jupyter_notebooks/regression/siren_reg_base_chip.ipynb

{"cells":[{"cell_type":"code","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/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ot7yTlloo0cj","outputId":"5aadba2c-9c17-4afe-a97b-e13e8e74991e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting tensorflow_addons\n","  Downloading tensorflow_addons-0.23.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (611 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/611.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     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keras_tuner-1.4.6-py3-none-any.whl (128 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m128.9/128.9 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.15.0)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (23.2)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from keras-tuner) (2.31.0)\n","Collecting kt-legacy (from keras-tuner)\n","  Downloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (3.6)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->keras-tuner) (2023.11.17)\n","Installing collected packages: kt-legacy, keras-tuner\n","Successfully installed keras-tuner-1.4.6 kt-legacy-1.0.5\n","Cloning into 'Siren'...\n","remote: Enumerating objects: 313, done.\u001b[K\n","remote: Counting objects: 100% (16/16), done.\u001b[K\n","remote: Compressing objects: 100% (14/14), done.\u001b[K\n","remote: Total 313 (delta 9), reused 7 (delta 2), pack-reused 297\u001b[K\n","Receiving objects: 100% (313/313), 57.54 MiB | 11.19 MiB/s, done.\n","Resolving deltas: 100% (81/81), done.\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"w1CuF_6zovaB","outputId":"9d9b1109-c443-4f7d-8b7a-44f1ec514059"},"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"]}],"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\"] = \"3\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ECpc4dC1ovaC","outputId":"0781bcaa-934e-462c-c891-f00c1761315b"},"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":null,"metadata":{"id":"-CTc71C7ovaD"},"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 = 100"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pRX9PhPMovaE"},"outputs":[],"source":["# ============================== paths ==============================\n","# needed for generate_data\n","modality = \"chip\"\n","exp_name = f\"siren_reg\"\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_reg_sample.csv\"\n","chip_path = f\"/content/Siren/dataset/{modality}\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5hSoqOaGovaE","outputId":"48c5ce25-7f6f-4726-998b-cb89d57394a1"},"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[\"chip\"] = train_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")\n","test_df[\"chip\"] = test_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")\n","val_df[\"chip\"] = val_df.id.map(lambda id: f\"{chip_path}/{id}.jpg\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":546},"id":"nUltYHOOovaF","outputId":"411ad0a2-f792-4ccb-a745-0e404f7d6fc8"},"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[\"gaps\"])\n","plt.title(\"Label Distribution\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":546},"id":"uyswzG4sovaF","outputId":"b955ec4f-9549-4212-ab8a-9801edbe0e6b"},"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[\"flank_wear\"])\n","plt.title(\"Label Distribution\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":546},"id":"HV_CDSADovaF","outputId":"036e0ff7-8841-48cb-8458-8235f38beebc"},"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[\"overhang\"])\n","plt.title(\"Label Distribution\")\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ha0PPa6jovaF","outputId":"265f5a3c-f24f-42aa-d5a0-8adf75359ba5"},"outputs":[{"output_type":"stream","name":"stdout","text":["check if permutation is same on all models\n","train_head          id   gaps  flank_wear  overhang  \\\n","14   T8R3B4   6.88       40.92     14.54   \n","10   T6R9B3  12.94       