jupyter_notebooks/classification/siren_base_tool.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NihuPkSrk_PE","outputId":"9a941653-3ca0-4884-f26d-eaf9015f8eaa","executionInfo":{"status":"ok","timestamp":1725549270023,"user_tz":-120,"elapsed":90674,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: keras==2.15.0 in /usr/local/lib/python3.10/dist-packages (2.15.0)\n","Collecting tensorflow==2.15.0.post1\n","  Downloading tensorflow-2.15.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\n","Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0.post1) (1.4.0)\n","Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.0.post1) (1.6.3)\n","Requirement already satisfied: flatbuffers>=23.5.26 in 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requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15.0.post1) (3.2.2)\n","Downloading tensorflow-2.15.0.post1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (475.2 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: tensorflow\n","  Attempting uninstall: tensorflow\n","    Found existing installation: tensorflow 2.15.0\n","    Uninstalling tensorflow-2.15.0:\n","      Successfully uninstalled tensorflow-2.15.0\n","Successfully installed tensorflow-2.15.0.post1\n","Requirement already satisfied: scipy==1.11.4 in /usr/local/lib/python3.10/dist-packages (1.11.4)\n","Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy==1.11.4) (1.25.2)\n","Collecting tensorflow-addons==0.21.0\n","  Downloading 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(129 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.1/129.1 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hDownloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n","Installing collected packages: kt-legacy, keras-tuner\n","Successfully installed keras-tuner-1.4.7 kt-legacy-1.0.5\n","Cloning into 'Siren'...\n","remote: Enumerating objects: 382, done.\u001b[K\n","remote: Counting objects: 100% (85/85), done.\u001b[K\n","remote: Compressing objects: 100% (54/54), done.\u001b[K\n","remote: Total 382 (delta 41), reused 72 (delta 31), pack-reused 297 (from 1)\u001b[K\n","Receiving objects: 100% (382/382), 192.98 MiB | 29.81 MiB/s, done.\n","Resolving deltas: 100% (113/113), done.\n","Updating files: 100% (236/236), done.\n"]}],"source":["!pip install --upgrade keras==2.15.0\n","!pip install --upgrade tensorflow==2.15.0.post1\n","!pip install --upgrade scipy==1.11.4\n","!pip install tensorflow-addons==0.21.0\n","!pip install keras-tuner --upgrade\n","\n","# clone siren repository, refresh folder to see the siren repository\n","# runtime -> change runtime -> choose a gpu/tpu\n","!git clone https://github.com/hubtru/Siren/"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XZLkj3XkkyX-","outputId":"37b125a8-501f-44e1-8895-7610e1ff11e8","executionInfo":{"status":"ok","timestamp":1725549277832,"user_tz":-120,"elapsed":7813,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n","\n","TensorFlow Addons (TFA) has ended development and introduction of new features.\n","TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n","Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n","\n","For more information see: https://github.com/tensorflow/addons/issues/2807 \n","\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/ensure_tf_install.py:53: UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.11.0 and strictly below 2.14.0 (nightly versions are not supported). \n"," The versions of TensorFlow you are currently using is 2.15.0 and is not supported. \n","Some things might work, some things might not.\n","If you were to encounter a bug, do not file an issue.\n","If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version. \n","You can find the compatibility matrix in TensorFlow Addon's readme:\n","https://github.com/tensorflow/addons\n","  warnings.warn(\n"]}],"source":["import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import tensorflow_addons as tfa\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from tensorflow import keras as k\n","from keras import layers\n","from sklearn.model_selection import train_test_split\n","import cv2\n","import os\n","\n","os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\""]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UJ0S8lB4kyYA","outputId":"8912ded0-7952-43ab-e530-6dc04980b57a","executionInfo":{"status":"ok","timestamp":1725549277833,"user_tz":-120,"elapsed":8,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Num GPUs Available:  0\n","GPUs:  []\n"]}],"source":["# ============================== gpu info ==============================\n","print(\"Num GPUs Available: \", len(tf.config.list_physical_devices(\"GPU\")))\n","print(\"GPUs: \", tf.config.list_physical_devices(\"GPU\"))"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"NbpFko1MkyYA","executionInfo":{"status":"ok","timestamp":1725549277833,"user_tz":-120,"elapsed":7,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["# ============================== variables ==============================\n","# architecture\n","image_size = 256\n","filters = 256\n","depth = 4\n","columns = 4\n","kernel_size = 5\n","patch_size = 16\n","num_classes = 3\n","\n","in_channel = 3\n","img_rows, img_cols = image_size, image_size\n","input_shape = (img_rows, img_cols, in_channel)\n","\n","learning_rate = 0.001\n","weight_decay = 0.0001\n","batch_size = 8\n","epochs = 20"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"6dy_wa4xkyYA","executionInfo":{"status":"ok","timestamp":1725549277833,"user_tz":-120,"elapsed":6,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["# ============================== paths ==============================\n","# needed for generate_data\n","modality = \"tool\"\n","exp_name = f\"siren\"\n","sub_exp_name = f\"base\"\n","model_name = f\"{exp_name}_{sub_exp_name}_{modality}\"\n","model_save_path = f\"{model_name}.h5\"\n","\n","# dataset\n","csv_path = \"/content/Siren/dataset/labels_sample.csv\"\n","tool_path = f\"/content/Siren/dataset/{modality}\""]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XQ13FLwUkyYA","outputId":"f05a9384-5157-4fa6-ca89-0fa9882d4403","executionInfo":{"status":"ok","timestamp":1725549277833,"user_tz":-120,"elapsed":6,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["size check train/test/val 14 3 3 20\n"]}],"source":["# ============================== load/prepare dataset ==============================\n","df = pd.read_csv(csv_path)\n","\n","# RANDOM DATASET: we switch to toolbased\n","# shuffle, need same state for tool/spec/chips/multi\n","np.random.seed(55)\n","df = df.iloc[np.random.permutation(len(df))]\n","# train/test/val : 70/15/15\n","train_df, test_df = train_test_split(df, test_size=0.3)\n","test_df, val_df = train_test_split(test_df, test_size=0.5)\n","\n","print(\n","    \"size check train/test/val\",\n","    len(train_df),\n","    len(test_df),\n","    len(val_df),\n","    (len(train_df) + len(test_df) + len(val_df)),\n",")\n","\n","train_df[\"tool\"] = train_df.id.map(lambda id: f\"{tool_path}/{id}.jpg\")\n","test_df[\"tool\"] = test_df.id.map(lambda id: f\"{tool_path}/{id}.jpg\")\n","val_df[\"tool\"] = val_df.id.map(lambda id: f\"{tool_path}/{id}.jpg\")"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":546},"id":"n0yTp2klkyYB","outputId":"a25c4805-0a98-46e6-b966-e939e6e7ea19","executionInfo":{"status":"ok","timestamp":1725549278765,"user_tz":-120,"elapsed":936,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 600x600 with 0 Axes>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 500x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.figure(figsize=(6, 6))\n","sns.displot(train_df[\"tool_label\"])\n","plt.title(\"Label