jupyter_notebooks/regression/siren_reg_base_multi_tc.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HPiciQkgqXN6","outputId":"0fdde55c-b8fa-4e4c-e931-eb6a6781f392"},"outputs":[{"name":"stdout","output_type":"stream","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[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m611.8/611.8 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow_addons) (23.2)\n","Collecting typeguard<3.0.0,>=2.7 (from tensorflow_addons)\n","  Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\n","Installing collected packages: typeguard, tensorflow_addons\n","Successfully installed tensorflow_addons-0.23.0 typeguard-2.13.3\n","Collecting keras-tuner\n","  Downloading 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[31m1.6 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: 332, done.\u001b[K\n","remote: Counting objects: 100% (35/35), done.\u001b[K\n","remote: Compressing objects: 100% (23/23), done.\u001b[K\n","remote: Total 332 (delta 16), reused 29 (delta 12), pack-reused 297\u001b[K\n","Receiving objects: 100% (332/332), 186.05 MiB | 26.83 MiB/s, done.\n","Resolving deltas: 100% (88/88), 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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IibRhxpmqRy5","outputId":"be1f45fa-8c77-4525-eddf-8f468c525512"},"outputs":[{"name":"stderr","output_type":"stream","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\"]=\"2\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H26BuZvtqRy6","outputId":"81bb881d-b7c5-412c-f1a2-1b4d2a5be83c"},"outputs":[{"name":"stdout","output_type":"stream","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":"HUFZjs0ZqRy7"},"outputs":[],"source":["# ============================== variables ==============================\n","image_size = 256\n","num_classes = 3\n","\n","in_channel_tool = 3\n","in_channel_chip = 3\n","img_rows, img_cols = image_size, image_size\n","input_shape_tool = (img_rows, img_cols, in_channel_tool)\n","input_shape_chip = (img_rows, img_cols, in_channel_chip)\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":"GeHN4Em2qRy7"},"outputs":[],"source":["# ============================== paths ==============================\n","# needed for generate_data\n","modality = \"multi_tc\"\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","model_tool_path = f\"/content/Siren/models/regression/{exp_name}_{sub_exp_name}_tool.h5\"\n","model_chip_path = f\"/content/Siren/models/regression/{exp_name}_{sub_exp_name}_chip.h5\"\n","\n","# dataset\n","csv_path = \"/content/Siren/dataset/labels_reg_sample.csv\"\n","tool_path = \"/content/Siren/dataset/tool\"\n","chip_path = \"/content/Siren/dataset/chip\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O6u3e4v-qRy7","outputId":"b00460e2-6857-4783-a510-8e3fce7e9cc2"},"outputs":[{"name":"stdout","output_type":"stream","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/chip/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\")\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":"99IFi5FTqRy8","outputId":"38bcb989-729c-4e4d-ddd9-2da8dbc5886c"},"outputs":[{"data":{"text/plain":["<Figure size 600x600 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":"pZTQ_HxbqRy8","outputId":"ffdd9d56-88e3-4772-a181-5b47f3a9a98b"},"outputs":[{"data":{"text/plain":["<Figure size 600x600 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":"Y9JgFnxGqRy8","outputId":"e70c1697-12d7-4fe8-bc4c-0d0e05680ca1"},"outputs":[{"data":{"text/plain":["<Figure size 600x600 with 0 Axes>"]},"metadata":{},"output_type":"display_data"},{"data":{"image/png":"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","text/plain":["<Figure size 500x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":"BKeZfek_qRy8","outputId":"3332d5eb-3f04-44f6-d63a-afbca518e8cd"},"outputs":[{"name":"stdout","output_type":"stream","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","                                       tool  \\\n","14   /content/Siren/dataset/tool/T8R3B4.jpg   \n","10   /content/Siren/dataset/tool/T6R9B3.jpg   \n","0    /content/Siren/dataset/tool/T1R2B1.jpg   \n","3   /content/Siren/dataset/tool/T2R13B4.jpg   \n","1    /content/Siren/dataset/tool/T1R7B4.jpg   \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","                                       tool  \\\n","16   /content/Siren/dataset/tool/T9R7B4.jpg   \n","18  /content/Siren/dataset/tool/T10R3B4.jpg   \n","9   /content/Siren/dataset/tool/T5R12B1.jpg   \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","                                       tool  \\\n","5   /content/Siren/dataset/tool/T3R10B1.jpg   \n","13  /content/Siren/dataset/tool/T7R14B3.jpg   \n","12   /content/Siren/dataset/tool/T7R8B3.jpg   \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":"deUWP_omqRy9"},"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":"uoLf61rZqRy9"},"outputs":[],"source":["x_train_tool = read_tools(train_df.tool.values, img_rows, img_cols, in_channel_tool)\n","x_test_tool = read_tools(test_df.tool.values, img_rows, img_cols, in_channel_tool)\n","x_val_tool = read_tools(val_df.tool.values, img_rows, img_cols, in_channel_tool)\n","\n","x_train_chip = read_tools(train_df.chip.values, img_rows, img_cols, in_channel_chip)\n","x_test_chip = read_tools(test_df.chip.values, img_rows, img_cols, in_channel_chip)\n","x_val_chip = read_tools(val_df.chip.values, img_rows, img_cols, in_channel_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"F8iYqt4nqRy9","outputId":"993c3d69-6b65-45e1-adaa-a3ae94793f63"},"outputs":[{"data":{"text/plain":["(14, 256, 256, 3)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["x_train_tool.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tNiyLIN1qRy9","outputId":"ca12870a-d020-4338-cb0d-75cd50c7eb00"},"outputs":[{"data":{"text/plain":["(14, 256, 256, 3)"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["x_train_chip.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273},"id":"USXCv7TXqRy9","outputId":"eee7de4e-fa5f-48a0-9bfc-33f04a502ec6"},"outputs":[{"data":{"image/png":"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","text/plain":["<PIL.Image.Image image mode=RGB size=256x256>"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["pil_img = tf.keras.utils.array_to_img(x_train_tool[0])\n","pil_img"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":273},"id":"BHZZS2lEqRy-","outputId":"eac7c9a1-f013-4867-d3bc-999d73dd6052"},"outputs":[{"data":{"image/png":"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","text/plain":["<PIL.Image.Image image mode=RGB size=256x256>"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["pil_img = tf.keras.utils.array_to_img(x_train_chip[0])\n","pil_img"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8uGdHSaSvVRI"},"outputs":[],"source":["# Split the last dimension into 3 separate spectrograms\n","spectrogram1 = x_train_spec[0, :, :, 0:3]  # First spectrogram (channels 0-2)\n","spectrogram2 = x_train_spec[0, :, :, 3:6]  # Second spectrogram (channels 3-5)\n","spectrogram3 = x_train_spec[0, :, :, 6:9]  # Third spectrogram (channels 6-8)\n","# Convert arrays to images\n","pil_img1 = tf.keras.utils.array_to_img(spectrogram1)\n","pil_img2 = tf.keras.utils.array_to_img(spectrogram2)\n","pil_img3 = tf.keras.utils.array_to_img(spectrogram3)\n","# Display the images\n","pil_img1\n","pil_img2\n","pil_img3"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"wRjx7XPnqRy-"},"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":"dXcDqLNSqRy-"},"outputs":[],"source":["# ============================== augment 1/2 ==============================\n","data_augmentation_tool = 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_tool\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iXCA_UGCqRy-"},"outputs":[],"source":["# ============================== augment 2/2 ==============================\n","data_augmentation_chip = 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_chip\",\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YYQ91KKGqRy-"},"outputs":[],"source":["x_train_tool = data_augmentation_tool(x_train_tool)\n","x_train_chip = data_augmentation_chip(x_train_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1PNNCCcjqRy_"},"outputs":[],"source":["train_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_tool\": x_train_tool,\n","            \"input_1_chip\": x_train_chip,\n","        },\n","        labels_train,\n","    )\n",")\n","test_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_tool\": x_test_tool,\n","            \"input_1_chip\": x_test_chip,\n","        },\n","        labels_test,\n","    )\n",")\n","val_dataset = tf.data.Dataset.from_tensor_slices(\n","    (\n","        {\n","            \"input_1_tool\": x_val_tool,\n","            \"input_1_chip\": x_val_chip,\n","        },\n","        labels_val,\n","    )\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"z0ZN9fCbqRy_"},"outputs":[],"source":["auto = tf.data.AUTOTUNE\n","inputs1 = k.Input(input_shape_tool)\n","inputs3 = k.Input(input_shape_chip)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Q-5TJ2LbqRy_"},"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","        pass\n","    return dataset.prefetch(auto)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Gu4vzyTmqRy_"},"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":"2DLF2LNiqRy_","outputId":"5abb3295-d282-4d50-a9d1-2a418e8c6f1d"},"outputs":[{"data":{"text/plain":["<_PrefetchDataset element_spec=({'input_1_tool': TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None), 'input_1_chip': TensorSpec(shape=(None, 256, 256, 3), dtype=tf.float32, name=None)}, TensorSpec(shape=(None, 3), dtype=tf.float64, name=None))>"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["train_ds"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9vEsEQocqRy_"},"outputs":[],"source":["model_tool = k.models.load_model(model_tool_path, compile=False)\n","model_chip = k.models.load_model(model_chip_path, compile=False)\n","\n","model_tool.trainable = False\n","model_chip.trainable = False\n","\n","\n","def load_siren(model_tool, model_chip):\n","    inputs1 = model_tool.input\n","    inputs1._name = \"input_tool\"\n","    inputs3 = model_chip.input\n","    inputs3._name = \"input_chip\"\n","\n","    truncated_model_tool = k.Model(\n","        inputs=model_tool.input, outputs=model_tool.layers[-2].output\n","    )\n","    truncated_model_chip = k.Model(\n","        inputs=model_chip.input, outputs=model_chip.layers[-2].output\n","    )\n","    mergedOut = k.layers.Concatenate()(\n","        [\n","            truncated_model_tool.output,\n","            truncated_model_chip.output,\n","        ]\n","    )\n","    output = layers.Dense(num_classes, activation=\"linear\", name=\"output_mult\")(\n","        mergedOut\n","    )\n","    return k.Model(inputs=[inputs1, inputs3], outputs=output)\n","\n","\n","for layer in model_tool.layers:\n","    layer._name = layer.name + str(\"_tool\")\n","for layer in model_chip.layers:\n","    layer._name = layer.name + str(\"_chip\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aFLpGwNzqRzA"},"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":"F7wGCC7lqRzA","outputId":"ea27ca5e-835e-49ca-b6a7-167d66cfdfe4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"model_2\"\n","__________________________________________________________________________________________________\n"," Layer (type)                Output Shape                 Param #   Connected to                  \n","==================================================================================================\n"," input_1_tool (InputLayer)   [(None, 256, 256, 3)]        0         []                            \n","                                                                                                  \n"," input_1_chip (InputLayer)   [(None, 256, 256, 3)]        0         []                            \n","                                                                                                  \n"," conv2d_tool (Conv2D)        (None, 16, 16, 256)          196864    ['input_1_tool[0][0]']        \n","                                                                                                  \n"," conv2d_chip (Conv2D)        (None, 16, 16, 256)          196864    ['input_1_chip[0][0]']        \n","                                                                                                  \n"," activation_tool (Activatio  (None, 16, 16, 256)          0         ['conv2d_tool[0][0]']         \n"," n)                                                                                               \n","                                                                                                  \n"," activation_chip (Activatio  (None, 16, 16, 256)          0         ['conv2d_chip[0][0]']         \n"," n)                                                                                               \n","                                                                                                  \n"," batch_normalization_tool (  (None, 16, 16, 256)          1024      ['activation_tool[0][0]']     \n"," BatchNormalization)                                                                              \n","                                                                                                  \n"," batch_normalization_chip (  (None, 16, 16, 256)          1024      ['activation_chip[0][0]']     \n"," BatchNormalization)                                                                              \n","                                                                                                  \n"," depthwise_conv2d_tool (Dep  (None, 16, 16, 256)          6656      ['batch_normalization_tool[0][\n"," thwiseConv2D)                                                      0]']                          \n","                                                                                                  \n"," depthwise_conv2d_chip (Dep  (None, 16, 16, 256)          6656      ['batch_normalization_chip[0][\n"," thwiseConv2D)                                                      0]']                          \n","                                                                                                  \n"," activation_1_tool (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_tool[0][0]'\n"," ion)                                                               ]                             \n","                                                                                                  \n"," activation_1_chip (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_chip[0][0]'\n"," ion)                                                               ]                             \n","                                                                                                  \n"," batch_normalization_1_tool  (None, 16, 16, 256)          1024      ['activation_1_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," batch_normalization_1_chip  (None, 16, 16, 256)          1024      ['activation_1_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_tool (Add)              (None, 16, 16, 256)          0         ['batch_normalization_1_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_tool[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," add_chip (Add)              (None, 16, 16, 256)          0         ['batch_normalization_1_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_chip[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," conv2d_1_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_tool[0][0]']            \n","                                                                                                  \n"," conv2d_1_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_chip[0][0]']            \n","                                                                                                  \n"," activation_2_tool (Activat  (None, 16, 16, 256)          0         ['conv2d_1_tool[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," activation_2_chip (Activat  (None, 16, 16, 256)          0         ['conv2d_1_chip[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_2_tool  (None, 16, 16, 256)          1024      ['activation_2_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," batch_normalization_2_chip  (None, 16, 16, 256)          1024      ['activation_2_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," depthwise_conv2d_1_tool (D  (None, 16, 16, 256)          6656      ['batch_normalization_2_tool[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," depthwise_conv2d_1_chip (D  (None, 16, 16, 256)          6656      ['batch_normalization_2_chip[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," activation_3_tool (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_1_tool[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," activation_3_chip (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_1_chip[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," batch_normalization_3_tool  (None, 16, 16, 256)          1024      ['activation_3_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," batch_normalization_3_chip  (None, 16, 16, 256)          1024      ['activation_3_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_1_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_3_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_2_tool[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," add_1_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_3_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_2_chip[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," conv2d_2_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_1_tool[0][0]']          \n","                                                                                                  \n"," conv2d_2_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_1_chip[0][0]']          \n","                                                                                                  \n"," activation_4_tool (Activat  (None, 16, 16, 256)          0         ['conv2d_2_tool[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," activation_4_chip (Activat  (None, 16, 16, 256)          0         ['conv2d_2_chip[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_4_tool  (None, 16, 16, 256)          1024      ['activation_4_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," batch_normalization_4_chip  (None, 16, 16, 256)          1024      ['activation_4_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_4_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_4_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_tool[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," add_4_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_4_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_chip[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," depthwise_conv2d_4_tool (D  (None, 16, 16, 256)          6656      ['add_4_tool[0][0]']          \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_4_chip (D  (None, 16, 16, 256)          6656      ['add_4_chip[0][0]']          \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_2_tool (D  (None, 16, 16, 256)          6656      ['batch_normalization_4_tool[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," activation_9_tool (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_4_tool[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," depthwise_conv2d_2_chip (D  (None, 16, 16, 256)          6656      ['batch_normalization_4_chip[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," activation_9_chip (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_4_chip[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," activation_5_tool (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_2_tool[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," batch_normalization_9_tool  (None, 16, 16, 256)          1024      ['activation_9_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," activation_5_chip (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_2_chip[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," batch_normalization_9_chip  (None, 16, 16, 256)          1024      ['activation_9_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," batch_normalization_5_tool  (None, 16, 16, 256)          1024      ['activation_5_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_5_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_9_tool[0\n","                                                                    ][0]',                        \n","                                                                     'add_4_tool[0][0]']          \n","                                                                                                  \n"," batch_normalization_5_chip  (None, 16, 16, 256)          1024      ['activation_5_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_5_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_9_chip[0\n","                                                                    ][0]',                        \n","                                                                     'add_4_chip[0][0]']          \n","                                                                                                  \n"," add_2_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_5_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_4_tool[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," conv2d_5_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_5_tool[0][0]']          \n","                                                                                                  \n"," add_2_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_5_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_4_chip[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," conv2d_5_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_5_chip[0][0]']          \n","                                                                                                  \n"," conv2d_3_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_2_tool[0][0]']          \n","                                                                                                  \n"," activation_10_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_5_tool[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," conv2d_3_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_2_chip[0][0]']          \n","                                                                                                  \n"," activation_10_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_5_chip[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_6_tool (Activat  (None, 16, 16, 256)          0         ['conv2d_3_tool[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_10_too  (None, 16, 16, 256)          1024      ['activation_10_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," activation_6_chip (Activat  (None, 16, 16, 256)          0         ['conv2d_3_chip[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_10_chi  (None, 16, 16, 256)          1024      ['activation_10_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_6_tool  (None, 16, 16, 256)          1024      ['activation_6_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_6_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_2_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_10_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," batch_normalization_6_chip  (None, 16, 16, 256)          1024      ['activation_6_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_6_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_2_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_10_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_7_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_6_tool[0\n","                                                                    ][0]',                        \n","                                                                     'add_6_tool[0][0]']          \n","                                                                                                  \n"," add_7_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_6_chip[0\n","                                                                    ][0]',                        \n","                                                                     'add_6_chip[0][0]']          \n","                                                                                                  \n"," depthwise_conv2d_5_tool (D  (None, 16, 16, 256)          6656      ['add_7_tool[0][0]']          \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_5_chip (D  (None, 16, 16, 256)          6656      ['add_7_chip[0][0]']          \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_3_tool (D  (None, 16, 16, 256)          6656      ['batch_normalization_6_tool[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," activation_11_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_5_tool[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," depthwise_conv2d_3_chip (D  (None, 16, 16, 256)          6656      ['batch_normalization_6_chip[0\n"," epthwiseConv2D)                                                    ][0]']                        \n","                                                                                                  \n"," activation_11_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_5_chip[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_7_tool (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_3_tool[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," batch_normalization_11_too  (None, 16, 16, 256)          1024      ['activation_11_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," activation_7_chip (Activat  (None, 16, 16, 256)          0         ['depthwise_conv2d_3_chip[0][0\n"," ion)                                                               ]']                           \n","                                                                                                  \n"," batch_normalization_11_chi  (None, 16, 16, 256)          1024      ['activation_11_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_7_tool  (None, 16, 16, 256)          1024      ['activation_7_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_8_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_11_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_7_tool[0][0]']          \n","                                                                                                  \n"," batch_normalization_7_chip  (None, 16, 16, 256)          1024      ['activation_7_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_8_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_11_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_7_chip[0][0]']          \n","                                                                                                  \n"," add_3_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_7_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_6_tool[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," conv2d_6_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_8_tool[0][0]']          \n","                                                                                                  \n"," add_3_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_7_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_6_chip[0\n","                                                                    ][0]']                        \n","                                                                                                  \n"," conv2d_6_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_8_chip[0][0]']          \n","                                                                                                  \n"," conv2d_4_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_3_tool[0][0]']          \n","                                                                                                  \n"," activation_12_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_6_tool[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," conv2d_4_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_3_chip[0][0]']          \n","                                                                                                  \n"," activation_12_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_6_chip[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_8_tool (Activat  (None, 16, 16, 256)          0         ['conv2d_4_tool[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_12_too  (None, 16, 16, 256)          1024      ['activation_12_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," activation_8_chip (Activat  (None, 16, 16, 256)          0         ['conv2d_4_chip[0][0]']       \n"," ion)                                                                                             \n","                                                                                                  \n"," batch_normalization_12_chi  (None, 16, 16, 256)          1024      ['activation_12_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_8_tool  (None, 16, 16, 256)          1024      ['activation_8_tool[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_9_tool (Add)            (None, 16, 16, 256)          0         ['batch_normalization_4_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_12_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," batch_normalization_8_chip  (None, 16, 16, 256)          1024      ['activation_8_chip[0][0]']   \n","  (BatchNormalization)                                                                            \n","                                                                                                  \n"," add_9_chip (Add)            (None, 16, 16, 256)          0         ['batch_normalization_4_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_12_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_10_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_8_tool[0\n","                                                                    ][0]',                        \n","                                                                     'add_9_tool[0][0]']          \n","                                                                                                  \n"," add_15_tool (Add)           (None, 16, 16, 256)          0         ['add_9_tool[0][0]',          \n","                                                                     'batch_normalization_tool[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," add_10_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_8_chip[0\n","                                                                    ][0]',                        \n","                                                                     'add_9_chip[0][0]']          \n","                                                                                                  \n"," add_15_chip (Add)           (None, 16, 16, 256)          0         ['add_9_chip[0][0]',          \n","                                                                     'batch_normalization_chip[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," depthwise_conv2d_6_tool (D  (None, 16, 16, 256)          6656      ['add_10_tool[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_8_tool (D  (None, 16, 16, 256)          6656      ['add_15_tool[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_6_chip (D  (None, 16, 16, 256)          6656      ['add_10_chip[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_8_chip (D  (None, 16, 16, 256)          6656      ['add_15_chip[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," activation_13_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_6_tool[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_17_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_8_tool[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_13_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_6_chip[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_17_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_8_chip[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," batch_normalization_13_too  (None, 16, 16, 256)          1024      ['activation_13_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_17_too  (None, 16, 16, 256)          1024      ['activation_17_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_13_chi  (None, 16, 16, 256)          1024      ['activation_13_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_17_chi  (None, 16, 16, 256)          1024      ['activation_17_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_11_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_13_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_10_tool[0][0]']         \n","                                                                                                  \n"," add_16_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_17_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_15_tool[0][0]']         \n","                                                                                                  \n"," add_11_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_13_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_10_chip[0][0]']         \n","                                                                                                  \n"," add_16_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_17_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_15_chip[0][0]']         \n","                                                                                                  \n"," conv2d_7_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_11_tool[0][0]']         \n","                                                                                                  \n"," conv2d_9_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_16_tool[0][0]']         \n","                                                                                                  \n"," conv2d_7_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_11_chip[0][0]']         \n","                                                                                                  \n"," conv2d_9_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_16_chip[0][0]']         \n","                                                                                                  \n"," activation_14_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_7_tool[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_18_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_9_tool[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_14_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_7_chip[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_18_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_9_chip[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_14_too  (None, 16, 16, 256)          1024      ['activation_14_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_18_too  (None, 16, 16, 256)          1024      ['activation_18_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_14_chi  (None, 16, 16, 256)          1024      ['activation_14_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_18_chi  (None, 16, 16, 256)          1024      ['activation_18_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_12_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_6_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_14_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_17_tool (Add)           (None, 16, 16, 256)          0         ['add_6_tool[0][0]',          \n","                                                                     'batch_normalization_18_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_12_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_6_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_14_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_17_chip (Add)           (None, 16, 16, 256)          0         ['add_6_chip[0][0]',          \n","                                                                     'batch_normalization_18_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_18_tool (Add)           (None, 16, 16, 256)          0         ['add_12_tool[0][0]',         \n","                                                                     'add_17_tool[0][0]']         \n","                                                                                                  \n"," add_18_chip (Add)           (None, 16, 16, 256)          0         ['add_12_chip[0][0]',         \n","                                                                     'add_17_chip[0][0]']         \n","                                                                                                  \n"," depthwise_conv2d_7_tool (D  (None, 16, 16, 256)          6656      ['add_12_tool[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_9_tool (D  (None, 16, 16, 256)          6656      ['add_18_tool[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_7_chip (D  (None, 16, 16, 256)          6656      ['add_12_chip[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," depthwise_conv2d_9_chip (D  (None, 16, 16, 256)          6656      ['add_18_chip[0][0]']         \n"," epthwiseConv2D)                                                                                  \n","                                                                                                  \n"," activation_15_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_7_tool[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_19_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_9_tool[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_15_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_7_chip[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," activation_19_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_9_chip[0][0\n"," tion)                                                              ]']                           \n","                                                                                                  \n"," batch_normalization_15_too  (None, 16, 16, 256)          1024      ['activation_15_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_19_too  (None, 16, 16, 256)          1024      ['activation_19_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_15_chi  (None, 16, 16, 256)          1024      ['activation_15_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_19_chi  (None, 16, 16, 256)          1024      ['activation_19_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_13_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_15_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_12_tool[0][0]']         \n","                                                                                                  \n"," add_19_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_19_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_18_tool[0][0]']         \n","                                                                                                  \n"," add_13_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_15_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_12_chip[0][0]']         \n","                                                                                                  \n"," add_19_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_19_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_18_chip[0][0]']         \n","                                                                                                  \n"," conv2d_8_tool (Conv2D)      (None, 16, 16, 256)          65792     ['add_13_tool[0][0]']         \n","                                                                                                  \n"," conv2d_10_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_19_tool[0][0]']         \n","                                                                                                  \n"," conv2d_8_chip (Conv2D)      (None, 16, 16, 256)          65792     ['add_13_chip[0][0]']         \n","                                                                                                  \n"," conv2d_10_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_19_chip[0][0]']         \n","                                                                                                  \n"," activation_16_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_8_tool[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_20_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_10_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_16_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_8_chip[0][0]']       \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_20_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_10_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_16_too  (None, 16, 16, 256)          1024      ['activation_16_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_20_too  (None, 16, 16, 256)          1024      ['activation_20_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_16_chi  (None, 16, 16, 256)          1024      ['activation_16_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_20_chi  (None, 16, 16, 256)          1024      ['activation_20_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_14_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_8_tool[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_16_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_20_tool (Add)           (None, 16, 16, 256)          0         ['add_9_tool[0][0]',          \n","                                                                     'batch_normalization_20_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_14_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_8_chip[0\n","                                                                    ][0]',                        \n","                                                                     'batch_normalization_16_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_20_chip (Add)           (None, 16, 16, 256)          0         ['add_9_chip[0][0]',          \n","                                                                     'batch_normalization_20_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_21_tool (Add)           (None, 16, 16, 256)          0         ['add_14_tool[0][0]',         \n","                                                                     'add_20_tool[0][0]']         \n","                                                                                                  \n"," add_26_tool (Add)           (None, 16, 16, 256)          0         ['add_20_tool[0][0]',         \n","                                                                     'batch_normalization_tool[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," add_21_chip (Add)           (None, 16, 16, 256)          0         ['add_14_chip[0][0]',         \n","                                                                     'add_20_chip[0][0]']         \n","                                                                                                  \n"," add_26_chip (Add)           (None, 16, 16, 256)          0         ['add_20_chip[0][0]',         \n","                                                                     