95.63     16.65   \n","0    T1R2B1   7.67       34.89     48.61   \n","3   T2R13B4   0.00      122.59     25.55   \n","1    T1R7B4  13.89       85.41     50.25   \n","\n","                                       chip  \n","14   /content/Siren/dataset/chip/T8R3B4.jpg  \n","10   /content/Siren/dataset/chip/T6R9B3.jpg  \n","0    /content/Siren/dataset/chip/T1R2B1.jpg  \n","3   /content/Siren/dataset/chip/T2R13B4.jpg  \n","1    /content/Siren/dataset/chip/T1R7B4.jpg  \n","test_head          id   gaps  flank_wear  overhang  \\\n","16   T9R7B4   0.00       94.62     17.05   \n","18  T10R3B4   0.00       44.26      7.10   \n","9   T5R12B1  21.79      334.39     16.14   \n","\n","                                       chip  \n","16   /content/Siren/dataset/chip/T9R7B4.jpg  \n","18  /content/Siren/dataset/chip/T10R3B4.jpg  \n","9   /content/Siren/dataset/chip/T5R12B1.jpg  \n","val_head          id   gaps  flank_wear  overhang  \\\n","5   T3R10B1   5.00      128.39     35.26   \n","13  T7R14B3  13.67      141.91     14.62   \n","12   T7R8B3   7.45       77.05     19.02   \n","\n","                                       chip  \n","5   /content/Siren/dataset/chip/T3R10B1.jpg  \n","13  /content/Siren/dataset/chip/T7R14B3.jpg  \n","12   /content/Siren/dataset/chip/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":null,"metadata":{"id":"dTDfi2SUovaG"},"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":null,"metadata":{"id":"x-BN15kdovaG"},"outputs":[],"source":["x_train_chip = read_tools(train_df.chip.values, img_rows, img_cols, in_channel)\n","x_test_chip = read_tools(test_df.chip.values, img_rows, img_cols, in_channel)\n","x_val_chip = read_tools(val_df.chip.values, img_rows, img_cols, in_channel)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tFRuEx_govaG","outputId":"3fcb7486-d381-4684-ecaa-343b21d75103"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(14, 256, 256, 3)"]},"metadata":{},"execution_count":13}],"source":["x_train_chip.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273},"id":"Oh3DV9mZovaH","outputId":"7720120f-b4ac-4141-aaaf-d19db1dd0b13"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PIL.Image.Image image mode=RGB 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\n"},"metadata":{},"execution_count":14}],"source":["pil_img = tf.keras.utils.array_to_img(x_train_chip[0])\n","pil_img"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UjxrJ7DyovaH"},"outputs":[],"source":["labels_train = train_df[[\"gaps\", \"flank_wear\", \"overhang\"]]\n","labels_test = test_df[[\"gaps\", \"flank_wear\", \"overhang\"]]\n","labels_val = val_df[[\"gaps\", \"flank_wear\", \"overhang\"]]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wXG_EamqovaH"},"outputs":[],"source":["train_dataset = tf.data.Dataset.from_tensor_slices((x_train_chip, labels_train))\n","test_dataset = tf.data.Dataset.from_tensor_slices((x_test_chip, labels_test))\n","val_dataset = tf.data.Dataset.from_tensor_slices((x_val_chip, labels_val))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2sKnzVK5ovaH"},"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":null,"metadata":{"id":"m8ldkI-FovaI"},"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":null,"metadata":{"id":"XGXsvVr_ovaI"},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8bLai9CHovaI","outputId":"0caf6fe4-d395-4971-c32b-f49edf9469bb"},"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.float64, name=None))>"]},"metadata":{},"execution_count":20}],"source":["train_ds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XLMco2wYovaI"},"outputs":[],"source":["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=\"linear\")(x)\n","\n","    return k.Model(inputs, outputs)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JgA27Fj7ovaI"},"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, loss=\"mse\", metrics=[tf.keras.metrics.MeanSquaredError()]\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 error: {round(accuracy, 