{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":34426,"databundleVersionId":3218632,"sourceType":"competition"}],"dockerImageVersionId":30733,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input/'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-06-09T05:58:08.091823Z","iopub.execute_input":"2024-06-09T05:58:08.092227Z","iopub.status.idle":"2024-06-09T05:58:29.202425Z","shell.execute_reply.started":"2024-06-09T05:58:08.092196Z","shell.execute_reply":"2024-06-09T05:58:29.201240Z"}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Cat and Dog Classification using CNN-KAN Method\n\n### Description of Kolmogorov-Arnold Network (KAN)\n\nThe paper: https://arxiv.org/abs/2404.19756\n\nThe Kolmogorov-Arnold Network (KAN) is a type of artificial neural network based on the Kolmogorov-Arnold Representation Theorem. This network has distinct characteristics in its structure and operation, differing from Multi-Layer Perceptrons (MLPs). KAN features the following:\n\n1. **Kolmogorov-Arnold Representation Theorem**:\n   \n    ![image.png](attachment:8559badc-31c3-4678-b7a2-6088296a186e.png)\n   \n   \n2. **Learnable Activations on Edges**:\n   - Unlike MLPs, which have fixed activations on nodes, KAN has learnable activations on edges.\n\n3. **Sum Operation on Nodes**:\n   - KAN combines the results of several sum operations performed on nodes.\n\n### Comparison Between KAN and MLP\n\n#### Similarities\n- **Both models** can be used for universal approximation of continuous functions.\n- **Both models** consist of layers that can be combined to form deep networks.\n\n#### Differences\n1. **Activation Structure**:\n   - **MLP**: Uses fixed activation functions on nodes.\n   - **KAN**: Uses learnable activation functions on edges.\n\n2. **Learning and Operation Locations**:\n   - **MLP**: Learnable weights are on the edges between layers, with fixed activation functions.\n   - **KAN**: The activation functions themselves are on the edges and learnable, with sum operations on nodes.\n\n3. **Mathematical Formulas**:\n\n   ![image.png](attachment:5740f37b-314b-4754-b47d-68b03e154bb3.png)\n\n4. **Model Visualization**:\n   - **MLP Shallow**: Depicted as a network with a single layer of fixed activations on nodes and weights on edges.\n   - **KAN Shallow**: Depicted as a network with learnable functions on edges and sum operations on nodes.\n   - **MLP Deep**: A network with multiple layers, each layer having weights and fixed activation functions.\n   - **KAN Deep**: A network with multiple layers, but the learnable functions are on the edges.\n\n### Conclusion\nKAN and MLP are two different approaches to building neural networks with universal approximation capabilities. KAN offers higher flexibility in terms of having learnable activation functions on edges compared to MLPs, which use fixed activation functions on nodes. This makes KAN more adaptive in some cases, although with additional complexity in 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"},"5740f37b-314b-4754-b47d-68b03e154bb3.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"\n\n<p align=\"center\">\n  <a href=\"url\"><img src=\"https://www.americanhumane.org/app/uploads/2016/08/shutterstock_162633491.jpg\" align=\"middle\" height=\"383\" width=\"640\" ></a>\n</p>","metadata":{}},{"cell_type":"code","source":"!nvidia-smi","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:16:18.414462Z","iopub.execute_input":"2024-06-09T06:16:18.415191Z","iopub.status.idle":"2024-06-09T06:16:19.468089Z","shell.execute_reply.started":"2024-06-09T06:16:18.415161Z","shell.execute_reply":"2024-06-09T06:16:19.466975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Checking the Directory Cat and Dog","metadata":{}},{"cell_type":"code","source":"# 000-dataset-catdog-25000\n!ls '/kaggle/input/dog-vs-cat-classification'","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:16:41.614162Z","iopub.execute_input":"2024-06-09T06:16:41.614524Z","iopub.status.idle":"2024-06-09T06:16:42.554637Z","shell.execute_reply.started":"2024-06-09T06:16:41.614485Z","shell.execute_reply":"2024-06-09T06:16:42.553551Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Spliting data training and data validation","metadata":{}},{"cell_type":"code","source":"import os\nimport shutil\nfrom sklearn.model_selection import train_test_split\n\n# Directory with our training cat pictures\ntrain_cat_dir = os.path.join('/kaggle/input/dog-vs-cat-classification/train/train/cats')\n# Directory with our training dog pictures\ntrain_dog_dir = os.path.join('/kaggle/input/dog-vs-cat-classification/train/train/dogs')\n\n# Create output directories for training and validation data\noutput_base_dir = './split_data'\ntrain_cat_output_dir = os.path.join(output_base_dir, 'train/cats')\ntrain_dog_output_dir = os.path.join(output_base_dir, 'train/dogs')\nvalidation_cat_output_dir = os.path.join(output_base_dir, 'validation/cats')\nvalidation_dog_output_dir = os.path.join(output_base_dir, 'validation/dogs')\n\nos.makedirs(train_cat_output_dir, exist_ok=True)\nos.makedirs(train_dog_output_dir, exist_ok=True)\nos.makedirs(validation_cat_output_dir, exist_ok=True)\nos.makedirs(validation_dog_output_dir, exist_ok=True)\n\n# List all cat and dog images\ncat_images = os.listdir(train_cat_dir)\ndog_images = os.listdir(train_dog_dir)\n\n# Split 2500 images for validation, remaining 10000 for training\ncat_train, cat_val = train_test_split(cat_images, test_size=2500, random_state=42)\ndog_train, dog_val = train_test_split(dog_images, test_size=2500, random_state=42)\n\n# Move cat images to the appropriate folders\nfor img in cat_train:\n    src = os.path.join(train_cat_dir, img)\n    dst = os.path.join(train_cat_output_dir, img)\n    shutil.copy(src, dst)\n\nfor img in cat_val:\n    src = os.path.join(train_cat_dir, img)\n    dst = os.path.join(validation_cat_output_dir, img)\n    shutil.copy(src, dst)\n\n# Move dog images to the appropriate folders\nfor img in dog_train:\n    src = os.path.join(train_dog_dir, img)\n    dst = os.path.join(train_dog_output_dir, img)\n    shutil.copy(src, dst)\n\nfor img in dog_val:\n    src = os.path.join(train_dog_dir, img)\n    dst = os.path.join(validation_dog_output_dir, img)\n    shutil.copy(src, dst)\n\n# Verify the split\nprint(f\"Total training cat images: {len(os.listdir(train_cat_output_dir))}\")\nprint(f\"Total training dog images: {len(os.listdir(train_dog_output_dir))}\")\nprint(f\"Total validation cat images: {len(os.listdir(validation_cat_output_dir))}\")\nprint(f\"Total validation dog images: {len(os.listdir(validation_dog_output_dir))}\")\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:16:46.322395Z","iopub.execute_input":"2024-06-09T06:16:46.323162Z","iopub.status.idle":"2024-06-09T06:18:37.131964Z","shell.execute_reply.started":"2024-06-09T06:16:46.323122Z","shell.execute_reply":"2024-06-09T06:18:37.130956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls './split_data'","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:22:02.538402Z","iopub.execute_input":"2024-06-09T06:22:02.538792Z","iopub.status.idle":"2024-06-09T06:22:03.519246Z","shell.execute_reply.started":"2024-06-09T06:22:02.538757Z","shell.execute_reply":"2024-06-09T06:22:03.518234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_cat_names = os.listdir(train_cat_output_dir)\nprint(train_cat_names[:10])\n\ntrain_dog_names = os.listdir(train_dog_output_dir)\nprint(train_dog_names[:10])\n\nvalidation_cat_names = os.listdir(validation_cat_output_dir)\nprint(validation_cat_names[:10])\n\nvalidation_dog_names = os.listdir(validation_dog_output_dir)\nprint(validation_dog_names[:10])","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:18:37.133790Z","iopub.execute_input":"2024-06-09T06:18:37.134071Z","iopub.status.idle":"2024-06-09T06:18:37.154579Z","shell.execute_reply.started":"2024-06-09T06:18:37.134047Z","shell.execute_reply":"2024-06-09T06:18:37.153739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Dataset exploration","metadata":{}},{"cell_type":"code","source":"%matplotlib inline\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\n# Parameters for our graph; we'll output images in a 4x4 configuration\nnrows = 4\nncols = 4\n\n# Index for iterating over images\npic_index = 0","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:18:37.155454Z","iopub.execute_input":"2024-06-09T06:18:37.155744Z","iopub.status.idle":"2024-06-09T06:18:37.161836Z","shell.execute_reply.started":"2024-06-09T06:18:37.155721Z","shell.execute_reply":"2024-06-09T06:18:37.160920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set up matplotlib fig, and size it to fit 4x4 pics\nfig = plt.gcf()\nfig.set_size_inches(ncols * 4, nrows * 4)\n\npic_index += 8\nnext_cat_pic = [os.path.join(train_cat_output_dir, fname)\n                for fname in train_cat_names[pic_index-8:pic_index]]\nnext_dog_pic = [os.path.join(train_dog_output_dir, fname)\n                for fname in train_dog_names[pic_index-8:pic_index]]\n\nfor i, img_path in enumerate(next_cat_pic + next_dog_pic):\n  # Set up subplot; subplot indices start at 1\n  sp = plt.subplot(nrows, ncols, i + 1)\n  sp.axis('Off') # Don't show axes (or gridlines)\n\n  img = mpimg.imread(img_path)\n  plt.imshow(img)\n\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:18:37.163703Z","iopub.execute_input":"2024-06-09T06:18:37.164209Z","iopub.status.idle":"2024-06-09T06:18:38.492108Z","shell.execute_reply.started":"2024-06-09T06:18:37.164184Z","shell.execute_reply":"2024-06-09T06:18:38.490906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Install library","metadata":{}},{"cell_type":"code","source":"!pip install -q torchsummary","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:26:13.046763Z","iopub.execute_input":"2024-06-09T06:26:13.047402Z","iopub.status.idle":"2024-06-09T06:26:27.202298Z","shell.execute_reply.started":"2024-06-09T06:26:13.047368Z","shell.execute_reply":"2024-06-09T06:26:27.201217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Import library","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import transforms, datasets\nfrom torch.utils.data import DataLoader, Dataset\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nfrom torchsummary import summary\nfrom sklearn.metrics import classification_report","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:26:27.204720Z","iopub.execute_input":"2024-06-09T06:26:27.205482Z","iopub.status.idle":"2024-06-09T06:26:27.216107Z","shell.execute_reply.started":"2024-06-09T06:26:27.205442Z","shell.execute_reply":"2024-06-09T06:26:27.215233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### 1. Definition of KANLinear\n  - This definition follows the provided source and is used in the CNN-KAN model.