Distribution\")\n","plt.show()"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4NT9M2PBkyYB","outputId":"4aa6e6fc-c899-4992-db8d-6990b7bad455","executionInfo":{"status":"ok","timestamp":1725549278765,"user_tz":-120,"elapsed":3,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["check if permutation is same on all models\n","train_head          id image_label  tool_label                                     tool\n","14   T8R3B4       sharp           1   /content/Siren/dataset/tool/T8R3B4.jpg\n","10   T6R9B3        used           2   /content/Siren/dataset/tool/T6R9B3.jpg\n","0    T1R2B1       sharp           1   /content/Siren/dataset/tool/T1R2B1.jpg\n","3   T2R13B4      dulled           3  /content/Siren/dataset/tool/T2R13B4.jpg\n","1    T1R7B4        used           2   /content/Siren/dataset/tool/T1R7B4.jpg\n","test_head          id image_label  tool_label                                     tool\n","16   T9R7B4        used           2   /content/Siren/dataset/tool/T9R7B4.jpg\n","18  T10R3B4       sharp           1  /content/Siren/dataset/tool/T10R3B4.jpg\n","9   T5R12B1      dulled           3  /content/Siren/dataset/tool/T5R12B1.jpg\n","val_head          id image_label  tool_label                                     tool\n","5   T3R10B1      dulled           3  /content/Siren/dataset/tool/T3R10B1.jpg\n","13  T7R14B3      dulled           3  /content/Siren/dataset/tool/T7R14B3.jpg\n","12   T7R8B3        used           2   /content/Siren/dataset/tool/T7R8B3.jpg\n"]}],"source":["print(\"check if permutation is same on all models\")\n","print(\"train_head\", train_df.head())\n","print(\"test_head\", test_df.head())\n","print(\"val_head\", val_df.head())"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"1NbjHFbtkyYB","executionInfo":{"status":"ok","timestamp":1725549278765,"user_tz":-120,"elapsed":2,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["def read_tools(file_paths, img_rows, img_cols, channels):\n","    \"\"\"\n","    Reads the spectogram files from disk and normalizes the pixel values\n","      @params:\n","        file_paths - Array of file paths to read from\n","        img_rows - The image height.\n","        img_cols - The image width.\n","        as_grey - Read the image as Greyscale or RGB.\n","        channels - Number of channels.\n","      @returns:\n","        The created and compiled model (Model)\n","    \"\"\"\n","    images = []\n","\n","    for file_path in file_paths:\n","        img = cv2.imread(file_path)\n","        res = cv2.resize(img, dsize=(img_rows, img_cols), interpolation=cv2.INTER_CUBIC)\n","        images.append(res)\n","\n","    images = np.asarray(images, dtype=np.float32)\n","\n","    # normalize\n","    images = images / np.max(images)\n","\n","    # reshape to match Keras expectaions\n","    images = images.reshape(images.shape[0], img_rows, img_cols, channels)\n","\n","    return images"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"OAfWERRNkyYB","executionInfo":{"status":"ok","timestamp":1725549279153,"user_tz":-120,"elapsed":390,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["x_train_tool = read_tools(train_df.tool.values, img_rows, img_cols, in_channel)\n","x_test_tool = read_tools(test_df.tool.values, img_rows, img_cols, in_channel)\n","x_val_tool = read_tools(val_df.tool.values, img_rows, img_cols, in_channel)"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4xjVPNWGkyYB","outputId":"ee3a238c-b332-4766-d225-95113943ab98","executionInfo":{"status":"ok","timestamp":1725549279153,"user_tz":-120,"elapsed":3,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(14, 256, 256, 3)"]},"metadata":{},"execution_count":11}],"source":["x_train_tool.shape"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273},"id":"4GCLQrDSkyYC","outputId":"f01cd141-afc3-47f3-839f-23ca7403ef36","executionInfo":{"status":"ok","timestamp":1725549279820,"user_tz":-120,"elapsed":669,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PIL.Image.Image image mode=RGB size=256x256>"],"image/png":"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\n"},"metadata":{},"execution_count":12}],"source":["pil_img = tf.keras.utils.array_to_img(x_train_tool[0])\n","pil_img"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I7LtDW2YkyYC","outputId":"f81672f1-39a1-4bba-c201-57017b0123b8","executionInfo":{"status":"ok","timestamp":1725549279820,"user_tz":-120,"elapsed":10,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 2, 1, 3, 2, 2, 3, 2, 1, 1, 3, 1, 2, 3])"]},"metadata":{},"execution_count":13}],"source":["labels_train = train_df.tool_label.values\n","labels_test = test_df.tool_label.values\n","labels_val = val_df.tool_label.values\n","\n","labels_train"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"IjlVnJUskyYC","executionInfo":{"status":"ok","timestamp":1725549279820,"user_tz":-120,"elapsed":8,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["labels_train = labels_train - 1\n","labels_test = labels_test - 1\n","labels_val = labels_val - 1\n","\n","labels_train = tf.keras.utils.to_categorical(\n","    labels_train, num_classes=num_classes, dtype=\"float32\"\n",")\n","labels_test = tf.keras.utils.to_categorical(\n","    labels_test, num_classes=num_classes, dtype=\"float32\"\n",")\n","labels_val = tf.keras.utils.to_categorical(\n","    labels_val, num_classes=num_classes, dtype=\"float32\"\n",")"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EVRRCdKfkyYC","outputId":"c72c6ab5-a2d6-48b9-f21e-103e02f68290","executionInfo":{"status":"ok","timestamp":1725549279820,"user_tz":-120,"elapsed":7,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[1., 0., 0.],\n","       [0., 1., 0.],\n","       [1., 0., 0.],\n","       [0., 0., 1.],\n","       [0., 1., 0.],\n","       [0., 1., 0.],\n","       [0., 0., 1.],\n","       [0., 1., 0.],\n","       [1., 0., 0.],\n","       [1., 0., 0.],\n","       [0., 0., 1.],\n","       [1., 0., 0.],\n","       [0., 1., 0.],\n","       [0., 0., 1.]], dtype=float32)"]},"metadata":{},"execution_count":15}],"source":["labels_train"]},{"cell_type":"code","execution_count":16,"metadata":{"id":"nyv3kzkxkyYC","executionInfo":{"status":"ok","timestamp":1725549279820,"user_tz":-120,"elapsed":6,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["train_dataset = tf.data.Dataset.from_tensor_slices((x_train_tool, labels_train))\n","test_dataset = tf.data.Dataset.from_tensor_slices((x_test_tool, labels_test))\n","val_dataset = tf.data.Dataset.from_tensor_slices((x_val_tool, labels_val))"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"EhsSsR4qkyYD","executionInfo":{"status":"ok","timestamp":1725549280100,"user_tz":-120,"elapsed":285,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["# ============================== augment ==============================\n","auto = tf.data.AUTOTUNE\n","\n","data_augmentation = k.Sequential(\n","    [\n","        k.layers.RandomBrightness(factor=(-0.2, 0.2), value_range=(0, 1)),\n","        k.layers.RandomContrast(factor=0.2),\n","        k.layers.GaussianNoise(stddev=0.2),\n","    ],\n","    name=\"data_augmentation\",\n",")"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"4miDiAM8kyYD","executionInfo":{"status":"ok","timestamp":1725549280101,"user_tz":-120,"elapsed":4,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["def generate_datasets(images, is_train=False, shuffle=False):\n","    dataset = images\n","    if shuffle:\n","        dataset = dataset.shuffle(batch_size * 10)\n","    dataset = dataset.batch(batch_size)\n","    if is_train:\n","        dataset = dataset.map(\n","            lambda x, y: (data_augmentation(x), y), num_parallel_calls=auto\n","        )\n","    return dataset.prefetch(auto)"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"CEBI5IDIkyYD","executionInfo":{"status":"ok","timestamp":1725549280433,"user_tz":-120,"elapsed":335,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["train_ds = generate_datasets(train_dataset, is_train=True, shuffle=True)\n","val_ds = generate_datasets(val_dataset)\n","test_ds = generate_datasets(test_dataset)"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0tzOjy08kyYD","outputId":"e14b8658-7ec2-4450-a279-349c8afe4026","executionInfo":{"status":"ok","timestamp":1725549280434,"user_tz":-120,"elapsed":5,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<_PrefetchDataset element_spec=(TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 3), dtype=tf.float32, name=None))>"]},"metadata":{},"execution_count":20}],"source":["train_ds"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"Ft_pS17FkyYD","executionInfo":{"status":"ok","timestamp":1725549280434,"user_tz":-120,"elapsed":4,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["#Functions\n","#https://github.com/hubtru/minape\n","def activation_module(x):\n","    x = layers.Activation(\"gelu\")(x)\n","    return layers.BatchNormalization()(x)\n","\n","\n","def base_module(x, filters: int, patch_size: int):\n","    x = layers.Conv2D(filters, kernel_size=patch_size, strides=patch_size)(x)\n","    return activation_module(x)\n","\n","\n","def depth_point_module(\n","    x, pe_block, column, level, depth, r_arr, filters: int, kernel_size: int\n","):\n","    if column == 0:\n","        # Depthwise convolution.\n","        x0 = x\n","        x = layers.DepthwiseConv2D(kernel_size=kernel_size, padding=\"same\")(x)\n","        x = layers.Add()([activation_module(x), x0])  # Residual.\n","\n","        # Pointwise convolution.\n","        x = layers.Conv2D(filters, kernel_size=1)(x)\n","        x = activation_module(x)\n","\n","        r_arr = reverasable_output_array(x, r_arr, column, level)\n","        return x, r_arr\n","    else:\n","        if level == 0:\n","            x0 = fusion_block_double(r_arr[column - 1][level + 1], pe_block)\n","        elif level < len(r_arr[0]) - 1 and level != 0:\n","            x0 = fusion_block_double(r_arr[column - 1][level + 1], x)\n","        else:\n","            x0 = x\n","        x = layers.DepthwiseConv2D(kernel_size=kernel_size, padding=\"same\")(x0)\n","        x = layers.Add()([activation_module(x), x0])  # Residual.\n","\n","        # Pointwise convolution.\n","        x = layers.Conv2D(filters, kernel_size=1)(x)\n","        x = activation_module(x)\n","\n","        if column != 0:\n","            x = fusion_block_double(r_arr[column - 1][level], x)\n","\n","        r_arr = reverasable_output_array(x, r_arr, column, level)\n","\n","        return x, r_arr\n","\n","\n","def fusion_block_double(x, y):\n","    return layers.Add()([x, y])\n","\n","\n","def reverasable_output_array(x, r_arr, column, level):\n","    r_arr[column][level] = x\n","    return r_arr\n","\n","\n","def load_siren(\n","    image_size=image_size,\n","    filters=filters,\n","    depth=depth,\n","    columns=columns,\n","    kernel_size=kernel_size,\n","    patch_size=patch_size,\n","    num_classes=num_classes,\n","):\n","    r_arr = [[None for _ in range(depth)] for _ in range(columns)]\n","\n","    inputs = k.Input(input_shape)\n","    # x = layers.Rescaling(scale=1.0 / 255)(inputs)         # already rescaled\n","\n","    # Extract patch embeddings.\n","    x = base_module(inputs, filters, patch_size)\n","    pe_block = x\n","\n","    # Siren blocks.\n","    for column in range(columns):\n","        for level in range(depth):\n","            x, r_arr = depth_point_module(\n","                x, pe_block, column, level, depth, r_arr, filters, kernel_size\n","            )\n","\n","    # Classification block.\n","    x = layers.GlobalAvgPool2D()(x)\n","    outputs = layers.Dense(num_classes, activation=\"softmax\")(x)\n","\n","    return k.Model(inputs, outputs)"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"dkbScaZUkyYD","executionInfo":{"status":"ok","timestamp":1725549280434,"user_tz":-120,"elapsed":3,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["def launch_experiment(model):\n","    optimizer = tfa.optimizers.AdamW(\n","        learning_rate=learning_rate, weight_decay=weight_decay\n","    )\n","\n","    model.compile(\n","        optimizer=optimizer,\n","        loss=\"categorical_crossentropy\",\n","        metrics=[\"accuracy\"],\n","    )\n","\n","    history = model.fit(\n","        train_ds,\n","        validation_data=val_ds,\n","        epochs=epochs,\n","    )\n","\n","    _, accuracy = model.evaluate(test_ds)\n","    print(f\"Test accuracy: {round(accuracy * 100, 2)}%\")\n","\n","    return history, model"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jsmEwqaZkyYE","outputId":"89dae69d-2402-49c2-f8c8-fa76c1053439","executionInfo":{"status":"ok","timestamp":1725549283744,"user_tz":-120,"elapsed":3313,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"model\"\n","__________________________________________________________________________________________________\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","==================================================================================================\n"," input_1 (InputLayer)        [(None, 256, 256, 3)]        0         []                            \n","                                                                                                  \n"," conv2d (Conv2D)             (None, 16, 16, 256)          196864    ['input_1[0][0]']             \n","                                                                                                  \n"," activation (Activation)     (None, 16, 16, 256)          0         ['conv2d[0][0]']              \n","                                                                                                  \n"," batch_normalization (Batch  (None, 16, 16, 256)          1024      ['activation[0][0]']          \n"," Normalization)                                                                                   \n","                                                                                                  \n"," depthwise_conv2d (Depthwis  (None, 16, 16, 256)          6656      ['batch_normalization[0][0]'] \n"," eConv2D)                                                                                         \n","                                                                                                  \n"," activation_1 (Activation)   (None, 16, 16, 256)          0         ['depthwise_conv2d[0][0]']    \n","                                                                                                  \n"," batch_normalization_1 (Bat  (None, 16, 16, 256)          1024      ['activation_1[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add (Add)                   (None, 16, 16, 256)          0         ['batch_normalization_1[0][0]'\n","                                                                    , 'batch_normalization[0][0]']\n","                                                                                                  \n"," conv2d_1 (Conv2D)           (None, 16, 16, 256)          65792     ['add[0][0]']                 \n","                                                                                                  \n"," activation_2 (Activation)   (None, 16, 16, 256)          0         ['conv2d_1[0][0]']            \n","                                                                                                  \n"," batch_normalization_2 (Bat  (None, 16, 16, 256)          1024      ['activation_2[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_1 (Depthw  (None, 16, 16, 256)          6656      ['batch_normalization_2[0][0]'\n"," iseConv2D)                                                         ]                             \n","                                                                                                  \n"," activation_3 (Activation)   (None, 16, 16, 256)          0         ['depthwise_conv2d_1[0][0]']  \n","                                                                                                  \n"," batch_normalization_3 (Bat  (None, 16, 16, 256)          1024      ['activation_3[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_1 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_3[0][0]'\n","                                                                    , 'batch_normalization_2[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," conv2d_2 (Conv2D)           (None, 16, 16, 256)          65792     ['add_1[0][0]']               \n","                                                                                                  \n"," activation_4 (Activation)   (None, 16, 16, 256)          0         ['conv2d_2[0][0]']            \n","                                                                                                  \n"," batch_normalization_4 (Bat  (None, 16, 16, 256)          1024      ['activation_4[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_4 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_4[0][0]'\n","                                                                    , 'batch_normalization[0][0]']\n","                                                                                                  \n"," depthwise_conv2d_4 (Depthw  (None, 16, 16, 256)          6656      ['add_4[0][0]']               \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," depthwise_conv2d_2 (Depthw  (None, 16, 16, 256)          6656      ['batch_normalization_4[0][0]'\n"," iseConv2D)                                                         ]                             \n","                                                                                                  \n"," activation_9 (Activation)   (None, 16, 16, 256)          0         ['depthwise_conv2d_4[0][0]']  \n","                                                                                                  \n"," activation_5 (Activation)   (None, 16, 16, 256)          0         ['depthwise_conv2d_2[0][0]']  \n","                                                                                                  \n"," batch_normalization_9 (Bat  (None, 16, 16, 256)          1024      ['activation_9[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," batch_normalization_5 (Bat  (None, 16, 16, 256)          1024      ['activation_5[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_5 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_9[0][0]'\n","                                                                    , 'add_4[0][0]']              \n","                                                                                                  \n"," add_2 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_5[0][0]'\n","                                                                    , 'batch_normalization_4[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," conv2d_5 (Conv2D)           (None, 16, 16, 256)          65792     ['add_5[0][0]']               \n","                                                                                                  \n"," conv2d_3 (Conv2D)           (None, 16, 16, 256)          65792     ['add_2[0][0]']               \n","                                                                                                  \n"," activation_10 (Activation)  (None, 16, 16, 256)          0         ['conv2d_5[0][0]']            \n","                                                                                                  \n"," activation_6 (Activation)   (None, 16, 16, 256)          0         ['conv2d_3[0][0]']            \n","                                                                                                  \n"," batch_normalization_10 (Ba  (None, 16, 16, 256)          1024      ['activation_10[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_6 (Bat  (None, 16, 16, 256)          1024      ['activation_6[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_6 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_2[0][0]'\n","                                                                    , 'batch_normalization_10[0][0\n","                                                                    ]']                           \n","                                                                                                  \n"," add_7 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_6[0][0]'\n","                                                                    , 'add_6[0][0]']              \n","                                                                                                  \n"," depthwise_conv2d_5 (Depthw  (None, 16, 16, 256)          6656      ['add_7[0][0]']               \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," depthwise_conv2d_3 (Depthw  (None, 16, 16, 256)          6656      ['batch_normalization_6[0][0]'\n"," iseConv2D)                                                         ]                             \n","                                                                                                  \n"," activation_11 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_5[0][0]']  \n","                                                                                                  \n"," activation_7 (Activation)   (None, 16, 16, 256)          0         ['depthwise_conv2d_3[0][0]']  \n","                                                                                                  \n"," batch_normalization_11 (Ba  (None, 16, 16, 256)          1024      ['activation_11[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_7 (Bat  (None, 16, 16, 256)          1024      ['activation_7[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_8 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_11[0][0]\n","                                                                    ',                            \n","                                                                     'add_7[0][0]']               \n","                                                                                                  \n"," add_3 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_7[0][0]'\n","                                                                    , 'batch_normalization_6[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," conv2d_6 (Conv2D)           (None, 16, 16, 256)          65792     ['add_8[0][0]']               \n","                                                                                                  \n"," conv2d_4 (Conv2D)           (None, 16, 16, 256)          65792     ['add_3[0][0]']               \n","                                                                                                  \n"," activation_12 (Activation)  (None, 16, 16, 256)          0         ['conv2d_6[0][0]']            \n","                                                                                                  \n"," activation_8 (Activation)   (None, 16, 16, 256)          0         ['conv2d_4[0][0]']            \n","                                                                                                  \n"," batch_normalization_12 (Ba  (None, 16, 16, 256)          1024      ['activation_12[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_8 (Bat  (None, 16, 16, 256)          1024      ['activation_8[0][0]']        \n"," chNormalization)                                                                                 \n","                                                                                                  \n"," add_9 (Add)                 (None, 16, 16, 256)          0         ['batch_normalization_4[0][0]'\n","                                                                    , 'batch_normalization_12[0][0\n","                                                                    ]']                           \n","                                                                                                  \n"," add_10 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_8[0][0]'\n","                                                                    , 'add_9[0][0]']              \n","                                                                                                  \n"," add_15 (Add)                (None, 16, 16, 256)          0         ['add_9[0][0]',               \n","                                                                     'batch_normalization[0][0]'] \n","                                                                                                  \n"," depthwise_conv2d_6 (Depthw  (None, 16, 16, 256)          6656      ['add_10[0][0]']              \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," depthwise_conv2d_8 (Depthw  (None, 16, 16, 256)          6656      ['add_15[0][0]']              \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," activation_13 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_6[0][0]']  \n","                                                                                                  \n"," activation_17 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_8[0][0]']  \n","                                                                                                  \n"," batch_normalization_13 (Ba  (None, 16, 16, 256)          1024      ['activation_13[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_17 (Ba  (None, 16, 16, 256)          1024      ['activation_17[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_11 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_13[0][0]\n","                                                                    ',                            \n","                                                                     'add_10[0][0]']              \n","                                                                                                  \n"," add_16 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_17[0][0]\n","                                                                    ',                            \n","                                                                     'add_15[0][0]']              \n","                                                                                                  \n"," conv2d_7 (Conv2D)           (None, 16, 16, 256)          65792     ['add_11[0][0]']              \n","                                                                                                  \n"," conv2d_9 (Conv2D)           (None, 16, 16, 256)          65792     ['add_16[0][0]']              \n","                                                                                                  \n"," activation_14 (Activation)  (None, 16, 16, 256)          0         ['conv2d_7[0][0]']            \n","                                                                                                  \n"," activation_18 (Activation)  (None, 16, 16, 256)          0         ['conv2d_9[0][0]']            \n","                                                                                                  \n"," batch_normalization_14 (Ba  (None, 16, 16, 256)          1024      ['activation_14[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_18 (Ba  (None, 16, 16, 256)          1024      ['activation_18[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_12 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_6[0][0]'\n","                                                                    , 'batch_normalization_14[0][0\n","                                                                    ]']                           \n","                                                                                                  \n"," add_17 (Add)                (None, 16, 16, 256)          0         ['add_6[0][0]',               \n","                                                                     'batch_normalization_18[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_18 (Add)                (None, 16, 16, 256)          0         ['add_12[0][0]',              \n","                                                                     'add_17[0][0]']              \n","                                                                                                  \n"," depthwise_conv2d_7 (Depthw  (None, 16, 16, 256)          6656      ['add_12[0][0]']              \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," depthwise_conv2d_9 (Depthw  (None, 16, 16, 256)          6656      ['add_18[0][0]']              \n"," iseConv2D)                                                                                       \n","                                                                                                  \n"," activation_15 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_7[0][0]']  \n","                                                                                                  \n"," activation_19 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_9[0][0]']  \n","                                                                                                  \n"," batch_normalization_15 (Ba  (None, 16, 16, 256)          1024      ['activation_15[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_19 (Ba  (None, 16, 16, 256)          1024      ['activation_19[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_13 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_15[0][0]\n","                                                                    ',                            \n","                                                                     'add_12[0][0]']              \n","                                                                                                  \n"," add_19 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_19[0][0]\n","                                                                    ',                            \n","                                                                     'add_18[0][0]']              \n","                                                                                                  \n"," conv2d_8 (Conv2D)           (None, 16, 16, 256)          65792     ['add_13[0][0]']              \n","                                                                                                  \n"," conv2d_10 (Conv2D)          (None, 16, 16, 256)          65792     ['add_19[0][0]']              \n","                                                                                                  \n"," activation_16 (Activation)  (None, 16, 16, 256)          0         ['conv2d_8[0][0]']            \n","                                                                                                  \n"," activation_20 (Activation)  (None, 16, 16, 256)          0         ['conv2d_10[0][0]']           \n","                                                                                                  \n"," batch_normalization_16 (Ba  (None, 16, 16, 256)          1024      ['activation_16[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_20 (Ba  (None, 16, 