2)}\")\n","\n","    return history, model"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IZ-rfk_8ovaI","outputId":"30a3cb6d-9ee5-426a-83c8-cf1a7fe351c7"},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"86f0lXiLovaJ","outputId":"499b1662-1cb8-4a0a-d423-f53b01eb3cb9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/100\n","2/2 [==============================] - 10s 2s/step - loss: 3940.9214 - mean_squared_error: 3940.9214 - val_loss: 4975.6958 - val_mean_squared_error: 4975.6958\n","Epoch 2/100\n","2/2 [==============================] - 1s 570ms/step - loss: 3838.7305 - mean_squared_error: 3838.7305 - val_loss: 5032.2065 - val_mean_squared_error: 5032.2065\n","Epoch 3/100\n","2/2 [==============================] - 1s 502ms/step - loss: 3685.1953 - mean_squared_error: 3685.1953 - val_loss: 5094.5024 - val_mean_squared_error: 5094.5024\n","Epoch 4/100\n","2/2 [==============================] - 1s 529ms/step - loss: 3528.2573 - mean_squared_error: 3528.2573 - val_loss: 5180.7603 - val_mean_squared_error: 5180.7603\n","Epoch 5/100\n","2/2 [==============================] - 1s 622ms/step - loss: 3721.5923 - mean_squared_error: 3721.5923 - val_loss: 5054.4800 - val_mean_squared_error: 5054.4800\n","Epoch 6/100\n","2/2 [==============================] - 1s 530ms/step - loss: 3358.0781 - mean_squared_error: 3358.0781 - val_loss: 5179.7388 - val_mean_squared_error: 5179.7388\n","Epoch 7/100\n","2/2 [==============================] - 1s 546ms/step - loss: 3327.7856 - mean_squared_error: 3327.7856 - val_loss: 5620.9126 - val_mean_squared_error: 5620.9126\n","Epoch 8/100\n","2/2 [==============================] - 2s 953ms/step - loss: 3233.7444 - mean_squared_error: 3233.7444 - val_loss: 5509.4058 - val_mean_squared_error: 5509.4058\n","Epoch 9/100\n","2/2 [==============================] - 2s 832ms/step - loss: 3422.9910 - mean_squared_error: 3422.9910 - val_loss: 5100.1299 - val_mean_squared_error: 5100.1299\n","Epoch 10/100\n","2/2 [==============================] - 1s 598ms/step - loss: 3390.5005 - mean_squared_error: 3390.5005 - val_loss: 5101.7261 - val_mean_squared_error: 5101.7261\n","Epoch 11/100\n","2/2 [==============================] - 1s 551ms/step - loss: 3136.8398 - mean_squared_error: 3136.8398 - val_loss: 5325.9370 - val_mean_squared_error: 5325.9370\n","Epoch 12/100\n","2/2 [==============================] - 1s 525ms/step - loss: 3215.9980 - mean_squared_error: 3215.9980 - val_loss: 4954.8091 - val_mean_squared_error: 4954.8091\n","Epoch 13/100\n","2/2 [==============================] - 1s 513ms/step - loss: 3510.8933 - mean_squared_error: 3510.8933 - val_loss: 5149.7285 - val_mean_squared_error: 5149.7285\n","Epoch 14/100\n","2/2 [==============================] - 1s 513ms/step - loss: 3199.9089 - mean_squared_error: 3199.9089 - val_loss: 5225.8999 - val_mean_squared_error: 5225.8999\n","Epoch 15/100\n","2/2 [==============================] - 1s 670ms/step - loss: 3035.2200 - mean_squared_error: 3035.2200 - val_loss: 4888.8853 - val_mean_squared_error: 4888.8853\n","Epoch 16/100\n","2/2 [==============================] - 2s 894ms/step - loss: 2983.7949 - mean_squared_error: 2983.7947 - val_loss: 4812.4004 - val_mean_squared_error: 4812.4004\n","Epoch 17/100\n","2/2 [==============================] - 1s 548ms/step - loss: 2972.4192 - mean_squared_error: 2972.4192 - val_loss: 5142.5259 - val_mean_squared_error: 5142.5259\n","Epoch 18/100\n","2/2 [==============================] - 1s 520ms/step - loss: 3021.5559 - mean_squared_error: 3021.5559 - val_loss: 4690.8267 - val_mean_squared_error: 4690.8267\n","Epoch 19/100\n","2/2 [==============================] - 1s 559ms/step - loss: 2877.6746 - mean_squared_error: 2877.6746 - val_loss: 4519.5928 - val_mean_squared_error: 4519.5928\n","Epoch 20/100\n","2/2 [==============================] - 1s 706ms/step - loss: 2816.9565 - mean_squared_error: 2816.9565 - val_loss: 4551.4033 - val_mean_squared_error: 4551.4033\n","Epoch 21/100\n","2/2 [==============================] - 2s 863ms/step - loss: 2775.3723 - mean_squared_error: 2775.3723 - val_loss: 5018.0972 - val_mean_squared_error: 5018.0972\n","Epoch 22/100\n","2/2 [==============================] - 1s 550ms/step - loss: 2958.3386 - mean_squared_error: 2958.3386 - val_loss: 4354.7783 - val_mean_squared_error: 4354.7783\n","Epoch 23/100\n","2/2 [==============================] - 1s 555ms/step - loss: 2789.2310 - mean_squared_error: 2789.2310 - val_loss: 3917.7292 - val_mean_squared_error: 3917.7292\n","Epoch 24/100\n","2/2 [==============================] - 2s 835ms/step - loss: 2897.9082 - mean_squared_error: 2897.9082 - val_loss: 4397.0396 - val_mean_squared_error: 4397.0396\n","Epoch 25/100\n","2/2 [==============================] - 1s 581ms/step - loss: 3055.5139 - mean_squared_error: 3055.5139 - val_loss: 4892.8638 - val_mean_squared_error: 4892.8638\n","Epoch 26/100\n","2/2 [==============================] - 1s 547ms/step - loss: 3021.8708 - mean_squared_error: 3021.8708 - val_loss: 5043.1460 - val_mean_squared_error: 5043.1460\n","Epoch 27/100\n","2/2 [==============================] - 1s 581ms/step - loss: 2772.1311 - mean_squared_error: 2772.1311 - val_loss: 4612.6997 - val_mean_squared_error: 4612.6997\n","Epoch 28/100\n","2/2 [==============================] - 2s 858ms/step - loss: 2697.9380 - mean_squared_error: 2697.9380 - val_loss: 4172.4351 - val_mean_squared_error: 4172.4351\n","Epoch 29/100\n","2/2 [==============================] - 1s 550ms/step - loss: 2616.7361 - mean_squared_error: 2616.7358 - val_loss: 4042.0027 - val_mean_squared_error: 4042.0027\n","Epoch 30/100\n","2/2 [==============================] - 1s 550ms/step - loss: 2714.6562 - mean_squared_error: 2714.6562 - val_loss: 4017.1755 - val_mean_squared_error: 4017.1755\n","Epoch 31/100\n","2/2 [==============================] - 1s 785ms/step - loss: 2631.4563 - mean_squared_error: 2631.4563 - val_loss: 3745.1399 - val_mean_squared_error: 3745.1399\n","Epoch 32/100\n","2/2 [==============================] - 2s 816ms/step - loss: 2625.2148 - mean_squared_error: 2625.2146 - val_loss: 3693.7805 - val_mean_squared_error: 3693.7805\n","Epoch 33/100\n","2/2 [==============================] - 1s 548ms/step - loss: 2564.1672 - mean_squared_error: 2564.1672 - val_loss: 4075.6052 - val_mean_squared_error: 4075.6052\n","Epoch 34/100\n","2/2 [==============================] - 2s 633ms/step - loss: 2982.0583 - mean_squared_error: 2982.0583 - val_loss: 3576.4004 - val_mean_squared_error: 3576.4004\n","Epoch 35/100\n","2/2 [==============================] - 1s 535ms/step - loss: 2551.5513 - mean_squared_error: 2551.5513 - val_loss: 3310.4641 - val_mean_squared_error: 3310.4641\n","Epoch 36/100\n","2/2 [==============================] - 1s 550ms/step - loss: 2705.4644 - mean_squared_error: 2705.4644 - val_loss: 3636.3699 - val_mean_squared_error: 3636.3699\n","Epoch 37/100\n","2/2 [==============================] - 1s 539ms/step - loss: 2555.9453 - mean_squared_error: 2555.9453 - val_loss: 3713.6272 - val_mean_squared_error: 3713.6272\n","Epoch 38/100\n","2/2 [==============================] - 1s 600ms/step - loss: 2379.6118 - mean_squared_error: 2379.6118 - val_loss: 3291.2590 - val_mean_squared_error: 3291.2590\n","Epoch 39/100\n","2/2 [==============================] - 1s 546ms/step - loss: 2379.9258 - mean_squared_error: 2379.9258 - val_loss: 3018.6169 - val_mean_squared_error: 3018.6169\n","Epoch 40/100\n","2/2 [==============================] - 2s 808ms/step - loss: 2281.9597 - mean_squared_error: 2281.9597 - val_loss: 2901.4766 - val_mean_squared_error: 2901.4766\n","Epoch 41/100\n","2/2 [==============================] - 1s 512ms/step - loss: 2657.5273 - mean_squared_error: 2657.5273 - val_loss: 3217.2424 - val_mean_squared_error: 3217.2424\n","Epoch 42/100\n","2/2 [==============================] - 1s 522ms/step - loss: 2267.4187 - mean_squared_error: 2267.4187 - val_loss: 3952.8779 - val_mean_squared_error: 