\n\n  <p align=\"center\">\n  <a href=\"url\"><img src=\"https://www.marktechpost.com/wp-content/uploads/2024/05/Screenshot-2024-05-04-at-12.59.57-PM.png\" align=\"middle\" height=\"383\" width=\"640\" ></a>\n</p>\n\nKAN vs MLP Complexity (please correct if there are any errors):\n\nBig O notation measures the time or space complexity of an algorithm in terms of its input. To compare the memory and performance requirements between the Kolmogorov-Arnold Network (KAN) and Multi-Layer Perceptron (MLP), we can look at the components that affect memory usage and computational time for both algorithms.\n\n### Multi-Layer Perceptron (MLP)\n\nMLP usually consists of several layers of neurons, where each neuron in one layer is connected to every neuron in the next layer.\n\n- **Time Complexity (Training):** O(N * M * L * E)\n  - N: Number of samples in the dataset\n  - M: Number of neurons per layer\n  - L: Number of layers\n  - E: Number of epochs\n\n- **Space Complexity:** O(M * L + N * F)\n  - M: Number of neurons per layer\n  - L: Number of layers\n  - N: Number of samples in the dataset\n  - F: Dimension of input features\n\n### Kolmogorov-Arnold Network (KAN)\n\nKAN uses learnable activation functions at each edge and summation operations at nodes, adding complexity to the model.\n\n- **Time Complexity (Training):** O(N * (M^2) * L * E)\n  - N: Number of samples in the dataset\n  - M: Number of neurons per layer (since the activation functions are learnable at each edge, there is additional complexity here)\n  - L: Number of layers\n  - E: Number of epochs\n\n- **Space Complexity:** O(M^2 * L + N * F)\n  - M: Number of neurons per layer (with learnable activation functions at each edge, memory requirements increase)\n  - L: Number of layers\n  - N: Number of samples in the dataset\n  - F: Dimension of input features\n\n### Conclusion\n\nFrom the analysis above, it can be concluded that KAN has higher complexity compared to MLP in terms of both time and space.\n\n1. **Time Complexity:**\n   - MLP: O(N * M * L * E)\n   - KAN: O(N * M^2 * L * E)\n\n2. **Space Complexity:**\n   - MLP: O(M * L + N * F)\n   - KAN: O(M^2 * L + N * F)\n\nIt can be seen that KAN requires more memory (RAM) during training because it has learnable activation functions at each edge, which significantly increases the number of parameters that need to be stored and processed.","metadata":{}},{"cell_type":"markdown","source":"### Class KANLinier\n\n[1] Referensi KAN: https://github.com/Blealtan/efficient-kan/blob/master/src/efficient_kan/kan.py","metadata":{}},{"cell_type":"code","source":"class KANLinear(nn.Module):\n    def __init__(self, in_features, out_features, grid_size=5, spline_order=3, scale_noise=0.1, scale_base=1.0, scale_spline=1.0, enable_standalone_scale_spline=True, base_activation=nn.SiLU, grid_eps=0.02, grid_range=[-1, 1]):\n        super(KANLinear, self).__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.grid_size = grid_size\n        self.spline_order = spline_order\n\n        h = (grid_range[1] - grid_range[0]) / grid_size\n        grid = ((torch.arange(-spline_order, grid_size + spline_order + 1) * h + grid_range[0]).expand(in_features, -1).contiguous())\n        self.register_buffer(\"grid\", grid)\n\n        self.base_weight = nn.Parameter(torch.Tensor(out_features, in_features))\n        self.spline_weight = nn.Parameter(torch.Tensor(out_features, in_features, grid_size + spline_order))\n        if enable_standalone_scale_spline:\n            self.spline_scaler = nn.Parameter(torch.Tensor(out_features, in_features))\n\n        self.scale_noise = scale_noise\n        self.scale_base = scale_base\n        self.scale_spline = scale_spline\n        self.enable_standalone_scale_spline = enable_standalone_scale_spline\n        self.base_activation = base_activation()\n        self.grid_eps = grid_eps\n\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base)\n        with torch.no_grad():\n            noise = ((torch.rand(self.grid_size + 1, self.in_features, self.out_features) - 1 / 2) * self.scale_noise / self.grid_size)\n            self.spline_weight.data.copy_((self.scale_spline if not self.enable_standalone_scale_spline else 1.0) * self.curve2coeff(self.grid.T[self.spline_order : -self.spline_order], noise))\n            if self.enable_standalone_scale_spline:\n                nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline)\n\n    def b_splines(self, x: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        grid = self.grid\n        x = x.unsqueeze(-1)\n        bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype)\n        for k in range(1, self.spline_order + 1):\n            bases = ((x - grid[:, : -(k + 1)]) / (grid[:, k:-1] - grid[:, : -(k + 1)]) * bases[:, :, :-1]) + ((grid[:, k + 1 :] - x) / (grid[:, k + 1 :] - grid[:, 1:(-k)]) * bases[:, :, 1:])\n        assert bases.size() == (x.size(0), self.in_features, self.grid_size + self.spline_order)\n        return bases.contiguous()\n\n    def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        assert y.size() == (x.size(0), self.in_features, self.out_features)\n        A = self.b_splines(x).transpose(0, 1)\n        B = y.transpose(0, 1)\n        solution = torch.linalg.lstsq(A, B).solution\n        result = solution.permute(2, 0, 1)\n        assert result.size() == (self.out_features, self.in_features, self.grid_size + self.spline_order)\n        return result.contiguous()\n\n    @property\n    def scaled_spline_weight(self):\n        