9ceb3235effd7fcde7f7538ed62 49","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
idbreed
0000bec180eb18c7604dcecc8fe0dba0719
1001513dfcb2ffafc82cccf4d8bbaba9737
2001cdf01b096e06d78e9e5112d41939785
300214f311d5d2247d5dfe4fe24b2303d15
40021f9ceb3235effd7fcde7f7538ed6249
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Dog Breed Dataset","metadata":{}},{"cell_type":"code","source":"class Dog_Breed_Dataset(Dataset):\n \n def __init__(self, df: pd.DataFrame, img_base_path: str, split: str, transforms = None): \n self.df = df\n self.img_base_path = img_base_path\n self.split = split\n self.transforms = transforms\n \n def __getitem__(self, index):\n # Path of the image\n img_path = os.path.join(self.img_base_path + self.df.loc[index,'id'] + '.jpg')\n # Read the image\n img = Image.open(img_path) \n # Perform the transformations\n if self.transforms:\n img = self.transforms(img)\n \n if self.split != 'test':\n y = self.df.loc[index, 'breed'] \n return img, y\n else: \n return img\n \n def __len__(self):\n return len(self.df) ","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.294338Z","iopub.execute_input":"2024-04-27T19:06:46.294624Z","iopub.status.idle":"2024-04-27T19:06:46.303799Z","shell.execute_reply.started":"2024-04-27T19:06:46.294595Z","shell.execute_reply":"2024-04-27T19:06:46.302847Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"# Data Augmentation","metadata":{}},{"cell_type":"code","source":"# train_transforms = transforms.Compose([\n# transforms.Resize((224,224)),\n# transforms.RandomHorizontalFlip(p=0.2),\n# transforms.RandomVerticalFlip(p=0.2),\n# transforms.ToTensor(),\n# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n# ])\n# test_transforms = transforms.Compose([\n# transforms.Resize((224,224)), \n# transforms.ToTensor(),\n# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n# ])\n","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.305561Z","iopub.execute_input":"2024-04-27T19:06:46.305913Z","iopub.status.idle":"2024-04-27T19:06:46.313358Z","shell.execute_reply.started":"2024-04-27T19:06:46.305869Z","shell.execute_reply":"2024-04-27T19:06:46.312436Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":"## Get Data Loaders","metadata":{}},{"cell_type":"markdown","source":"### Split the dataset","metadata":{}},{"cell_type":"code","source":"train, val = train_test_split(train_data, test_size=0.2, random_state=42, stratify=train_data['breed'])\n\ntrain = train.reset_index(drop=True)\nval = val.reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.31498Z","iopub.execute_input":"2024-04-27T19:06:46.315343Z","iopub.status.idle":"2024-04-27T19:06:46.343268Z","shell.execute_reply.started":"2024-04-27T19:06:46.31531Z","shell.execute_reply":"2024-04-27T19:06:46.342357Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":"### Get the data loaders","metadata":{}},{"cell_type":"code","source":"train_dataset = Dog_Breed_Dataset(\n df=train,\n img_base_path='/kaggle/input/dog-breed-identification/train/',\n split='train'\n)\nvalidation_dataset = Dog_Breed_Dataset(\n df=val,\n img_base_path='/kaggle/input/dog-breed-identification/train/',\n split='val'\n \n)\n\ntrain_dl = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)\nvalidation_dl = DataLoader(validation_dataset, batch_size=64, shuffle=False, num_workers=4) ","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.34846Z","iopub.execute_input":"2024-04-27T19:06:46.348709Z","iopub.status.idle":"2024-04-27T19:06:46.355142Z","shell.execute_reply.started":"2024-04-27T19:06:46.348688Z","shell.execute_reply":"2024-04-27T19:06:46.354049Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"print(f\"Train data length: {len(train_dl.dataset)}, Validation data length: {len(validation_dl.dataset)}\")","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.356377Z","iopub.execute_input":"2024-04-27T19:06:46.356699Z","iopub.status.idle":"2024-04-27T19:06:46.366131Z","shell.execute_reply.started":"2024-04-27T19:06:46.356659Z","shell.execute_reply":"2024-04-27T19:06:46.365164Z"},"trusted":true},"execution_count":39,"outputs":[{"name":"stdout","text":"Train data length: 8177, Validation data length: 2045\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Training process","metadata":{}},{"cell_type":"markdown","source":"## Training function","metadata":{}},{"cell_type":"code","source":"def train_model(train_dl, val_dl, model, epochs=15): \n \n train_acc_history = []\n val_acc_history = []\n train_loss_history = []\n val_loss_history = []\n # Best validation accuracy\n best_val_loss = 1_000_000.0 \n # Get initial weights\n weights = model.get_weights()\n \n for epoch in range(epochs):\n print(\"=\"*15, \"Epoch: \", str(epoch), \"=\"*15)\n \n train_correct_pred = 0\n val_correct_pred = 0\n train_acc = 0\n val_acc = 0\n train_loss = 0\n val_loss = 0\n \n # Set to training mode\n model.train()\n \n for x, y in train_dl: \n # Convert data to Tensor \n x = x.clone().detach().to(device).requires_grad_(True)\n y = y.clone().detach().long().to(device)\n # Reset gradients\n model.optim.zero_grad()\n # Predict\n preds = model(x) \n \n # Compute the loss \n loss = model.criterion(preds,y) \n \n # Compute the gradients \n loss.backward()\n # Update weights\n model.optim.step()\n # Count the correct predictions\n preds = T.argmax(preds, dim=1) \n train_correct_pred += (preds.long().unsqueeze(1) == y.unsqueeze(1)).sum().item()\n \n train_loss += loss.item() \n \n train_acc = train_correct_pred / len(train_dl.dataset)\n \n train_acc_history.append(train_acc)\n \n train_loss_history.append(train_loss)\n \n # Switch to evaluation mode\n model.eval() \n \n with T.no_grad():\n for x, y in val_dl: \n # Convert data to Tensor \n x = x.clone().detach().to(device)\n y = y.clone().detach().long().to(device) \n # Predict\n preds = model(x) \n # Compute the loss\n loss = model.criterion(preds,y) \n \n val_loss += loss.item() \n # Count the correct predictions\n preds = T.argmax(preds, dim=1)\n \n val_correct_pred += (preds.long().unsqueeze(1) == y.unsqueeze(1)).sum().item() \n \n model.scheduler.step() \n \n val_acc = val_correct_pred / len(val_dl.dataset)\n \n val_acc_history.append(val_acc)\n val_loss_history.append(val_loss) \n # Save the weights of the best model\n if best_val_loss > val_loss:\n best_val_loss = val_loss\n weights = model.get_weights()\n \n print(\"Train acc: {:.4f} | Train Loss: {:.4f} | Validation acc: {:.4f} | Validation Loss: {:.4f}\".format(train_acc, train_loss, val_acc, val_loss))\n # Load best model\n model.load_weights(weights)\n \n return [train_acc_history, train_loss_history, val_acc_history, val_loss_history], model","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.36767Z","iopub.execute_input":"2024-04-27T19:06:46.368083Z","iopub.status.idle":"2024-04-27T19:06:46.386828Z","shell.execute_reply.started":"2024-04-27T19:06:46.368051Z","shell.execute_reply":"2024-04-27T19:06:46.385885Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"markdown","source":"## Models","metadata":{}},{"cell_type":"markdown","source":"Inception model","metadata":{}},{"cell_type":"code","source":"inception = models.inception_v3(pretrained=True)\n\ninception_model = nn.Sequential(\n inception.Conv2d_1a_3x3,\n inception.Conv2d_2a_3x3,\n inception.Conv2d_2b_3x3,\n inception.maxpool1,\n inception.Conv2d_3b_1x1,\n inception.Conv2d_4a_3x3,\n inception.maxpool2,\n inception.Mixed_5b,\n inception.Mixed_5c,\n inception.Mixed_5d,\n inception.Mixed_6a,\n inception.Mixed_6b,\n inception.Mixed_6c,\n inception.Mixed_6d,\n inception.Mixed_6e,\n inception.Mixed_7a,\n inception.Mixed_7b,\n inception.Mixed_7c,\n inception.avgpool\n)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.387799Z","iopub.execute_input":"2024-04-27T19:06:46.388047Z","iopub.status.idle":"2024-04-27T19:06:46.791512Z","shell.execute_reply.started":"2024-04-27T19:06:46.388026Z","shell.execute_reply":"2024-04-27T19:06:46.790473Z"},"trusted":true},"execution_count":41,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-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/opt/conda/lib/python3.10/site-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=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"Resnet50 model","metadata":{}},{"cell_type":"code","source":"resnet50 = models.resnet50(pretrained=True)\n\nresnet50_model = nn.Sequential(\n resnet50.conv1,\n resnet50.bn1,\n resnet50.relu,\n resnet50.maxpool,\n resnet50.layer1,\n resnet50.layer2,\n resnet50.layer3,\n resnet50.layer4,\n resnet50.avgpool\n)\n","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:46.792662Z","iopub.execute_input":"2024-04-27T19:06:46.792956Z","iopub.status.idle":"2024-04-27T19:06:47.325148Z","shell.execute_reply.started":"2024-04-27T19:06:46.792932Z","shell.execute_reply":"2024-04-27T19:06:47.324102Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n warnings.warn(msg)\n","output_type":"stream"}]},{"cell_type":"code","source":"# Freeze parameters of pretrained models\nfor param in resnet50_model.parameters(): \n param.requires_grad = False\n \nfor param in inception_model.parameters(): \n param.requires_grad = False","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.326249Z","iopub.execute_input":"2024-04-27T19:06:47.326553Z","iopub.status.idle":"2024-04-27T19:06:47.334413Z","shell.execute_reply.started":"2024-04-27T19:06:47.326528Z","shell.execute_reply":"2024-04-27T19:06:47.333417Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"markdown","source":"### Model class","metadata":{}},{"cell_type":"code","source":"class Model(nn.Module):\n \n def __init__(self, inception_model, resnet50_model):\n super(Model,self).