3952.8779\n","Epoch 43/100\n","2/2 [==============================] - 1s 528ms/step - loss: 2491.2952 - mean_squared_error: 2491.2952 - val_loss: 3741.5381 - val_mean_squared_error: 3741.5381\n","Epoch 44/100\n","2/2 [==============================] - 1s 541ms/step - loss: 2210.7600 - mean_squared_error: 2210.7600 - val_loss: 3425.8230 - val_mean_squared_error: 3425.8230\n","Epoch 45/100\n","2/2 [==============================] - 1s 548ms/step - loss: 2228.7415 - mean_squared_error: 2228.7415 - val_loss: 3240.0398 - val_mean_squared_error: 3240.0398\n","Epoch 46/100\n","2/2 [==============================] - 1s 543ms/step - loss: 2193.7346 - mean_squared_error: 2193.7346 - val_loss: 3126.6609 - val_mean_squared_error: 3126.6609\n","Epoch 47/100\n","2/2 [==============================] - 1s 550ms/step - loss: 2079.2886 - mean_squared_error: 2079.2886 - val_loss: 3136.6074 - val_mean_squared_error: 3136.6074\n","Epoch 48/100\n","2/2 [==============================] - 1s 675ms/step - loss: 2023.6613 - mean_squared_error: 2023.6613 - val_loss: 3237.7170 - val_mean_squared_error: 3237.7170\n","Epoch 49/100\n","2/2 [==============================] - 2s 905ms/step - loss: 2015.5262 - mean_squared_error: 2015.5262 - val_loss: 3047.6963 - val_mean_squared_error: 3047.6963\n","Epoch 50/100\n","2/2 [==============================] - 2s 811ms/step - loss: 2047.1505 - mean_squared_error: 2047.1505 - val_loss: 2703.2756 - val_mean_squared_error: 2703.2756\n","Epoch 51/100\n","2/2 [==============================] - 1s 544ms/step - loss: 2203.5940 - mean_squared_error: 2203.5940 - val_loss: 2455.2791 - val_mean_squared_error: 2455.2791\n","Epoch 52/100\n","2/2 [==============================] - 1s 553ms/step - loss: 2028.9983 - mean_squared_error: 2028.9983 - val_loss: 2488.4678 - val_mean_squared_error: 2488.4678\n","Epoch 53/100\n","2/2 [==============================] - 1s 608ms/step - loss: 1999.1775 - mean_squared_error: 1999.1775 - val_loss: 2729.4077 - val_mean_squared_error: 2729.4077\n","Epoch 54/100\n","2/2 [==============================] - 1s 550ms/step - loss: 1842.8531 - mean_squared_error: 1842.8531 - val_loss: 2997.2842 - val_mean_squared_error: 2997.2842\n","Epoch 55/100\n","2/2 [==============================] - 1s 527ms/step - loss: 1874.7780 - mean_squared_error: 1874.7780 - val_loss: 3140.5706 - val_mean_squared_error: 3140.5706\n","Epoch 56/100\n","2/2 [==============================] - 1s 547ms/step - loss: 1854.4684 - mean_squared_error: 1854.4684 - val_loss: 2880.9646 - val_mean_squared_error: 2880.9646\n","Epoch 57/100\n","2/2 [==============================] - 1s 591ms/step - loss: 1829.4906 - mean_squared_error: 1829.4906 - val_loss: 2671.8389 - val_mean_squared_error: 2671.8389\n","Epoch 58/100\n","2/2 [==============================] - 2s 684ms/step - loss: 1678.9451 - mean_squared_error: 1678.9451 - val_loss: 2627.6064 - val_mean_squared_error: 2627.6064\n","Epoch 59/100\n","2/2 [==============================] - 1s 532ms/step - loss: 1921.6671 - mean_squared_error: 1921.6671 - val_loss: 2439.2161 - val_mean_squared_error: 2439.2161\n","Epoch 60/100\n","2/2 [==============================] - 1s 558ms/step - loss: 1582.2634 - mean_squared_error: 1582.2634 - val_loss: 2261.9094 - val_mean_squared_error: 2261.9094\n","Epoch 61/100\n","2/2 [==============================] - 1s 544ms/step - loss: 1656.6376 - mean_squared_error: 1656.6376 - val_loss: 2080.0830 - val_mean_squared_error: 2080.0830\n","Epoch 62/100\n","2/2 [==============================] - 1s 550ms/step - loss: 1513.2040 - mean_squared_error: 1513.2040 - val_loss: 1851.3013 - val_mean_squared_error: 1851.3013\n","Epoch 63/100\n","2/2 [==============================] - 1s 541ms/step - loss: 1538.1456 - mean_squared_error: 1538.1456 - val_loss: 1684.7963 - val_mean_squared_error: 1684.7963\n","Epoch 64/100\n","2/2 [==============================] - 1s 565ms/step - loss: 1503.4752 - mean_squared_error: 1503.4752 - val_loss: 