return self.spline_weight * (self.spline_scaler.unsqueeze(-1) if self.enable_standalone_scale_spline else 1.0)\n\n    def forward(self, x: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        base_output = F.linear(self.base_activation(x), self.base_weight)\n        spline_output = F.linear(self.b_splines(x).view(x.size(0), -1), self.scaled_spline_weight.view(self.out_features, -1))\n        return base_output + spline_output\n\n    @torch.no_grad()\n    def update_grid(self, x: torch.Tensor, margin=0.01):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        batch = x.size(0)\n        splines = self.b_splines(x).permute(1, 0, 2)\n        orig_coeff = self.scaled_spline_weight.permute(1, 2, 0)\n        unreduced_spline_output = torch.bmm(splines, orig_coeff).permute(1, 0, 2)\n        x_sorted = torch.sort(x, dim=0)[0]\n        grid_adaptive = x_sorted[torch.linspace(0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device)]\n        uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size\n        grid_uniform = (torch.arange(self.grid_size + 1, dtype=torch.float32, device=x.device).unsqueeze(1) * uniform_step + x_sorted[0] - margin)\n        grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive\n        grid = torch.cat([grid[:1] - uniform_step * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1), grid, grid[-1:] + uniform_step * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1)], dim=0)\n        self.grid.copy_(grid.T)\n        self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output))\n\n    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):\n        l1_fake = self.spline_weight.abs().mean(-1)\n        regularization_loss_activation = l1_fake.sum()\n        p = l1_fake / regularization_loss_activation\n        regularization_loss_entropy = -torch.sum(p * p.log())\n        return regularize_activation * regularization_loss_activation + regularize_entropy * regularization_loss_entropy\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:26:27.217276Z","iopub.execute_input":"2024-06-09T06:26:27.217617Z","iopub.status.idle":"2024-06-09T06:26:27.248390Z","shell.execute_reply.started":"2024-06-09T06:26:27.217592Z","shell.execute_reply":"2024-06-09T06:26:27.247413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Model CNNKAN\n\n[2] Referensi CNN-KAN: https://github.com/jakariaemon/CNN-KAN\n\n  # Basics CNN model for CIFAR-100 with KANLinear\n  \n    class CNNKAN(nn.Module):\n        def __init__(self):\n            super(CNNKAN, self).__init__()\n            self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)  \n            self.pool1 = nn.MaxPool2d(2)\n            self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n            self.pool2 = nn.MaxPool2d(2)\n            self.kan1 = KANLinear(64 * 8 * 8, 256)  \n            self.kan2 = KANLinear(256, 100)\n\n        def forward(self, x):\n            x = F.selu(self.conv1(x))\n            x = self.pool1(x)\n            x = F.selu(self.conv2(x))\n            x = self.pool2(x)\n            x = x.view(x.size(0), -1)\n            x = self.kan1(x)\n            x = self.kan2(x)\n            return x","metadata":{}},{"cell_type":"markdown","source":"### Model CNNKAN with modification for optimization","metadata":{}},{"cell_type":"code","source":"class CNNKAN(nn.Module):\n    def __init__(self):\n        super(CNNKAN, self).__init__()\n        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n        self.bn1 = nn.BatchNorm2d(32)\n        self.pool1 = nn.MaxPool2d(2)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n        self.bn2 = nn.BatchNorm2d(64)\n        self.pool2 = nn.MaxPool2d(2)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        self.bn3 = nn.BatchNorm2d(128)\n        self.pool3 = nn.MaxPool2d(2)\n        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n        self.bn4 = nn.BatchNorm2d(256)\n        self.pool4 = nn.MaxPool2d(2)\n        self.dropout = nn.Dropout(0.5)\n        self.kan1 = KANLinear(256 * 12 * 12, 512)\n        self.kan2 = KANLinear(512, 1)\n\n    def forward(self, x):\n        x = F.selu(self.bn1(self.conv1(x)))\n        x = self.pool1(x)\n        x = F.selu(self.bn2(self.conv2(x)))\n        x = self.pool2(x)\n        x = F.selu(self.bn3(self.conv3(x)))\n        x = self.pool3(x)\n        x = F.selu(self.bn4(self.conv4(x)))\n        x = self.pool4(x)\n        x = x.view(x.size(0), -1)\n        x = self.dropout(x)\n        x = self.kan1(x)\n        x = self.dropout(x)\n        x = self.kan2(x)\n        return x\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:34:11.677233Z","iopub.execute_input":"2024-06-09T10:34:11.677785Z","iopub.status.idle":"2024-06-09T10:34:11.696659Z","shell.execute_reply.started":"2024-06-09T10:34:11.677740Z","shell.execute_reply":"2024-06-09T10:34:11.695667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 3. Creating PyTorch Dataset and DataLoader\n  - Use transformations and PyTorch DataLoader:","metadata":{}},{"cell_type":"code","source":"# Directory paths\ntrain_dir = './split_data/train'\nval_dir = './split_data/validation'\n\n# Define the transformations\ntransform = transforms.Compose([\n    transforms.Resize((200, 200)),\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomRotation(10),\n    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2),\n    transforms.RandomResizedCrop(200, scale=(0.8, 