__init__()\n \n self.inception_model = inception_model\n self.resnet50_model = resnet50_model \n \n self.output = nn.Sequential(\n nn.Dropout(0.7),\n nn.Linear(4096,120) \n )\n \n self.to(device)\n # Optimizer \n self.optim = T.optim.SGD(self.output.parameters(), lr=0.001, momentum=0.9)\n # Loss\n self.criterion = T.nn.CrossEntropyLoss()\n # Scheduler\n self.scheduler = T.optim.lr_scheduler.StepLR(self.optim, step_size=7, gamma=0.1)\n \n def forward(self, x):\n X1 = self.inception_model(x)\n X2 = self.resnet50_model(x)\n \n X1 = X1.view(X1.size(0), -1)\n X2 = X2.view(X2.size(0), -1)\n \n X = T.cat([X1, X2], dim=1)\n \n P = self.output(X) \n \n return P\n \n def get_weights(self):\n return self.output.state_dict()\n \n def load_weights(self, weights):\n self.output.load_state_dict(weights)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.335408Z","iopub.execute_input":"2024-04-27T19:06:47.335716Z","iopub.status.idle":"2024-04-27T19:06:47.346952Z","shell.execute_reply.started":"2024-04-27T19:06:47.335693Z","shell.execute_reply":"2024-04-27T19:06:47.346133Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"## Training model","metadata":{}},{"cell_type":"code","source":"model = Model(inception_model, resnet50_model)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.348329Z","iopub.execute_input":"2024-04-27T19:06:47.348658Z","iopub.status.idle":"2024-04-27T19:06:47.447877Z","shell.execute_reply.started":"2024-04-27T19:06:47.348617Z","shell.execute_reply":"2024-04-27T19:06:47.447046Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"code","source":"history, model = train_model(train_dl, validation_dl, model)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.448774Z","iopub.execute_input":"2024-04-27T19:06:47.449039Z","iopub.status.idle":"2024-04-27T19:06:47.93655Z","shell.execute_reply.started":"2024-04-27T19:06:47.449017Z","shell.execute_reply":"2024-04-27T19:06:47.933416Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"=============== Epoch: 0 ===============\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[46], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m history, model \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_dl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[40], line 25\u001b[0m, in \u001b[0;36mtrain_model\u001b[0;34m(train_dl, val_dl, model, epochs)\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;66;03m# Set to training mode\u001b[39;00m\n\u001b[1;32m 23\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[0;32m---> 25\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x, y \u001b[38;5;129;01min\u001b[39;00m train_dl: \n\u001b[1;32m 26\u001b[0m \u001b[38;5;66;03m# Convert data to Tensor \u001b[39;00m\n\u001b[1;32m 27\u001b[0m x \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mclone()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mto(device)\u001b[38;5;241m.\u001b[39mrequires_grad_(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 28\u001b[0m y \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mclone()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mlong()\u001b[38;5;241m.\u001b[39mto(device)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 628\u001b[0m \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m 629\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset() \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 630\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 633\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m 634\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1345\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1343\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1344\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_task_info[idx]\n\u001b[0;32m-> 1345\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1371\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 1369\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_put_index()\n\u001b[1;32m 1370\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ExceptionWrapper):\n\u001b[0;32m-> 1371\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1372\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/_utils.py:694\u001b[0m, in \u001b[0;36mExceptionWrapper.