16, 256)          1024      ['activation_20[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_14 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_8[0][0]'\n","                                                                    , 'batch_normalization_16[0][0\n","                                                                    ]']                           \n","                                                                                                  \n"," add_20 (Add)                (None, 16, 16, 256)          0         ['add_9[0][0]',               \n","                                                                     'batch_normalization_20[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_21 (Add)                (None, 16, 16, 256)          0         ['add_14[0][0]',              \n","                                                                     'add_20[0][0]']              \n","                                                                                                  \n"," add_26 (Add)                (None, 16, 16, 256)          0         ['add_20[0][0]',              \n","                                                                     'batch_normalization[0][0]'] \n","                                                                                                  \n"," depthwise_conv2d_10 (Depth  (None, 16, 16, 256)          6656      ['add_21[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_12 (Depth  (None, 16, 16, 256)          6656      ['add_26[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_21 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_10[0][0]'] \n","                                                                                                  \n"," activation_25 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_12[0][0]'] \n","                                                                                                  \n"," batch_normalization_21 (Ba  (None, 16, 16, 256)          1024      ['activation_21[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_25 (Ba  (None, 16, 16, 256)          1024      ['activation_25[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_22 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_21[0][0]\n","                                                                    ',                            \n","                                                                     'add_21[0][0]']              \n","                                                                                                  \n"," add_27 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_25[0][0]\n","                                                                    ',                            \n","                                                                     'add_26[0][0]']              \n","                                                                                                  \n"," conv2d_11 (Conv2D)          (None, 16, 16, 256)          65792     ['add_22[0][0]']              \n","                                                                                                  \n"," conv2d_13 (Conv2D)          (None, 16, 16, 256)          65792     ['add_27[0][0]']              \n","                                                                                                  \n"," activation_22 (Activation)  (None, 16, 16, 256)          0         ['conv2d_11[0][0]']           \n","                                                                                                  \n"," activation_26 (Activation)  (None, 16, 16, 256)          0         ['conv2d_13[0][0]']           \n","                                                                                                  \n"," batch_normalization_22 (Ba  (None, 16, 16, 256)          1024      ['activation_22[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_26 (Ba  (None, 16, 16, 256)          1024      ['activation_26[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_23 (Add)                (None, 16, 16, 256)          0         ['add_12[0][0]',              \n","                                                                     'batch_normalization_22[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_28 (Add)                (None, 16, 16, 256)          0         ['add_17[0][0]',              \n","                                                                     'batch_normalization_26[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_29 (Add)                (None, 16, 16, 256)          0         ['add_23[0][0]',              \n","                                                                     'add_28[0][0]']              \n","                                                                                                  \n"," depthwise_conv2d_11 (Depth  (None, 16, 16, 256)          6656      ['add_23[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," depthwise_conv2d_13 (Depth  (None, 16, 16, 256)          6656      ['add_29[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_23 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_11[0][0]'] \n","                                                                                                  \n"," activation_27 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_13[0][0]'] \n","                                                                                                  \n"," batch_normalization_23 (Ba  (None, 16, 16, 256)          1024      ['activation_23[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_27 (Ba  (None, 16, 16, 256)          1024      ['activation_27[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_24 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_23[0][0]\n","                                                                    ',                            \n","                                                                     'add_23[0][0]']              \n","                                                                                                  \n"," add_30 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_27[0][0]\n","                                                                    ',                            \n","                                                                     'add_29[0][0]']              \n","                                                                                                  \n"," conv2d_12 (Conv2D)          (None, 16, 16, 256)          65792     ['add_24[0][0]']              \n","                                                                                                  \n"," conv2d_14 (Conv2D)          (None, 16, 16, 256)          65792     ['add_30[0][0]']              \n","                                                                                                  \n"," activation_24 (Activation)  (None, 16, 16, 256)          0         ['conv2d_12[0][0]']           \n","                                                                                                  \n"," activation_28 (Activation)  (None, 16, 16, 256)          0         ['conv2d_14[0][0]']           \n","                                                                                                  \n"," batch_normalization_24 (Ba  (None, 16, 16, 256)          1024      ['activation_24[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," batch_normalization_28 (Ba  (None, 16, 16, 256)          1024      ['activation_28[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_25 (Add)                (None, 16, 16, 256)          0         ['add_14[0][0]',              \n","                                                                     