1.0)),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\n# Create the datasets\ntrain_dataset = datasets.ImageFolder(root=train_dir, transform=transform)\nval_dataset = datasets.ImageFolder(root=val_dir, transform=transform)\n\n# Create the data loaders with smaller batch size\ntrain_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:28:30.716332Z","iopub.execute_input":"2024-06-09T06:28:30.717188Z","iopub.status.idle":"2024-06-09T06:28:30.992095Z","shell.execute_reply.started":"2024-06-09T06:28:30.717155Z","shell.execute_reply":"2024-06-09T06:28:30.991301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 4. Training dan Evaluasi Model\n  - Bagian ini mendefinisikan loop training dan evaluasi model. Clear Gradients dan Kosongkan Cache","metadata":{}},{"cell_type":"code","source":"%%time\n\n# Model definition\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nmodel = CNNKAN().to(device)\n\n# Use DataParallel if using more than one GPU\nif torch.cuda.device_count() > 1:\n    model = nn.DataParallel(model)\n    \nprint(model)\nsummary(model, input_size=(3, 200, 200))\n\n# Print model parameter details\ndef print_parameter_details(model):\n    for name, param in model.named_parameters():\n        print(f\"{name}: {param.size()} {'requires_grad' if param.requires_grad else 'frozen'}\")\n\nprint_parameter_details(model)\n\n# Visualize KAN parameters\ndef visualize_kan_parameters(kan_layer, layer_name):\n    base_weights = kan_layer.base_weight.data.cpu().numpy()\n    plt.hist(base_weights.ravel(), bins=50)\n    plt.title(f\"Distribution of Base Weights - {layer_name}\")\n    plt.xlabel(\"Weight Value\")\n    plt.ylabel(\"Frequency\")\n    plt.show()\n    if hasattr(kan_layer, 'spline_weight'):\n        spline_weights = kan_layer.spline_weight.data.cpu().numpy()\n        plt.hist(spline_weights.ravel(), bins=50)\n        plt.title(f\"Distribution of Spline Weights - {layer_name}\")\n        plt.xlabel(\"Weight Value\")\n        plt.ylabel(\"Frequency\")\n        plt.show()\n\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-3)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')\n\n# Training function\ndef train(model, train_loader, criterion, optimizer, device, epoch):\n    model.train()\n    running_loss = 0.0\n    for batch_idx, (inputs, labels) in enumerate(train_loader):\n        inputs, labels = inputs.to(device), labels.to(device)\n        optimizer.zero_grad()\n        outputs = model(inputs)\n        loss = criterion(outputs, labels.unsqueeze(1).float())\n        loss.backward()\n        optimizer.step()\n        running_loss += loss.item()\n\n        if batch_idx % 10 == 0:\n            print(f'Train Epoch: {epoch} [{batch_idx * len(inputs)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\\tLoss: {loss.item():.6f}')\n\n        # Clear CUDA cache\n        torch.cuda.empty_cache()\n\n    return running_loss / len(train_loader)\n\n# Validation function\ndef validate(model, val_loader, criterion, device):\n    model.eval()\n    val_loss = 0.0\n    correct = 0\n    total = 0\n    all_labels = []\n    all_predictions = []\n    with torch.no_grad():\n        for inputs, labels in val_loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            outputs = model(inputs)\n            loss = criterion(outputs, labels.unsqueeze(1).float())\n            val_loss += loss.item()\n            predicted = (outputs > 0.5).float()\n            total += labels.size(0)\n            correct += (predicted.squeeze() == labels).sum().item()\n\n            all_labels.extend(labels.cpu().numpy())\n            all_predictions.extend(predicted.squeeze().cpu().numpy())\n\n            # Clear CUDA cache\n            torch.cuda.empty_cache()\n\n    accuracy = 100 * correct / total\n    print(classification_report(all_labels, all_predictions))\n    return val_loss / len(val_loader), accuracy\n\n# Early stopping\nbest_val_loss = float('inf')\npatience = 5\npatience_counter = 0\n\n# Lists to store loss and accuracy values\ntrain_losses = []\nval_losses = []\nval_accuracies = []\n\n# Input jumlah epoch\njumlah_epoch = int(input(\"Masukkan jumlah epoch: \"))\nfor epoch in range(jumlah_epoch):\n    train_loss = train(model, train_loader, criterion, optimizer, device, epoch)\n    val_loss, val_accuracy = validate(model, val_loader, criterion, device)\n\n    # Save losses and accuracy\n    train_losses.append(train_loss)\n    val_losses.append(val_loss)\n    val_accuracies.append(val_accuracy)\n\n    print(f\"Epoch {epoch+1}/{jumlah_epoch}, Train Loss: {train_loss:.6f}, Validation Loss: {val_loss:.6f}, Validation Accuracy: {val_accuracy:.2f}%\")\n    scheduler.step(val_loss)\n\n    # Early stopping\n    if val_loss < best_val_loss:\n        best_val_loss = val_loss\n        patience_counter = 0\n        torch.save(model.state_dict(), 'best_model_weights_KAN.pth')\n    else:\n        patience_counter += 1\n        if patience_counter >= patience:\n            print(\"Early stopping triggered\")\n            break\n\n# Load the best model weights\nmodel.load_state_dict(torch.load('best_model_weights_KAN.pth'))\n\n# Evaluate model on validation set\ndef test(model, val_loader, device):\n    model.eval()\n    correct = 0\n    total = 0\n    with torch.no_grad():\n        for inputs, labels in val_loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            outputs = model(inputs)\n            predicted = (outputs > 0.5).float()\n            total += labels.size(0)\n            correct += (predicted.squeeze() == labels).sum().item()\n\n            # Clear CUDA cache\n            torch.cuda.empty_cache()\n\n    accuracy = 100 * correct / total\n    return accuracy\n\naccuracy = test(model, val_loader, device)\nprint(f'Validation Accuracy: {accuracy:.2f}%')\n\n# Plot training and validation loss\ndef plot_loss(train_losses, val_losses):\n    plt.figure()\n    plt.plot(train_losses, label='Training Loss')\n    plt.plot(val_losses, label='Validation Loss')\n    plt.xlabel('Epochs')\n    plt.ylabel('Loss')\n    plt.title('Training and Validation Loss')\n    plt.legend()\n    plt.show()\n\n# Plot validation accuracy\ndef plot_accuracy(val_accuracies):\n    plt.figure()\n    plt.plot(val_accuracies, label='Validation Accuracy')\n    plt.xlabel('Epochs')\n    plt.ylabel('Accuracy')\n    plt.title('Validation Accuracy')\n    plt.legend()\n    plt.show()\n\n# Plot the losses and accuracy\nplot_loss(train_losses, val_losses)\nplot_accuracy(val_accuracies)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T06:28:36.360182Z","iopub.execute_input":"2024-06-09T06:28:36.361121Z","iopub.status.idle":"2024-06-09T09:55:53.450656Z","shell.execute_reply.started":"2024-06-09T06:28:36.361079Z","shell.execute_reply":"2024-06-09T09:55:53.449693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Result\n\n![WhatsApp Image 2024-06-09 at 17.14.20_d7727b94.jpg](attachment:5690f1a1-ba50-4bb8-b0dd-0daf9ce90ee0.jpg)","metadata":{},"attachments":{"5690f1a1-ba50-4bb8-b0dd-0daf9ce90ee0.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"## 5. Evaluasi Model pada Data Test","metadata":{}},{"cell_type":"code","source":"\"\"\"\n# Create the test dataset and loader\ntest_dir = '/tmp/test'\ntest_dataset = datasets.ImageFolder(root=test_dir, transform=transform)\ntest_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)\n\ndef test(model, test_loader, device):\n    model.eval()\n    correct = 0\n    total = 0\n    with torch.no_grad():\n        for inputs, labels in test_loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            outputs = model(inputs)\n            predicted = (outputs > 0.5).float()\n            total += labels.size(0)\n            correct += (predicted.squeeze() == labels).sum().item()\n\n            # Kosongkan cache CUDA\n            torch.cuda.empty_cache()\n\n    accuracy = 100 * correct / total\n    return accuracy\n\naccuracy = test(model, test_loader, device)\nprint(f'Test Accuracy: {accuracy}%')\n\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 6. Plot ROC Curve","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc, classification_report, accuracy_score, precision_score, recall_score, f1_score\nimport torch\n\n# Function to calculate predictions and true labels\ndef get_predictions_and_labels(model, val_loader, device):\n    model.eval()\n    preds = []\n    true_labels = []\n    with torch.no_grad():\n        for inputs, labels in val_loader:\n            inputs, labels = inputs.to(device), labels.to(device)\n            outputs = model(inputs)\n            preds.extend(outputs.cpu().numpy())\n            true_labels.extend(labels.cpu().numpy())\n\n    return np.array(preds), np.array(true_labels)\n\n# Function to plot ROC Curve\ndef plot_roc_curve(true_labels, preds):\n    fpr, tpr, _ = roc_curve(true_labels, preds)\n    roc_auc = auc(fpr, tpr)\n\n    plt.figure()\n    lw = 2\n    plt.plot(fpr, tpr, color='darkorange', lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel('False Positive Rate')\n    plt.ylabel('True Positive Rate')\n    plt.title('Receiver Operating Characteristic with KAN-CNN')\n    plt.legend(loc=\"lower right\")\n    plt.show()\n\n# Function to calculate and print classification metrics\ndef print_classification_metrics(true_labels, preds):\n    preds_binary = (preds >= 0.5).astype(int) # Threshold for binary classification\n    accuracy = accuracy_score(true_labels, preds_binary)\n    precision = precision_score(true_labels, preds_binary)\n    recall = recall_score(true_labels, preds_binary)\n    f1 = f1_score(true_labels, preds_binary)\n    report = classification_report(true_labels, preds_binary, target_names=['Class 0', 'Class 1'])\n\n    print(\" \")\n    print(f'Accuracy: {accuracy:.4f}')\n    print(f'Precision: {precision:.4f}')\n    print(f'Recall: {recall:.4f}')\n    print(f'F1 Score: {f1:.4f}')\n    print('Classification Report:')\n    print(report)\n\n# Assuming you have your model, val_loader, and device already defined\n# Example usage\npreds, true_labels = get_predictions_and_labels(model, val_loader, device)\nplot_roc_curve(true_labels, preds)\nprint_classification_metrics(true_labels, preds)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-09T09:56:08.359202Z","iopub.execute_input":"2024-06-09T09:56:08.359899Z","iopub.status.idle":"2024-06-09T09:57:00.800290Z","shell.execute_reply.started":"2024-06-09T09:56:08.359862Z","shell.execute_reply":"2024-06-09T09:57:00.799438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Result ROC-Curve\n\n![WhatsApp Image 2024-06-09 at 17.13.06_0dc3fbe8.jpg](attachment:1d62a62c-c74c-449b-9b88-96cdfc29c224.jpg)","metadata":{},"attachments":{"1d62a62c-c74c-449b-9b88-96cdfc29c224.jpg":{"image/jpeg":"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Making a Prediction","metadata":{}},{"cell_type":"code","source":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torchvision import