1523.4897 - val_mean_squared_error: 1523.4897\n","Epoch 65/100\n","2/2 [==============================] - 2s 863ms/step - loss: 1449.5067 - mean_squared_error: 1449.5067 - val_loss: 1466.2350 - val_mean_squared_error: 1466.2350\n","Epoch 66/100\n","2/2 [==============================] - 1s 562ms/step - loss: 1446.2450 - mean_squared_error: 1446.2450 - val_loss: 1630.2310 - val_mean_squared_error: 1630.2310\n","Epoch 67/100\n","2/2 [==============================] - 1s 528ms/step - loss: 1453.0684 - mean_squared_error: 1453.0684 - val_loss: 1644.8081 - val_mean_squared_error: 1644.8081\n","Epoch 68/100\n","2/2 [==============================] - 1s 568ms/step - loss: 1291.7250 - mean_squared_error: 1291.7250 - val_loss: 1493.2764 - val_mean_squared_error: 1493.2764\n","Epoch 69/100\n","2/2 [==============================] - 2s 823ms/step - loss: 1321.9531 - mean_squared_error: 1321.9531 - val_loss: 1352.5018 - val_mean_squared_error: 1352.5018\n","Epoch 70/100\n","2/2 [==============================] - 2s 618ms/step - loss: 1264.9154 - mean_squared_error: 1264.9154 - val_loss: 1597.7284 - val_mean_squared_error: 1597.7284\n","Epoch 71/100\n","2/2 [==============================] - 1s 637ms/step - loss: 1220.9559 - mean_squared_error: 1220.9559 - val_loss: 1998.7988 - val_mean_squared_error: 1998.7988\n","Epoch 72/100\n","2/2 [==============================] - 2s 861ms/step - loss: 1411.9603 - mean_squared_error: 1411.9603 - val_loss: 1862.9961 - val_mean_squared_error: 1862.9961\n","Epoch 73/100\n","2/2 [==============================] - 1s 534ms/step - loss: 1093.4512 - mean_squared_error: 1093.4512 - val_loss: 1935.2012 - val_mean_squared_error: 1935.2012\n","Epoch 74/100\n","2/2 [==============================] - 1s 612ms/step - loss: 1109.5682 - mean_squared_error: 1109.5682 - val_loss: 2130.5168 - val_mean_squared_error: 2130.5168\n","Epoch 75/100\n","2/2 [==============================] - 1s 541ms/step - loss: 1016.5948 - mean_squared_error: 1016.5948 - val_loss: 2121.7649 - val_mean_squared_error: 2121.7649\n","Epoch 76/100\n","2/2 [==============================] - 1s 538ms/step - loss: 1027.7413 - mean_squared_error: 1027.7413 - val_loss: 1908.9946 - val_mean_squared_error: 1908.9946\n","Epoch 77/100\n","2/2 [==============================] - 1s 533ms/step - loss: 1093.0510 - mean_squared_error: 1093.0510 - val_loss: 1663.8810 - val_mean_squared_error: 1663.8810\n","Epoch 78/100\n","2/2 [==============================] - 1s 610ms/step - loss: 1032.5035 - mean_squared_error: 1032.5035 - val_loss: 1501.7123 - val_mean_squared_error: 1501.7123\n","Epoch 79/100\n","2/2 [==============================] - 1s 553ms/step - loss: 931.6075 - mean_squared_error: 931.6075 - val_loss: 1519.2894 - val_mean_squared_error: 1519.2894\n","Epoch 80/100\n","2/2 [==============================] - 1s 540ms/step - loss: 892.8688 - mean_squared_error: 892.8688 - val_loss: 1533.4337 - val_mean_squared_error: 1533.4337\n","Epoch 81/100\n","2/2 [==============================] - 1s 777ms/step - loss: 981.7867 - mean_squared_error: 981.7867 - val_loss: 1612.9814 - val_mean_squared_error: 1612.9814\n","Epoch 82/100\n","2/2 [==============================] - 1s 540ms/step - loss: 901.2326 - mean_squared_error: 901.2326 - val_loss: 1391.8120 - val_mean_squared_error: 1391.8120\n","Epoch 83/100\n","2/2 [==============================] - 1s 557ms/step - loss: 807.5446 - mean_squared_error: 807.5446 - val_loss: 1240.6298 - val_mean_squared_error: 1240.6298\n","Epoch 84/100\n","2/2 [==============================] - 1s 539ms/step - loss: 872.6932 - mean_squared_error: 872.6932 - val_loss: 1246.1051 - val_mean_squared_error: 1246.1051\n","Epoch 85/100\n","2/2 [==============================] - 1s 532ms/step - loss: 751.3587 - mean_squared_error: 751.3587 - val_loss: 1292.6091 - val_mean_squared_error: 1292.6091\n","Epoch 86/100\n","2/2 [==============================] - 1s 538ms/step - loss: 685.4395 - mean_squared_error: 