{"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":"nvidiaTeslaT4","dataSources":[{"sourceId":34426,"databundleVersionId":3218632,"sourceType":"competition"}],"dockerImageVersionId":30733,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"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\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\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":"2024-06-17T23:17:49.679531Z","iopub.execute_input":"2024-06-17T23:17:49.680087Z","iopub.status.idle":"2024-06-17T23:17:50.670874Z","shell.execute_reply.started":"2024-06-17T23:17:49.680049Z","shell.execute_reply":"2024-06-17T23:17:50.669911Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Cat and Dog Classification using ResNet-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:4d97a307-2dcc-4058-ac94-e4717c8f2965.png)\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:3cd32c62-f6ae-4f5a-b2de-ae644c100045.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 learning.","metadata":{},"attachments":{"4d97a307-2dcc-4058-ac94-e4717c8f2965.png":{"image/png":"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"},"3cd32c62-f6ae-4f5a-b2de-ae644c100045.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":"2024-06-17T23:17:50.672494Z","iopub.execute_input":"2024-06-17T23:17:50.672901Z","iopub.status.idle":"2024-06-17T23:17:51.724095Z","shell.execute_reply.started":"2024-06-17T23:17:50.672870Z","shell.execute_reply":"2024-06-17T23:17:51.722940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Checking the Directory Cat and Dog","metadata":{}},{"cell_type":"code","source":"!ls '/kaggle/input/dog-vs-cat-classification'","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:17:51.731019Z","iopub.execute_input":"2024-06-17T23:17:51.731384Z","iopub.status.idle":"2024-06-17T23:17:52.735921Z","shell.execute_reply.started":"2024-06-17T23:17:51.731343Z","shell.execute_reply":"2024-06-17T23:17:52.734739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Spliting data training and 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":"2024-06-17T23:17:52.737722Z","iopub.execute_input":"2024-06-17T23:17:52.738034Z","iopub.status.idle":"2024-06-17T23:23:44.696401Z","shell.execute_reply.started":"2024-06-17T23:17:52.738005Z","shell.execute_reply":"2024-06-17T23:23:44.695452Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls './split_data'","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:23:44.697805Z","iopub.execute_input":"2024-06-17T23:23:44.698172Z","iopub.status.idle":"2024-06-17T23:23:45.644550Z","shell.execute_reply.started":"2024-06-17T23:23:44.698139Z","shell.execute_reply":"2024-06-17T23:23:45.643546Z"},"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":"2024-06-17T23:23:45.645991Z","iopub.execute_input":"2024-06-17T23:23:45.646301Z","iopub.status.idle":"2024-06-17T23:23:45.669555Z","shell.execute_reply.started":"2024-06-17T23:23:45.646270Z","shell.execute_reply":"2024-06-17T23:23:45.668706Z"},"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":"2024-06-17T23:23:45.670622Z","iopub.execute_input":"2024-06-17T23:23:45.670890Z","iopub.status.idle":"2024-06-17T23:23:45.677729Z","shell.execute_reply.started":"2024-06-17T23:23:45.670855Z","shell.execute_reply":"2024-06-17T23:23:45.676788Z"},"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":"2024-06-17T23:23:45.679038Z","iopub.execute_input":"2024-06-17T23:23:45.679317Z","iopub.status.idle":"2024-06-17T23:23:47.279616Z","shell.execute_reply.started":"2024-06-17T23:23:45.679294Z","shell.execute_reply":"2024-06-17T23:23:47.278625Z"},"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":"2024-06-17T23:23:47.284750Z","iopub.execute_input":"2024-06-17T23:23:47.285087Z","iopub.status.idle":"2024-06-17T23:24:00.728077Z","shell.execute_reply.started":"2024-06-17T23:23:47.285059Z","shell.execute_reply":"2024-06-17T23:24:00.726808Z"},"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, models\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\nfrom torch.optim.lr_scheduler import OneCycleLR\nfrom tqdm.notebook import tqdm","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:24:00.729627Z","iopub.execute_input":"2024-06-17T23:24:00.729996Z","iopub.status.idle":"2024-06-17T23:24:05.624951Z","shell.execute_reply.started":"2024-06-17T23:24:00.729964Z","shell.execute_reply":"2024-06-17T23:24:05.623895Z"},"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] Reference: 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":"2024-06-17T23:24:05.626420Z","iopub.execute_input":"2024-06-17T23:24:05.627278Z","iopub.status.idle":"2024-06-17T23:24:05.659944Z","shell.execute_reply.started":"2024-06-17T23:24:05.627238Z","shell.execute_reply":"2024-06-17T23:24:05.659058Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## 2. Model CNNKAN\n\nReference: CNN-KAN: https://github.com/jakariaemon/CNN-KAN \n\n  # Basics CNN model for Cat and Dog Classifications 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 VGG16-KAN \n\nReference KANVGG16 architectures from: https://www.linkedin.com/posts/damienbenveniste_this-is-a-pure-kolmogorovarnold-kan-convolutional-activity-7193403017008680960-FOHW?utm_source=share&utm_medium=member_android","metadata":{}},{"cell_type":"code","source":"# Define the model\nclass KANVGG16(nn.Module):\n    def __init__(self, num_classes=1):  # For binary classification (cats and dogs)\n        super(KANVGG16, self).