transforms\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom ipywidgets import FileUpload\nfrom IPython.display import display\n\n# Tentukan device\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# Definisikan transformasi gambar\ntransform = transforms.Compose([\n    transforms.Resize((200, 200)),\n    transforms.ToTensor()\n])\n\n# Fungsi untuk memuat dan memproses gambar\ndef load_and_preprocess_image(path):\n    img = Image.open(path).convert('RGB')\n    img_tensor = transform(img).unsqueeze(0).to(device)\n    return img, img_tensor\n\nclass KANLinear(nn.Module):\n    def __init__(self, in_features, out_features, grid_size=5, spline_order=3, scale_noise=0.1, scale_base=1.0, scale_spline=1.0, enable_standalone_scale_spline=True, base_activation=nn.SiLU, grid_eps=0.02, grid_range=[-1, 1]):\n        super(KANLinear, self).__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.grid_size = grid_size\n        self.spline_order = spline_order\n\n        h = (grid_range[1] - grid_range[0]) / grid_size\n        grid = ((torch.arange(-spline_order, grid_size + spline_order + 1) * h + grid_range[0]).expand(in_features, -1).contiguous())\n        self.register_buffer(\"grid\", grid)\n\n        self.base_weight = nn.Parameter(torch.Tensor(out_features, in_features))\n        self.spline_weight = nn.Parameter(torch.Tensor(out_features, in_features, grid_size + spline_order))\n        if enable_standalone_scale_spline:\n            self.spline_scaler = nn.Parameter(torch.Tensor(out_features, in_features))\n\n        self.scale_noise = scale_noise\n        self.scale_base = scale_base\n        self.scale_spline = scale_spline\n        self.enable_standalone_scale_spline = enable_standalone_scale_spline\n        self.base_activation = base_activation()\n        self.grid_eps = grid_eps\n\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base)\n        with torch.no_grad():\n            noise = ((torch.rand(self.grid_size + 1, self.in_features, self.out_features) - 1 / 2) * self.scale_noise / self.grid_size)\n            self.spline_weight.data.copy_((self.scale_spline if not self.enable_standalone_scale_spline else 1.0) * self.curve2coeff(self.grid.T[self.spline_order : -self.spline_order], noise))\n            if self.enable_standalone_scale_spline:\n                nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline)\n\n    def b_splines(self, x: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        grid = self.grid\n        x = x.unsqueeze(-1)\n        bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype)\n        for k in range(1, self.spline_order + 1):\n            bases = ((x - grid[:, : -(k + 1)]) / (grid[:, k:-1] - grid[:, : -(k + 1)]) * bases[:, :, :-1]) + ((grid[:, k + 1 :] - x) / (grid[:, k + 1 :] - grid[:, 1:(-k)]) * bases[:, :, 1:])\n        assert bases.size() == (x.size(0), self.in_features, self.grid_size + self.spline_order)\n        return bases.contiguous()\n\n    def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        assert y.size() == (x.size(0), self.in_features, self.out_features)\n        A = self.b_splines(x).transpose(0, 1)\n        B = y.transpose(0, 1)\n        solution = torch.linalg.lstsq(A, B).solution\n        result = solution.permute(2, 0, 1)\n        assert result.size() == (self.out_features, self.in_features, self.grid_size + self.spline_order)\n        return result.contiguous()\n\n    @property\n    def scaled_spline_weight(self):\n        return self.spline_weight * (self.spline_scaler.unsqueeze(-1) if self.enable_standalone_scale_spline else 1.0)\n\n    def forward(self, x: torch.Tensor):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        base_output = F.linear(self.base_activation(x), self.base_weight)\n        spline_output = F.linear(self.b_splines(x).view(x.size(0), -1), self.scaled_spline_weight.view(self.out_features, -1))\n        return base_output + spline_output\n\n    @torch.no_grad()\n    def update_grid(self, x: torch.Tensor, margin=0.01):\n        assert x.dim() == 2 and x.size(1) == self.in_features\n        batch = x.size(0)\n        splines = self.b_splines(x).permute(1, 0, 2)\n        orig_coeff = self.scaled_spline_weight.permute(1, 2, 0)\n        unreduced_spline_output = torch.bmm(splines, orig_coeff).permute(1, 0, 2)\n        x_sorted = torch.sort(x, dim=0)[0]\n        grid_adaptive = x_sorted[torch.linspace(0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device)]\n        uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size\n        grid_uniform = (torch.arange(self.grid_size + 1, dtype=torch.float32, device=x.device).unsqueeze(1) * uniform_step + x_sorted[0] - margin)\n        grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive\n        grid = torch.cat([grid[:1] - uniform_step * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1), grid, grid[-1:] + uniform_step * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1)], dim=0)\n        self.grid.copy_(grid.T)\n        self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output))\n\n    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):\n        l1_fake = self.spline_weight.abs().mean(-1)\n        regularization_loss_activation = l1_fake.sum()\n        p = l1_fake / regularization_loss_activation\n        regularization_loss_entropy = -torch.sum(p * p.log())\n        return regularize_activation * regularization_loss_activation + regularize_entropy * regularization_loss_entropy\n\n# Model Anda harus didefinisikan di sini sebelum memuat state_dict\nclass CNNKAN(nn.Module):\n    def __init__(self):\n        super(CNNKAN, self).