685.4395 - val_loss: 1257.8976 - val_mean_squared_error: 1257.8976\n","Epoch 87/100\n","2/2 [==============================] - 1s 542ms/step - loss: 706.0817 - mean_squared_error: 706.0817 - val_loss: 1093.5499 - val_mean_squared_error: 1093.5499\n","Epoch 88/100\n","2/2 [==============================] - 1s 549ms/step - loss: 700.1211 - mean_squared_error: 700.1211 - val_loss: 865.1398 - val_mean_squared_error: 865.1398\n","Epoch 89/100\n","2/2 [==============================] - 1s 604ms/step - loss: 730.2951 - mean_squared_error: 730.2951 - val_loss: 759.5916 - val_mean_squared_error: 759.5916\n","Epoch 90/100\n","2/2 [==============================] - 2s 930ms/step - loss: 615.8714 - mean_squared_error: 615.8714 - val_loss: 671.1282 - val_mean_squared_error: 671.1282\n","Epoch 91/100\n","2/2 [==============================] - 2s 881ms/step - loss: 654.6939 - mean_squared_error: 654.6939 - val_loss: 636.3928 - val_mean_squared_error: 636.3928\n","Epoch 92/100\n","2/2 [==============================] - 1s 537ms/step - loss: 641.5180 - mean_squared_error: 641.5180 - val_loss: 661.4759 - val_mean_squared_error: 661.4759\n","Epoch 93/100\n","2/2 [==============================] - 1s 542ms/step - loss: 530.9825 - mean_squared_error: 530.9825 - val_loss: 798.5852 - val_mean_squared_error: 798.5852\n","Epoch 94/100\n","2/2 [==============================] - 1s 532ms/step - loss: 596.2057 - mean_squared_error: 596.2057 - val_loss: 819.0331 - val_mean_squared_error: 819.0331\n","Epoch 95/100\n","2/2 [==============================] - 1s 531ms/step - loss: 470.0270 - mean_squared_error: 470.0270 - val_loss: 726.8245 - val_mean_squared_error: 726.8245\n","Epoch 96/100\n","2/2 [==============================] - 1s 525ms/step - loss: 460.6156 - mean_squared_error: 460.6156 - val_loss: 703.5242 - val_mean_squared_error: 703.5242\n","Epoch 97/100\n","2/2 [==============================] - 1s 513ms/step - loss: 492.3954 - mean_squared_error: 492.3954 - val_loss: 848.5555 - val_mean_squared_error: 848.5555\n","Epoch 98/100\n","2/2 [==============================] - 1s 799ms/step - loss: 694.6177 - mean_squared_error: 694.6177 - val_loss: 899.1970 - val_mean_squared_error: 899.1970\n","Epoch 99/100\n","2/2 [==============================] - 2s 799ms/step - loss: 418.7508 - mean_squared_error: 418.7508 - val_loss: 985.5427 - val_mean_squared_error: 985.5427\n","Epoch 100/100\n","2/2 [==============================] - 1s 518ms/step - loss: 393.5549 - mean_squared_error: 393.5549 - val_loss: 1015.4630 - val_mean_squared_error: 1015.4630\n","1/1 [==============================] - 0s 72ms/step - loss: 8210.6094 - mean_squared_error: 8210.6094\n","Test error: 8210.61\n"]}],"source":["history, model = launch_experiment(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o44wPSbZovaJ","outputId":"a941587e-419a-421a-9c5e-8432f68d93aa"},"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2S1M9JSgovaJ","outputId":"0f72a010-b187-4024-998d-0efe6572e4ae"},"outputs":[{"output_type":"stream","name":"stdout","text":["1/1 [==============================] - 1s 828ms/step\n"]}],"source":["pred = model.predict(test_ds)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NXT7gX_fovaJ"},"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":null,"metadata":{"id":"VZRfKbFkovaJ"},"outputs":[],"source":["multi_pred = pred\n","gaps_pred, flank_wear_pred, overhang_pred = multi_pred.T\n","\n","multi_true = labels_test.to_numpy()\n","gaps_true, flank_wear_true, overhang_true = multi_true.T"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q4KexXKXovaJ"},"outputs":[],"source":["labels_list = [\n","    \"gaps\",\n","    \"flank_wear\",\n","    \"overhang\",\n","    \"multi\",\n","]\n","pred_list = [\n","    gaps_pred,\n","    flank_wear_pred,\n","    overhang_pred,\n","    multi_pred,\n","]\n","true_list = [\n","    gaps_true,\n","    flank_wear_true,\n","    overhang_true,\n","    