'batch_normalization_24[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_31 (Add)                (None, 16, 16, 256)          0         ['add_20[0][0]',              \n","                                                                     'batch_normalization_28[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," add_32 (Add)                (None, 16, 16, 256)          0         ['add_25[0][0]',              \n","                                                                     'add_31[0][0]']              \n","                                                                                                  \n"," depthwise_conv2d_14 (Depth  (None, 16, 16, 256)          6656      ['add_32[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_29 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_14[0][0]'] \n","                                                                                                  \n"," batch_normalization_29 (Ba  (None, 16, 16, 256)          1024      ['activation_29[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_33 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_29[0][0]\n","                                                                    ',                            \n","                                                                     'add_32[0][0]']              \n","                                                                                                  \n"," conv2d_15 (Conv2D)          (None, 16, 16, 256)          65792     ['add_33[0][0]']              \n","                                                                                                  \n"," activation_30 (Activation)  (None, 16, 16, 256)          0         ['conv2d_15[0][0]']           \n","                                                                                                  \n"," batch_normalization_30 (Ba  (None, 16, 16, 256)          1024      ['activation_30[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_34 (Add)                (None, 16, 16, 256)          0         ['add_23[0][0]',              \n","                                                                     'batch_normalization_30[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," depthwise_conv2d_15 (Depth  (None, 16, 16, 256)          6656      ['add_34[0][0]']              \n"," wiseConv2D)                                                                                      \n","                                                                                                  \n"," activation_31 (Activation)  (None, 16, 16, 256)          0         ['depthwise_conv2d_15[0][0]'] \n","                                                                                                  \n"," batch_normalization_31 (Ba  (None, 16, 16, 256)          1024      ['activation_31[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_35 (Add)                (None, 16, 16, 256)          0         ['batch_normalization_31[0][0]\n","                                                                    ',                            \n","                                                                     'add_34[0][0]']              \n","                                                                                                  \n"," conv2d_16 (Conv2D)          (None, 16, 16, 256)          65792     ['add_35[0][0]']              \n","                                                                                                  \n"," activation_32 (Activation)  (None, 16, 16, 256)          0         ['conv2d_16[0][0]']           \n","                                                                                                  \n"," batch_normalization_32 (Ba  (None, 16, 16, 256)          1024      ['activation_32[0][0]']       \n"," tchNormalization)                                                                                \n","                                                                                                  \n"," add_36 (Add)                (None, 16, 16, 256)          0         ['add_25[0][0]',              \n","                                                                     'batch_normalization_32[0][0]\n","                                                                    ']                            \n","                                                                                                  \n"," global_average_pooling2d (  (None, 256)                  0         ['add_36[0][0]']              \n"," GlobalAveragePooling2D)                                                                          \n","                                                                                                  \n"," dense (Dense)               (None, 3)                    771       ['global_average_pooling2d[0][\n","                                                                    0]']                          \n","                                                                                                  \n","==================================================================================================\n","Total params: 1390595 (5.30 MB)\n","Trainable params: 1373699 (5.24 MB)\n","Non-trainable params: 16896 (66.00 KB)\n","__________________________________________________________________________________________________\n"]}],"source":["model = load_siren()\n","model.summary()"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YOorEc13kyYE","outputId":"816234b9-10c7-4d8a-9547-5c178704c914","executionInfo":{"status":"ok","timestamp":1725549334247,"user_tz":-120,"elapsed":50506,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/20\n","2/2 [==============================] - 22s 4s/step - loss: 1.5886 - accuracy: 0.4286 - val_loss: 1.4503 - val_accuracy: 0.3333\n","Epoch 2/20\n","2/2 [==============================] - 3s 1s/step - loss: 1.6571 - accuracy: 0.6429 - val_loss: 2.3971 - val_accuracy: 0.3333\n","Epoch 3/20\n","2/2 [==============================] - 2s 807ms/step - loss: 1.6782 - accuracy: 0.6429 - val_loss: 4.2693 - val_accuracy: 0.0000e+00\n","Epoch 4/20\n","2/2 [==============================] - 1s 531ms/step - loss: 1.7322 - accuracy: 0.6429 - val_loss: 8.4038 - val_accuracy: 0.0000e+00\n","Epoch 5/20\n","2/2 [==============================] - 1s 527ms/step - loss: 0.7776 - accuracy: 0.6429 - val_loss: 11.3703 - val_accuracy: 0.0000e+00\n","Epoch 6/20\n","2/2 [==============================] - 1s 518ms/step - loss: 2.0188 - accuracy: 0.4286 - val_loss: 8.2281 - val_accuracy: 0.0000e+00\n","Epoch 7/20\n","2/2 [==============================] - 1s 541ms/step - loss: 1.9863 - accuracy: 0.3571 - val_loss: 6.9173 - val_accuracy: 0.0000e+00\n","Epoch 8/20\n","2/2 [==============================] - 1s 541ms/step - loss: 2.1639 - accuracy: 0.4286 - val_loss: 5.5563 - val_accuracy: 0.0000e+00\n","Epoch 9/20\n","2/2 [==============================] - 1s 737ms/step - loss: 2.0417 - accuracy: 0.5000 - val_loss: 6.2734 - val_accuracy: 0.0000e+00\n","Epoch 10/20\n","2/2 [==============================] - 2s 863ms/step - loss: 1.7446 - accuracy: 0.2857 - val_loss: 3.2309 - val_accuracy: 0.0000e+00\n","Epoch 11/20\n","2/2 [==============================] - 2s 765ms/step - loss: 1.5906 - accuracy: 0.7143 - val_loss: 1.9244 - val_accuracy: 0.6667\n","Epoch 12/20\n","2/2 [==============================] - 1s 611ms/step - loss: 1.2977 - accuracy: 0.4286 - val_loss: 1.8134 - val_accuracy: 0.6667\n","Epoch 13/20\n","2/2 [==============================] - 1s 573ms/step - loss: 0.4289 - accuracy: 0.8571 - val_loss: 2.3460 - val_accuracy: 0.3333\n","Epoch 14/20\n","2/2 [==============================] - 1s 