__init__()\n        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n        self.bn1 = nn.BatchNorm2d(32)\n        self.pool1 = nn.MaxPool2d(2)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n        self.bn2 = nn.BatchNorm2d(64)\n        self.pool2 = nn.MaxPool2d(2)\n        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n        self.bn3 = nn.BatchNorm2d(128)\n        self.pool3 = nn.MaxPool2d(2)\n        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n        self.bn4 = nn.BatchNorm2d(256)\n        self.pool4 = nn.MaxPool2d(2)\n        self.dropout = nn.Dropout(0.5)\n        self.kan1 = KANLinear(256 * 12 * 12, 512)  # Sesuaikan dengan dimensi yang benar\n        self.kan2 = KANLinear(512, 1)\n\n    def forward(self, x):\n        x = F.selu(self.bn1(self.conv1(x)))\n        x = self.pool1(x)\n        x = F.selu(self.bn2(self.conv2(x)))\n        x = self.pool2(x)\n        x = F.selu(self.bn3(self.conv3(x)))\n        x = self.pool3(x)\n        x = F.selu(self.bn4(self.conv4(x)))\n        x = self.pool4(x)\n        x = x.view(x.size(0), -1)\n        x = self.dropout(x)\n        x = self.kan1(x)\n        x = self.dropout(x)\n        x = self.kan2(x)\n        return x\n\n# Membuat instance model\nmodel = CNNKAN().to(device)\n\n# Memuat state_dict dari file dengan penyesuaian nama parameter\ndef load_state_dict_adjusted(model, state_dict_path):\n    state_dict = torch.load(state_dict_path, map_location=device)\n    new_state_dict = {}\n    for k, v in state_dict.items():\n        if k.startswith('module.'):\n            new_state_dict[k[7:]] = v  # menghapus prefix 'module.'\n        else:\n            new_state_dict[k] = v\n    model.load_state_dict(new_state_dict)\n\nmodel_path = '/kaggle/working/best_model_weights_KAN.pth'  # Ganti dengan path model Anda\nload_state_dict_adjusted(model, model_path)\n\n# Fungsi untuk melakukan prediksi\ndef predict_and_display(img, img_tensor):\n    model.eval()\n    with torch.no_grad():\n        output = model(img_tensor)\n        prob = torch.sigmoid(output).item()\n\n    # Menampilkan gambar beserta hasil prediksi\n    plt.imshow(np.array(img))\n    plt.axis('off')\n    prediction_text = f\"Probabilitas: {prob:.4f}\\n\"\n    prediction_text += \"This is a cat\" if prob < 0.5 else \"This is a dog\"\n    plt.title(prediction_text)\n    plt.show()\n\n# Widget untuk mengunggah file\nuploader = FileUpload(accept='image/*', multiple=True)\ndisplay(uploader)\n\n# Fungsi untuk menangani pengunggahan file\ndef handle_upload(change):\n    for name, file_info in uploader.value.items():\n        content = file_info['content']\n        path = f\"/kaggle/working/{name}\"\n        with open(path, 'wb') as f:\n            f.write(content)\n        \n        img, img_tensor = load_and_preprocess_image(path)\n        predict_and_display(img, img_tensor)\n\n# Mendaftarkan handler untuk widget pengunggah\nuploader.observe(handle_upload, names='value')","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:53:26.326979Z","iopub.execute_input":"2024-06-09T10:53:26.327376Z","iopub.status.idle":"2024-06-09T10:53:34.752755Z","shell.execute_reply.started":"2024-06-09T10:53:26.327347Z","shell.execute_reply":"2024-06-09T10:53:34.751805Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## This prediction result from google colab ","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:89dc888a-28d4-4dc7-a701-8f6ab92a67e5.png)","metadata":{},"attachments":{"89dc888a-28d4-4dc7-a701-8f6ab92a67e5.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"### If code prediction in kaggle error, alternative you can use the colab version:\n\nCode colab: https://colab.research.google.com/drive/10AZsbsfUrET_tHk614BO-9CSPSjAShHZ?usp=sharing ","metadata":{}},{"cell_type":"markdown","source":"## Download the model","metadata":{}},{"cell_type":"code","source":"!df -h","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:44:55.483403Z","iopub.execute_input":"2024-06-09T10:44:55.484415Z","iopub.status.idle":"2024-06-09T10:44:56.474027Z","shell.execute_reply.started":"2024-06-09T10:44:55.484372Z","shell.execute_reply":"2024-06-09T10:44:56.473022Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!cd /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:14:49.145687Z","iopub.execute_input":"2024-06-09T10:14:49.146442Z","iopub.status.idle":"2024-06-09T10:14:50.117385Z","shell.execute_reply.started":"2024-06-09T10:14:49.146409Z","shell.execute_reply":"2024-06-09T10:14:50.116165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:47:27.372930Z","iopub.execute_input":"2024-06-09T10:47:27.373321Z","iopub.status.idle":"2024-06-09T10:47:28.350135Z","shell.execute_reply.started":"2024-06-09T10:47:27.373290Z","shell.execute_reply":"2024-06-09T10:47:28.349127Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from IPython.display import FileLink \nFileLink(r'best_model_weights_KAN.pth')","metadata":{"execution":{"iopub.status.busy":"2024-06-09T10:22:44.866238Z","iopub.execute_input":"2024-06-09T10:22:44.867146Z","iopub.status.idle":"2024-06-09T10:22:44.873672Z","shell.execute_reply.started":"2024-06-09T10:22:44.867109Z","shell.execute_reply":"2024-06-09T10:22:44.872701Z"},"trusted":true},"execution_count":null,"outputs":[]}]}