multi_true,\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"zAK6shdiovaK","outputId":"c410c302-4e19-40a8-9ee5-c6cc74b3b716"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# loss\n","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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"bDiBHV-NovaK","outputId":"1e57c123-9ab0-4d42-b55b-06e112b5a35e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x400 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}],"source":["for label, y_pred, y_true in zip(labels_list, pred_list, true_list):\n","    # https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py\n","    fig, axs = plt.subplots(ncols=2, figsize=(8, 4))\n","    PredictionErrorDisplay.from_predictions(\n","        y_true=y_true,\n","        y_pred=y_pred,\n","        kind=\"actual_vs_predicted\",\n","        subsample=100,\n","        ax=axs[0],\n","        random_state=0,\n","    )\n","    axs[0].set_title(\"Actual vs. Predicted values\")\n","    PredictionErrorDisplay.from_predictions(\n","        y_true=y_true,\n","        y_pred=y_pred,\n","        kind=\"residual_vs_predicted\",\n","        subsample=100,\n","        ax=axs[1],\n","        random_state=0,\n","    )\n","    axs[1].set_title(\"Residuals vs. Predicted Values\")\n","    fig.suptitle(\"Plotting cross-validated predictions \" + label)\n","    plt.tight_layout()\n","    plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M-hXE9JZovaK"},"outputs":[],"source":["def min_shift(list1, list2):\n","    list1 = np.array(list1)\n","    list2 = np.array(list2)\n","    if list1.ndim == 2:\n","        for i in range(len(list1)):\n","            if np.min([list1, list2]) > 0:\n","                continue\n","            min = np.min([list1[i], list2[i]])\n","            list1[i] -= min\n","            list2[i] -= min\n","        return list1, list2\n","\n","    min = np.min([list1, list2])\n","    list1 = list1 - min\n","    list2 = list1 - min\n","\n","    return list1, list2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"npq7XayWovaK","outputId":"0c605541-b608-4371-a395-31b2df38436e"},"outputs":[{"output_type":"stream","name":"stdout","text":["===== gaps results =====\n","MAE:  12.696588370005289\n","MSE:  250.07008844813504\n","RMSE:  15.813604536857973\n","r2_score:  -1.3700625121320722\n","MSLE_min_shifted:  0.27901192366086197\n","MAPE:  1.818461450587341e+16\n","===== flank_wear results =====\n","MAE:  104.4538345336914\n","MSE:  24356.535705671955\n","RMSE:  156.06580569001\n","r2_score:  -0.5201594873214876\n","MSLE:  0.9390507586386306\n","MAPE:  0.47121314474800946\n","===== overhang results =====\n","MAE:  4.7096080271403\n","MSE:  25.220199046217875\n","RMSE:  5.021971629372061\n","r2_score:  -0.25022881251761664\n","MSLE:  0.1469404320750167\n","MAPE:  0.3612053662242595\n","===== multi results =====\n","MAE:  40.620010310279\n","MSE:  8210.608664388768\n","RMSE:  58.96712728541335\n","r2_score:  -0.7134836039903921\n","MSLE_min_shifted:  2.3678836187340333\n","MAPE:  6061538168624469.0\n"]}],"source":["for label, y_pred, y_true in zip(labels_list, pred_list, true_list):\n","    print(f\"===== {label} results =====\")\n","    print(\"MAE: \", metrics.mean_absolute_error(y_true, y_pred))\n","    print(\"MSE: \", metrics.mean_squared_error(y_true, y_pred))\n","    print(\"RMSE: \", metrics.mean_squared_error(y_true, y_pred, squared=False))\n","    print(\"r2_score: \", metrics.r2_score(y_true, y_pred))\n","    try:\n","        print(\"MSLE: \", metrics.mean_squared_log_error(y_true, y_pred))\n","    except ValueError as err:\n","        y_true_shift, y_pred_shift = min_shift(y_true, y_pred)\n","        print(\n","            \"MSLE_min_shifted: \",\n","            metrics.mean_squared_log_error(y_true_shift, y_pred_shift),\n","        )\n","    print(\"MAPE: \", metrics.mean_absolute_percentage_error(y_true, y_pred))"]}],"metadata":{"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,"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}