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 690\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 691\u001b[0m \u001b[38;5;66;03m# If the exception takes multiple arguments, don't try to\u001b[39;00m\n\u001b[1;32m 692\u001b[0m \u001b[38;5;66;03m# instantiate since we don't know how to\u001b[39;00m\n\u001b[1;32m 693\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 694\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n","\u001b[0;31mTypeError\u001b[0m: Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\n data = fetcher.fetch(index)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 54, in fetch\n return self.collate_fn(data)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 265, in default_collate\n return collate(batch, collate_fn_map=default_collate_fn_map)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 142, in collate\n return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 142, in \n return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 150, in collate\n raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found \n"],"ename":"TypeError","evalue":"Caught TypeError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loop\n data = fetcher.fetch(index)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 54, in fetch\n return self.collate_fn(data)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 265, in default_collate\n return collate(batch, collate_fn_map=default_collate_fn_map)\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 142, in collate\n return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 142, in \n return [collate(samples, collate_fn_map=collate_fn_map) for samples in transposed] # Backwards compatibility.\n File \"/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/collate.py\", line 150, in collate\n raise TypeError(default_collate_err_msg_format.format(elem_type))\nTypeError: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found \n","output_type":"error"}]},{"cell_type":"code","source":"# Training and Validation Results\nfig, axs = plt.subplots(1,2, figsize=(15,6))\naxs[0].plot(range(15), history[0], label=\"Training accuracy\")\naxs[0].plot(range(15), history[2], label=\"Validation accuracy\")\naxs[0].set_xlabel('Epoch')\naxs[0].set_ylabel('Accuracy')\naxs[0].grid(True)\n\naxs[1].plot(range(15), history[1], label=\"Training Loss\")\naxs[1].plot(range(15), history[3], label=\"Validation Loss\")\naxs[1].set_xlabel('Epoch')\naxs[1].set_ylabel('Loss')\naxs[1].grid(True)\n\nplt.suptitle(\"Training and Validation Results of Model\")\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.938131Z","iopub.status.idle":"2024-04-27T19:06:47.938655Z","shell.execute_reply.started":"2024-04-27T19:06:47.938391Z","shell.execute_reply":"2024-04-27T19:06:47.938417Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nfrom sklearn.metrics import confusion_matrix\n\n# Set model to evaluation mode\nmodel.eval()\n\n# Lists to store true labels and predicted labels\ntrue_labels = []\npred_labels = []\n\n# Iterate over validation data loader\nwith torch.no_grad():\n for images, labels in validation_dl:\n images = images.to(device)\n labels = labels.to(device)\n \n # Forward pass\n outputs = model(images)\n _, predicted = torch.max(outputs, 1)\n \n # Append true labels and predicted labels\n true_labels.extend(labels.cpu().numpy())\n pred_labels.extend(predicted.cpu().numpy())\n\n# Compute confusion matrix\nconf_matrix = confusion_matrix(true_labels, pred_labels)\n\n# Plot confusion matrix\nplt.figure(figsize=(12, 10))\nsns.heatmap(conf_matrix, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\nplt.xlabel(\"Predicted labels\")\nplt.ylabel(\"True labels\")\nplt.title(\"Confusion Matrix\")\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.940355Z","iopub.status.idle":"2024-04-27T19:06:47.940866Z","shell.execute_reply.started":"2024-04-27T19:06:47.940598Z","shell.execute_reply":"2024-04-27T19:06:47.940618Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import precision_score, recall_score, f1_score\n\n# Set model to evaluation mode\nmodel.eval()\n\n# Lists to store true labels and predicted labels\ntrue_labels = []\npred_labels = []\n\n# Iterate over validation data loader\nwith torch.no_grad():\n for images, labels in validation_dl:\n images = images.to(device)\n labels = labels.to(device)\n \n # Forward pass\n outputs = model(images)\n _, predicted = torch.max(outputs, 1)\n \n # Append true labels and predicted labels\n true_labels.extend(labels.cpu().numpy())\n pred_labels.extend(predicted.cpu().numpy())\n\n# Calculate precision, recall, and F1 score\nprecision = precision_score(true_labels, pred_labels, average='weighted')\nrecall = recall_score(true_labels, pred_labels, average='weighted')\nf1 = f1_score(true_labels, pred_labels, average='weighted')\n\nprint(f'Precision: {precision:.4f}')\nprint(f'Recall: {recall:.4f}')\nprint(f'F1 Score: {f1:.4f}')\n","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.942398Z","iopub.status.idle":"2024-04-27T19:06:47.942929Z","shell.execute_reply.started":"2024-04-27T19:06:47.942627Z","shell.execute_reply":"2024-04-27T19:06:47.942654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{}},{"cell_type":"markdown","source":"## Read test data","metadata":{}},{"cell_type":"code","source":"test_data = pd.DataFrame([])\nfor dirname, _, filename in os.walk('/kaggle/input/dog-breed-identification/test/'):\n filename = pd.Series(filename)\n test_data = pd.concat([test_data, filename], axis=0)\ntest_data.columns = ['id']\ntest_data['id'] = test_data['id'].str.replace(\".jpg\",\"\")","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.944209Z","iopub.status.idle":"2024-04-27T19:06:47.944698Z","shell.execute_reply.started":"2024-04-27T19:06:47.944434Z","shell.execute_reply":"2024-04-27T19:06:47.944453Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Dataset shape\nprint(f\"Test dataset shape: {test_data.shape}\")\n# Sample of the train_data DataFrame\ntest_data.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.946278Z","iopub.status.idle":"2024-04-27T19:06:47.94711Z","shell.execute_reply.started":"2024-04-27T19:06:47.946806Z","shell.execute_reply":"2024-04-27T19:06:47.946826Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Get Test Data Loader","metadata":{}},{"cell_type":"code","source":"test_dataset = Dog_Breed_Dataset(\n df=test_data,\n img_base_path='/kaggle/input/dog-breed-identification/test/',\n split='test',\n transforms=test_transforms\n)\n\ntest_dl = DataLoader(test_dataset, batch_size=64, shuffle=False, num_workers=4)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.948441Z","iopub.status.idle":"2024-04-27T19:06:47.948875Z","shell.execute_reply.started":"2024-04-27T19:06:47.948651Z","shell.execute_reply":"2024-04-27T19:06:47.948669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def test_model(test_dl, model): \n \n # Predictions DataFrame\n prob_preds = pd.DataFrame([])\n \n for x in test_dl:\n # Convert data to Tensor \n x = x.clone().detach().to(device) \n # Predict\n pred = model(x) \n prob_pred = T.nn.functional.softmax(pred, dim=1)\n \n prob_pred = prob_pred.detach().cpu().numpy() \n prob_pred = pd.DataFrame(prob_pred)\n \n prob_preds = pd.concat([prob_preds, prob_pred], axis=0) \n \n return prob_preds ","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.94996Z","iopub.status.idle":"2024-04-27T19:06:47.95033Z","shell.execute_reply.started":"2024-04-27T19:06:47.95014Z","shell.execute_reply":"2024-04-27T19:06:47.950171Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds = test_model(test_dl, model)\ntest_preds.shape","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.951875Z","iopub.status.idle":"2024-04-27T19:06:47.952336Z","shell.execute_reply.started":"2024-04-27T19:06:47.952103Z","shell.execute_reply":"2024-04-27T19:06:47.952122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.953673Z","iopub.status.idle":"2024-04-27T19:06:47.954198Z","shell.execute_reply.started":"2024-04-27T19:06:47.953929Z","shell.execute_reply":"2024-04-27T19:06:47.953951Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set columns to breed names\nnum_classes = []\nfor num_class in test_preds.columns:\n num_classes.append(num_class)\n\nnum_classes = np.array(num_classes)\nnum_classes = le.inverse_transform(num_classes)\ntest_preds.columns = list(num_classes)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.955722Z","iopub.status.idle":"2024-04-27T19:06:47.956203Z","shell.execute_reply.started":"2024-04-27T19:06:47.955954Z","shell.execute_reply":"2024-04-27T19:06:47.955972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.957576Z","iopub.status.idle":"2024-04-27T19:06:47.958172Z","shell.execute_reply.started":"2024-04-27T19:06:47.957913Z","shell.execute_reply":"2024-04-27T19:06:47.957937Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set id column\ntest_preds = test_preds.reset_index(drop=True)\nids = test_data.loc[:,'id']\ntest_preds = pd.concat([ids, test_preds], axis=1)\ntest_preds.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.959744Z","iopub.status.idle":"2024-04-27T19:06:47.960456Z","shell.execute_reply.started":"2024-04-27T19:06:47.960213Z","shell.execute_reply":"2024-04-27T19:06:47.960235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds.to_csv('submission.csv', index=None)","metadata":{"execution":{"iopub.status.busy":"2024-04-27T19:06:47.961425Z","iopub.status.idle":"2024-04-27T19:06:47.961888Z","shell.execute_reply.started":"2024-04-27T19:06:47.961632Z","shell.execute_reply":"2024-04-27T19:06:47.961651Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}