__init__()\n        self.features = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(64),  # Added Batch Normalization\n\n            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(128),  # Added Batch Normalization\n\n            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(256),  # Added Batch Normalization\n\n            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(512),  # Added Batch Normalization\n\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(512),  # Added Batch Normalization\n        )\n        self.classifier = nn.Sequential(\n            KANLinear(512 * 7 * 7, 2048),  # Adjusted for input size 224x224\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),  # Increased Dropout\n            KANLinear(2048, 2048),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),  # Increased Dropout\n            KANLinear(2048, num_classes)\n        )\n\n    def forward(self, x):\n        x = self.features(x)\n        x = torch.flatten(x, 1)\n        x = self.classifier(x)\n        return x","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:24:05.661145Z","iopub.execute_input":"2024-06-17T23:24:05.661433Z","iopub.status.idle":"2024-06-17T23:24:05.679116Z","shell.execute_reply.started":"2024-06-17T23:24:05.661408Z","shell.execute_reply":"2024-06-17T23:24:05.678145Z"},"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 transformations for the training data\ntransform_train = transforms.Compose([\n    transforms.RandomResizedCrop(224),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\n# For validation data, we resize to 224x224 and then just convert to tensor and normalize\ntransform_val = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n])\n\nbatch_size = 32\n\n# Create the datasets\ntrain_dataset = datasets.ImageFolder(root=train_dir, transform=transform_train)\nval_dataset = datasets.ImageFolder(root=val_dir, transform=transform_val)\n\n# Create the data loaders\ntrain_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:24:05.680330Z","iopub.execute_input":"2024-06-17T23:24:05.680659Z","iopub.status.idle":"2024-06-17T23:24:05.809065Z","shell.execute_reply.started":"2024-06-17T23:24:05.680632Z","shell.execute_reply":"2024-06-17T23:24:05.808073Z"},"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":"# Clear CUDA Cache\ntorch.cuda.empty_cache()","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:24:05.810260Z","iopub.execute_input":"2024-06-17T23:24:05.810542Z","iopub.status.idle":"2024-06-17T23:24:05.814737Z","shell.execute_reply.started":"2024-06-17T23:24:05.810518Z","shell.execute_reply":"2024-06-17T23:24:05.813783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n\nepoch_number = 50\n\n# Model definition\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nmodel = KANVGG16().to(device)\nprint(model)\nsummary(model, input_size=(3, 224, 224))\n\n# Use DataParallel if using more than one GPU\nif torch.cuda.device_count() > 1:\n    model = nn.DataParallel(model)\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\n# Use BCEWithLogitsLoss for binary classification\ncriterion = nn.BCEWithLogitsLoss()\noptimizer = optim.Adam(model.parameters(), lr=0.0001, weight_decay=1e-5)  # Added weight decay\nscheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.001, steps_per_epoch=len(train_loader), epochs=epoch_number)\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).float().view(-1, 1)  # Penyesuaian di sini\n        optimizer.zero_grad()\n        outputs = model(inputs)\n        loss = criterion(outputs, labels)\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\n# Validation function with batch progress display\ndef validate(model, val_loader, criterion, device, epoch):\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 batch_idx, (inputs, labels) in enumerate(val_loader):\n            inputs, labels = inputs.to(device), labels.to(device).float().view(-1, 1)  # Penyesuaian di sini\n            outputs = model(inputs)\n            loss = criterion(outputs, labels)\n            val_loss += loss.item()\n            predicted = (outputs > 0.5).float()\n            total += labels.size(0)\n            correct += (predicted.squeeze() == labels.squeeze()).sum().item()  # Sesuaikan perbandingan agar bekerja dengan dimensi yang sama\n\n            all_labels.extend(labels.cpu().numpy())\n            all_predictions.extend(predicted.squeeze().cpu().numpy())\n\n            if batch_idx % 10 == 0:\n                print(f'Validation Epoch: {epoch} [{batch_idx * len(inputs)}/{len(val_loader.dataset)} ({100. * batch_idx / len(val_loader):.0f}%)]\\tLoss: {loss.item():.6f}')\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 = epoch_number\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, epoch)\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)","metadata":{"execution":{"iopub.status.busy":"2024-06-17T23:24:05.816143Z","iopub.execute_input":"2024-06-17T23:24:05.816670Z","iopub.status.idle":"2024-06-18T06:24:02.784035Z","shell.execute_reply.started":"2024-06-17T23:24:05.816608Z","shell.execute_reply":"2024-06-18T06:24:02.783078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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":{"iopub.status.busy":"2024-06-18T06:24:02.785366Z","iopub.execute_input":"2024-06-18T06:24:02.785723Z","iopub.status.idle":"2024-06-18T06:24:02.793320Z","shell.execute_reply.started":"2024-06-18T06:24:02.785687Z","shell.execute_reply":"2024-06-18T06:24:02.792460Z"},"trusted":true},"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":"2024-06-18T06:25:54.063986Z","iopub.execute_input":"2024-06-18T06:25:54.064751Z","iopub.status.idle":"2024-06-18T06:27:00.920345Z","shell.execute_reply.started":"2024-06-18T06:25:54.064695Z","shell.execute_reply":"2024-06-18T06:27:00.919332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Making Prediction\n\nCode Colab: https://colab.research.google.com/drive/1h-a2_EvDZYKawcH847a3kj7gyujWsQPR?usp=sharing","metadata":{}},{"cell_type":"code","source":"# Tentukan device\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# Definisikan transformasi gambar\ntransform = transforms.Compose([\n    transforms.Resize((224, 224)),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n\n# Fungsi untuk memuat dan memproses gambar\ndef load_and_preprocess_image(img_bytes):\n    img = Image.open(BytesIO(img_bytes)).convert('RGB')\n    img_tensor = transform(img).unsqueeze(0).to(device)\n    return img, img_tensor\n\n# Define the model\nclass KANVGG16(nn.Module):\n    def __init__(self, num_classes=1):  # For binary classification (cats and dogs)\n        super(KANVGG16, self).__init__()\n        self.features = nn.Sequential(\n            nn.Conv2d(3, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(64, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(64),  # Added Batch Normalization\n\n            nn.Conv2d(64, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(128, 128, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(128),  # Added Batch Normalization\n\n            nn.Conv2d(128, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(256),  # Added Batch Normalization\n\n            nn.Conv2d(256, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(512),  # Added Batch Normalization\n\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(512, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2),\n            nn.BatchNorm2d(512),  # Added Batch Normalization\n        )\n        self.classifier = nn.Sequential(\n            KANLinear(512 * 7 * 7, 2048),  # Adjusted for input size 224x224\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),  # Increased Dropout\n            KANLinear(2048, 2048),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),  # Increased Dropout\n            KANLinear(2048, num_classes)\n        )\n\n    def forward(self, x):\n        x = self.features(x)\n        x = torch.flatten(x, 1)\n        x = self.classifier(x)\n        return x\n\n# Membuat instance model\nmodel = KANVGG16().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[len('module.'):]] = v\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 dan menampilkan gambar\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).cpu().numpy().flatten()\n    plt.imshow(np.array(img))\n    plt.axis('off')\n    prediction_text = f\"Probabilitas: {prob[0]:.4f}\\n\"\n    prediction_text += \"This is a cat\" if prob[0] < 0.5 else \"This is a dog\"\n    plt.title(prediction_text)\n    plt.show()\n\n# Fungsi untuk menangani pengunggahan file\ndef handle_upload():\n    uploaded = files.upload()\n    for name, data in uploaded.items():\n        img, img_tensor = load_and_preprocess_image(data)\n        predict_and_display(img, img_tensor)\n\n# Memanggil fungsi untuk menangani pengunggahan file\nhandle_upload()","metadata":{"execution":{"iopub.status.busy":"2024-06-18T06:24:03.145179Z","iopub.status.idle":"2024-06-18T06:24:03.145636Z","shell.execute_reply.started":"2024-06-18T06:24:03.145403Z","shell.execute_reply":"2024-06-18T06:24:03.145422Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Result prediction in colab\n\n![image.png](attachment:85b03500-0689-4050-9275-ce2a4a923da6.png)","metadata":{},"attachments":{"85b03500-0689-4050-9275-ce2a4a923da6.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## Download the Model","metadata":{}},{"cell_type":"code","source":"!cd /kaggle/working/","metadata":{"execution":{"iopub.status.busy":"2024-06-18T06:27:13.422140Z","iopub.execute_input":"2024-06-18T06:27:13.422511Z","iopub.status.idle":"2024-06-18T06:27:14.409818Z","shell.execute_reply.started":"2024-06-18T06:27:13.422480Z","shell.execute_reply":"2024-06-18T06:27:14.408696Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!ls","metadata":{"execution":{"iopub.status.busy":"2024-06-18T06:27:16.724010Z","iopub.execute_input":"2024-06-18T06:27:16.724454Z","iopub.status.idle":"2024-06-18T06:27:17.734659Z","shell.execute_reply.started":"2024-06-18T06:27:16.724410Z","shell.execute_reply":"2024-06-18T06:27:17.733529Z"},"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":"2024-06-18T06:27:36.450912Z","iopub.execute_input":"2024-06-18T06:27:36.451899Z","iopub.status.idle":"2024-06-18T06:27:36.459032Z","shell.execute_reply.started":"2024-06-18T06:27:36.451852Z","shell.execute_reply":"2024-06-18T06:27:36.457997Z"},"trusted":true},"execution_count":null,"outputs":[]}]}