594ms/step - loss: 1.1621 - accuracy: 0.5000 - val_loss: 2.2815 - val_accuracy: 0.0000e+00\n","Epoch 15/20\n","2/2 [==============================] - 1s 509ms/step - loss: 1.1822 - accuracy: 0.6429 - val_loss: 2.7150 - val_accuracy: 0.0000e+00\n","Epoch 16/20\n","2/2 [==============================] - 1s 532ms/step - loss: 1.1658 - accuracy: 0.4286 - val_loss: 2.2372 - val_accuracy: 0.0000e+00\n","Epoch 17/20\n","2/2 [==============================] - 1s 545ms/step - loss: 0.6874 - accuracy: 0.8571 - val_loss: 1.3281 - val_accuracy: 0.0000e+00\n","Epoch 18/20\n","2/2 [==============================] - 1s 525ms/step - loss: 1.1496 - accuracy: 0.5714 - val_loss: 1.6261 - val_accuracy: 0.3333\n","Epoch 19/20\n","2/2 [==============================] - 1s 590ms/step - loss: 0.7666 - accuracy: 0.8571 - val_loss: 2.7068 - val_accuracy: 0.3333\n","Epoch 20/20\n","2/2 [==============================] - 2s 817ms/step - loss: 1.1084 - accuracy: 0.5714 - val_loss: 3.3266 - val_accuracy: 0.3333\n","1/1 [==============================] - 0s 127ms/step - loss: 5.6713 - accuracy: 0.3333\n","Test accuracy: 33.33%\n"]}],"source":["history, model = launch_experiment(model)"]},{"cell_type":"code","execution_count":25,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RtzFNVvTkyYE","outputId":"c18a088e-93e9-4338-8e44-86e9c64cd638","executionInfo":{"status":"ok","timestamp":1725549334487,"user_tz":-120,"elapsed":252,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n","  saving_api.save_model(\n"]}],"source":["model.save(model_save_path)"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bh_T0Ln7kyYE","outputId":"b4de9db5-73a9-4e46-a7cc-d863e3138cbb","executionInfo":{"status":"ok","timestamp":1725549335810,"user_tz":-120,"elapsed":1325,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 1s 1s/step\n"]}],"source":["pred = model.predict(test_ds)"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"81LUlxK8kyYE","executionInfo":{"status":"ok","timestamp":1725549335810,"user_tz":-120,"elapsed":3,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[],"source":["# ============================== generate data ==============================\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from scipy import interp\n","from itertools import cycle\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import classification_report\n","from sklearn.metrics import PredictionErrorDisplay\n","from sklearn import metrics\n","import tensorflow as tf\n","from tensorflow import keras as k"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"s2xqy1yskyYE","outputId":"db56f3f6-d70a-40e1-8b9e-0c4e2324363c","executionInfo":{"status":"ok","timestamp":1725549336389,"user_tz":-120,"elapsed":582,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.plot(history.history[\"loss\"])\n","plt.plot(history.history[\"val_loss\"])\n","plt.title(\"model loss\")\n","plt.ylabel(\"loss\")\n","plt.xlabel(\"epoch\")\n","plt.legend([\"train\", \"val\"], loc=\"upper left\")\n","plt.show()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"7KEwPcwqkyYF","outputId":"39c16a12-94bb-4fdd-93b9-4ff424efbaaf","executionInfo":{"status":"ok","timestamp":1725549336390,"user_tz":-120,"elapsed":10,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["plt.plot(history.history[\"accuracy\"])\n","plt.plot(history.history[\"val_accuracy\"])\n","plt.title(\"model accuracy\")\n","plt.ylabel(\"accuracy\")\n","plt.xlabel(\"epoch\")\n","plt.legend([\"train\", \"val\"], loc=\"upper left\")\n","plt.show()"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":524},"id":"PQ8Tw-y0kyYF","outputId":"04cfa518-7b5e-49d8-f770-bf7800d41f9b","executionInfo":{"status":"ok","timestamp":1725549336710,"user_tz":-120,"elapsed":329,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 0s 70ms/step\n"]},{"output_type":"stream","name":"stderr","text":["<ipython-input-30-0227eecc12f4>:27: DeprecationWarning: scipy.interp is deprecated and will be removed in SciPy 2.0.0, use numpy.interp instead\n","  mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["x_test = test_ds\n","y_test = labels_test\n","y_score = model.predict(x_test)\n","y_test = y_test\n","\n","n_classes = num_classes\n","# Plot linewidth.\n","lw = 2\n","# Compute ROC curve and ROC area for each class\n","fpr = dict()\n","tpr = dict()\n","roc_auc = dict()\n","for i in range(n_classes):\n","    fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])\n","    roc_auc[i] = auc(fpr[i], tpr[i])\n","\n","# Compute micro-average ROC curve and ROC area\n","fpr[\"micro\"], tpr[\"micro\"], _ = roc_curve(y_test.ravel(), y_score.ravel())\n","roc_auc[\"micro\"] = auc(fpr[\"micro\"], tpr[\"micro\"])\n","\n","# First aggregate all false positive rates\n","all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))\n","\n","# Then interpolate all ROC curves at this points\n","mean_tpr = np.zeros_like(all_fpr)\n","for i in range(n_classes):\n","    mean_tpr += interp(all_fpr, fpr[i], tpr[i])\n","\n","# Finally average it and compute AUC\n","mean_tpr /= n_classes\n","\n","fpr[\"macro\"] = all_fpr\n","tpr[\"macro\"] = mean_tpr\n","roc_auc[\"macro\"] = auc(fpr[\"macro\"], tpr[\"macro\"])\n","\n","# Plot all ROC curves\n","plt.figure(1)\n","plt.plot(\n","    fpr[\"micro\"],\n","    tpr[\"micro\"],\n","    label=\"micro-average ROC curve (area = {0:0.2f})\" \"\".format(roc_auc[\"micro\"]),\n","    color=\"deeppink\",\n","    linestyle=\":\",\n","    linewidth=4,\n",")\n","\n","plt.plot(\n","    fpr[\"macro\"],\n","    tpr[\"macro\"],\n","    label=\"macro-average ROC curve (area = {0:0.2f})\" \"\".format(roc_auc[\"macro\"]),\n","    color=\"navy\",\n","    linestyle=\":\",\n","    linewidth=4,\n",")\n","\n","colors = cycle([\"aqua\", \"darkorange\", \"cornflowerblue\"])\n","for i, color in zip(range(n_classes), colors):\n","    plt.plot(\n","        fpr[i],\n","        tpr[i],\n","        color=color,\n","        lw=lw,\n","        label=\"ROC curve of class {0} (area = {1:0.2f})\" \"\".format(i, roc_auc[i]),\n","    )\n","\n","plt.plot([0, 1], [0, 1], \"k--\", lw=lw)\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 multi-class\")\n","plt.legend(loc=\"lower right\")\n","plt.show()"]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"109I2o3QkyYF","outputId":"ce61856a-f2cd-49ee-d9b2-153c3bfa117f","executionInfo":{"status":"ok","timestamp":1725549336711,"user_tz":-120,"elapsed":9,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[[1 0 0]\n"," [1 0 0]\n"," [0 1 0]]\n","              precision    recall  f1-score   support\n","\n","           0       0.50      1.00      0.67         1\n","           1       0.00      0.00      0.00         1\n","           2       0.00      0.00      0.00         1\n","\n","    accuracy                           0.33         3\n","   macro avg       0.17      0.33      0.22         3\n","weighted avg       0.17      0.33      0.22         3\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}],"source":["y_classes = np.argmax(y_score, axis=1)\n","labels_test_f1 = np.argmax(y_test, axis=1)\n","\n","print(metrics.confusion_matrix(labels_test_f1, y_classes))\n","print(classification_report(labels_test_f1, y_classes))"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GXzwXPTpkyYF","outputId":"acb86c1e-f9b8-4063-e789-4edaa3e712e9","executionInfo":{"status":"ok","timestamp":1725549336711,"user_tz":-120,"elapsed":7,"user":{"displayName":"Kaito Kid","userId":"01531680708781259329"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["AUC:  1.0\n"]}],"source":["fpr, tpr, thresholds = metrics.roc_curve(labels_test_f1, y_classes, pos_label=2)\n","print(\"AUC: \", metrics.auc(fpr, tpr))"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.17"},"orig_nbformat":4},"nbformat":4,"nbformat_minor":0}