'batch_normalization_chip[0][\n","                                                                    0]']                          \n","                                                                                                  \n"," depthwise_conv2d_10_tool (  (None, 16, 16, 256)          6656      ['add_21_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_12_tool (  (None, 16, 16, 256)          6656      ['add_26_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_10_chip (  (None, 16, 16, 256)          6656      ['add_21_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_12_chip (  (None, 16, 16, 256)          6656      ['add_26_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," activation_21_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_10_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_25_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_12_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_21_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_10_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_25_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_12_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," batch_normalization_21_too  (None, 16, 16, 256)          1024      ['activation_21_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_25_too  (None, 16, 16, 256)          1024      ['activation_25_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_21_chi  (None, 16, 16, 256)          1024      ['activation_21_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_25_chi  (None, 16, 16, 256)          1024      ['activation_25_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_22_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_21_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_21_tool[0][0]']         \n","                                                                                                  \n"," add_27_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_25_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_26_tool[0][0]']         \n","                                                                                                  \n"," add_22_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_21_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_21_chip[0][0]']         \n","                                                                                                  \n"," add_27_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_25_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_26_chip[0][0]']         \n","                                                                                                  \n"," conv2d_11_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_22_tool[0][0]']         \n","                                                                                                  \n"," conv2d_13_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_27_tool[0][0]']         \n","                                                                                                  \n"," conv2d_11_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_22_chip[0][0]']         \n","                                                                                                  \n"," conv2d_13_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_27_chip[0][0]']         \n","                                                                                                  \n"," activation_22_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_11_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_26_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_13_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_22_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_11_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_26_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_13_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_22_too  (None, 16, 16, 256)          1024      ['activation_22_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_26_too  (None, 16, 16, 256)          1024      ['activation_26_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_22_chi  (None, 16, 16, 256)          1024      ['activation_22_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_26_chi  (None, 16, 16, 256)          1024      ['activation_26_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_23_tool (Add)           (None, 16, 16, 256)          0         ['add_12_tool[0][0]',         \n","                                                                     'batch_normalization_22_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_28_tool (Add)           (None, 16, 16, 256)          0         ['add_17_tool[0][0]',         \n","                                                                     'batch_normalization_26_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_23_chip (Add)           (None, 16, 16, 256)          0         ['add_12_chip[0][0]',         \n","                                                                     'batch_normalization_22_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_28_chip (Add)           (None, 16, 16, 256)          0         ['add_17_chip[0][0]',         \n","                                                                     'batch_normalization_26_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_29_tool (Add)           (None, 16, 16, 256)          0         ['add_23_tool[0][0]',         \n","                                                                     'add_28_tool[0][0]']         \n","                                                                                                  \n"," add_29_chip (Add)           (None, 16, 16, 256)          0         ['add_23_chip[0][0]',         \n","                                                                     'add_28_chip[0][0]']         \n","                                                                                                  \n"," depthwise_conv2d_11_tool (  (None, 16, 16, 256)          6656      ['add_23_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_13_tool (  (None, 16, 16, 256)          6656      ['add_29_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_11_chip (  (None, 16, 16, 256)          6656      ['add_23_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_13_chip (  (None, 16, 16, 256)          6656      ['add_29_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," activation_23_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_11_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_27_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_13_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_23_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_11_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_27_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_13_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," batch_normalization_23_too  (None, 16, 16, 256)          1024      ['activation_23_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_27_too  (None, 16, 16, 256)          1024      ['activation_27_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_23_chi  (None, 16, 16, 256)          1024      ['activation_23_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_27_chi  (None, 16, 16, 256)          1024      ['activation_27_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_24_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_23_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_23_tool[0][0]']         \n","                                                                                                  \n"," add_30_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_27_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_29_tool[0][0]']         \n","                                                                                                  \n"," add_24_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_23_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_23_chip[0][0]']         \n","                                                                                                  \n"," add_30_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_27_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_29_chip[0][0]']         \n","                                                                                                  \n"," conv2d_12_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_24_tool[0][0]']         \n","                                                                                                  \n"," conv2d_14_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_30_tool[0][0]']         \n","                                                                                                  \n"," conv2d_12_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_24_chip[0][0]']         \n","                                                                                                  \n"," conv2d_14_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_30_chip[0][0]']         \n","                                                                                                  \n"," activation_24_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_12_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_28_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_14_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_24_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_12_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_28_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_14_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_24_too  (None, 16, 16, 256)          1024      ['activation_24_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_28_too  (None, 16, 16, 256)          1024      ['activation_28_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_24_chi  (None, 16, 16, 256)          1024      ['activation_24_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_28_chi  (None, 16, 16, 256)          1024      ['activation_28_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_25_tool (Add)           (None, 16, 16, 256)          0         ['add_14_tool[0][0]',         \n","                                                                     'batch_normalization_24_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_31_tool (Add)           (None, 16, 16, 256)          0         ['add_20_tool[0][0]',         \n","                                                                     'batch_normalization_28_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_25_chip (Add)           (None, 16, 16, 256)          0         ['add_14_chip[0][0]',         \n","                                                                     'batch_normalization_24_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_31_chip (Add)           (None, 16, 16, 256)          0         ['add_20_chip[0][0]',         \n","                                                                     'batch_normalization_28_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_32_tool (Add)           (None, 16, 16, 256)          0         ['add_25_tool[0][0]',         \n","                                                                     'add_31_tool[0][0]']         \n","                                                                                                  \n"," add_32_chip (Add)           (None, 16, 16, 256)          0         ['add_25_chip[0][0]',         \n","                                                                     'add_31_chip[0][0]']         \n","                                                                                                  \n"," depthwise_conv2d_14_tool (  (None, 16, 16, 256)          6656      ['add_32_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_14_chip (  (None, 16, 16, 256)          6656      ['add_32_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," activation_29_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_14_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_29_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_14_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," batch_normalization_29_too  (None, 16, 16, 256)          1024      ['activation_29_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_29_chi  (None, 16, 16, 256)          1024      ['activation_29_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_33_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_29_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_32_tool[0][0]']         \n","                                                                                                  \n"," add_33_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_29_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_32_chip[0][0]']         \n","                                                                                                  \n"," conv2d_15_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_33_tool[0][0]']         \n","                                                                                                  \n"," conv2d_15_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_33_chip[0][0]']         \n","                                                                                                  \n"," activation_30_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_15_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_30_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_15_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_30_too  (None, 16, 16, 256)          1024      ['activation_30_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_30_chi  (None, 16, 16, 256)          1024      ['activation_30_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_34_tool (Add)           (None, 16, 16, 256)          0         ['add_23_tool[0][0]',         \n","                                                                     'batch_normalization_30_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_34_chip (Add)           (None, 16, 16, 256)          0         ['add_23_chip[0][0]',         \n","                                                                     'batch_normalization_30_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," depthwise_conv2d_15_tool (  (None, 16, 16, 256)          6656      ['add_34_tool[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," depthwise_conv2d_15_chip (  (None, 16, 16, 256)          6656      ['add_34_chip[0][0]']         \n"," DepthwiseConv2D)                                                                                 \n","                                                                                                  \n"," activation_31_tool (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_15_tool[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," activation_31_chip (Activa  (None, 16, 16, 256)          0         ['depthwise_conv2d_15_chip[0][\n"," tion)                                                              0]']                          \n","                                                                                                  \n"," batch_normalization_31_too  (None, 16, 16, 256)          1024      ['activation_31_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_31_chi  (None, 16, 16, 256)          1024      ['activation_31_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_35_tool (Add)           (None, 16, 16, 256)          0         ['batch_normalization_31_tool[\n","                                                                    0][0]',                       \n","                                                                     'add_34_tool[0][0]']         \n","                                                                                                  \n"," add_35_chip (Add)           (None, 16, 16, 256)          0         ['batch_normalization_31_chip[\n","                                                                    0][0]',                       \n","                                                                     'add_34_chip[0][0]']         \n","                                                                                                  \n"," conv2d_16_tool (Conv2D)     (None, 16, 16, 256)          65792     ['add_35_tool[0][0]']         \n","                                                                                                  \n"," conv2d_16_chip (Conv2D)     (None, 16, 16, 256)          65792     ['add_35_chip[0][0]']         \n","                                                                                                  \n"," activation_32_tool (Activa  (None, 16, 16, 256)          0         ['conv2d_16_tool[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," activation_32_chip (Activa  (None, 16, 16, 256)          0         ['conv2d_16_chip[0][0]']      \n"," tion)                                                                                            \n","                                                                                                  \n"," batch_normalization_32_too  (None, 16, 16, 256)          1024      ['activation_32_tool[0][0]']  \n"," l (BatchNormalization)                                                                           \n","                                                                                                  \n"," batch_normalization_32_chi  (None, 16, 16, 256)          1024      ['activation_32_chip[0][0]']  \n"," p (BatchNormalization)                                                                           \n","                                                                                                  \n"," add_36_tool (Add)           (None, 16, 16, 256)          0         ['add_25_tool[0][0]',         \n","                                                                     'batch_normalization_32_tool[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," add_36_chip (Add)           (None, 16, 16, 256)          0         ['add_25_chip[0][0]',         \n","                                                                     'batch_normalization_32_chip[\n","                                                                    0][0]']                       \n","                                                                                                  \n"," global_average_pooling2d_t  (None, 256)                  0         ['add_36_tool[0][0]']         \n"," ool (GlobalAveragePooling2                                                                       \n"," D)                                                                                               \n","                                                                                                  \n"," global_average_pooling2d_c  (None, 256)                  0         ['add_36_chip[0][0]']         \n"," hip (GlobalAveragePooling2                                                                       \n"," D)                                                                                               \n","                                                                                                  \n"," concatenate (Concatenate)   (None, 512)                  0         ['global_average_pooling2d_too\n","                                                                    l[0][0]',                     \n","                                                                     'global_average_pooling2d_chi\n","                                                                    p[0][0]']                     \n","                                                                                                  \n"," output_mult (Dense)         (None, 3)                    1539      ['concatenate[0][0]']         \n","                                                                                                  \n","==================================================================================================\n","Total params: 2781187 (10.61 MB)\n","Trainable params: 1539 (6.01 KB)\n","Non-trainable params: 2779648 (10.60 MB)\n","__________________________________________________________________________________________________\n"]}],"source":["model = load_siren(model_tool, model_chip)\n","model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fp8yKU6VqRzA","outputId":"402ee1a5-0e56-46c2-9d9d-5d8d913b083a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/100\n","2/2 [==============================] - 13s 2s/step - loss: 3876.9194 - mean_squared_error: 3876.9194 - val_loss: 4775.3521 - val_mean_squared_error: 4775.3521\n","Epoch 2/100\n","2/2 [==============================] - 1s 405ms/step - loss: 3759.3601 - mean_squared_error: 3759.3601 - val_loss: 4643.8394 - val_mean_squared_error: 4643.8394\n","Epoch 3/100\n","2/2 [==============================] - 1s 404ms/step - loss: 3659.9431 - mean_squared_error: 3659.9431 - val_loss: 4515.0073 - val_mean_squared_error: 4515.0073\n","Epoch 4/100\n","2/2 [==============================] - 1s 388ms/step - loss: 3553.2283 - mean_squared_error: 3553.2283 - val_loss: 4390.3530 - val_mean_squared_error: 4390.3530\n","Epoch 5/100\n","2/2 [==============================] - 1s 764ms/step - loss: 3456.6367 - mean_squared_error: 3456.6367 - val_loss: 4269.4946 - val_mean_squared_error: 4269.4946\n","Epoch 6/100\n","2/2 [==============================] - 1s 442ms/step - loss: 3355.6719 - mean_squared_error: 3355.6719 - val_loss: 4153.2524 - val_mean_squared_error: 4153.2524\n","Epoch 7/100\n","2/2 [==============================] - 1s 405ms/step - loss: 3270.6213 - mean_squared_error: 3270.6213 - val_loss: 4040.2500 - val_mean_squared_error: 4040.2500\n","Epoch 8/100\n","2/2 [==============================] - 1s 388ms/step - loss: 3180.4409 - mean_squared_error: 3180.4409 - val_loss: 3931.5754 - val_mean_squared_error: 3931.5754\n","Epoch 9/100\n","2/2 [==============================] - 1s 398ms/step - loss: 3087.6484 - mean_squared_error: 3087.6484 - val_loss: 3827.5110 - val_mean_squared_error: 3827.5110\n","Epoch 10/100\n","2/2 [==============================] - 1s 405ms/step - loss: 3012.4722 - mean_squared_error: 3012.4722 - val_loss: 3725.9119 - val_mean_squared_error: 3725.9119\n","Epoch 11/100\n","2/2 [==============================] - 1s 382ms/step - loss: 2935.0669 - mean_squared_error: 2935.0669 - val_loss: 3627.9651 - val_mean_squared_error: 3627.9651\n","Epoch 12/100\n","2/2 [==============================] - 1s 391ms/step - loss: 2863.9011 - mean_squared_error: 2863.9011 - val_loss: 3532.5813 - val_mean_squared_error: 3532.5813\n","Epoch 13/100\n","2/2 [==============================] - 1s 400ms/step - loss: 2792.1663 - mean_squared_error: 2792.1663 - val_loss: 3440.6045 - val_mean_squared_error: 3440.6045\n","Epoch 14/100\n","2/2 [==============================] - 1s 531ms/step - loss: 2717.5496 - mean_squared_error: 2717.5496 - val_loss: 3352.0393 - val_mean_squared_error: 3352.0393\n","Epoch 15/100\n","2/2 [==============================] - 1s 582ms/step - loss: 2647.9536 - mean_squared_error: 2647.9536 - val_loss: 3266.6375 - val_mean_squared_error: 3266.6375\n","Epoch 16/100\n","2/2 [==============================] - 1s 391ms/step - loss: 2593.1121 - mean_squared_error: 2593.1121 - val_loss: 3182.8059 - val_mean_squared_error: 3182.8059\n","Epoch 17/100\n","2/2 [==============================] - 1s 393ms/step - loss: 2528.3259 - mean_squared_error: 2528.3259 - val_loss: 3101.5801 - val_mean_squared_error: 3101.5801\n","Epoch 18/100\n","2/2 [==============================] - 1s 397ms/step - loss: 2472.4363 - mean_squared_error: 2472.4363 - val_loss: 3023.2043 - val_mean_squared_error: 3023.2043\n","Epoch 19/100\n","2/2 [==============================] - 1s 400ms/step - loss: 2409.7444 - mean_squared_error: 2409.7444 - val_loss: 2947.9641 - val_mean_squared_error: 2947.9641\n","Epoch 20/100\n","2/2 [==============================] - 1s 422ms/step - loss: 2356.7385 - mean_squared_error: 2356.7385 - val_loss: 2873.8645 - val_mean_squared_error: 2873.8645\n","Epoch 21/100\n","2/2 [==============================] - 1s 395ms/step - loss: 2301.3118 - mean_squared_error: 2301.3118 - val_loss: 2801.1768 - val_mean_squared_error: 2801.1768\n","Epoch 22/100\n","2/2 [==============================] - 1s 405ms/step - loss: 2247.6392 - mean_squared_error: 2247.6392 - val_loss: 2730.4338 - val_mean_squared_error: 2730.4338\n","Epoch 23/100\n","2/2 [==============================] - 1s 395ms/step - loss: 2198.7795 - mean_squared_error: 2198.7795 - val_loss: 2661.5642 - val_mean_squared_error: 2661.5642\n","Epoch 24/100\n","2/2 [==============================] - 1s 398ms/step - loss: 2147.9167 - mean_squared_error: 2147.9167 - val_loss: 2593.8713 - val_mean_squared_error: 2593.8713\n","Epoch 25/100\n","2/2 [==============================] - 1s 391ms/step - loss: 2104.9604 - mean_squared_error: 2104.9604 - val_loss: 2527.6692 - val_mean_squared_error: 2527.6692\n","Epoch 26/100\n","2/2 [==============================] - 1s 402ms/step - loss: 2051.6128 - mean_squared_error: 2051.6128 - val_loss: 2463.6355 - val_mean_squared_error: 2463.6355\n","Epoch 27/100\n","2/2 [==============================] - 1s 515ms/step - loss: 2011.3048 - mean_squared_error: 2011.3048 - val_loss: 2400.2463 - val_mean_squared_error: 2400.2463\n","Epoch 28/100\n","2/2 [==============================] - 1s 729ms/step - loss: 1970.4158 - mean_squared_error: 1970.4158 - val_loss: 2338.8533 - val_mean_squared_error: 2338.8533\n","Epoch 29/100\n","2/2 [==============================] - 1s 431ms/step - loss: 1925.5724 - mean_squared_error: 1925.5724 - val_loss: 2279.6575 - val_mean_squared_error: 2279.6575\n","Epoch 30/100\n","2/2 [==============================] - 1s 395ms/step - loss: 1883.2245 - mean_squared_error: 1883.2245 - val_loss: 2221.9529 - val_mean_squared_error: 2221.9529\n","Epoch 31/100\n","2/2 [==============================] - 1s 419ms/step - loss: 1848.1018 - mean_squared_error: 1848.1018 - val_loss: 2164.7781 - val_mean_squared_error: 2164.7781\n","Epoch 32/100\n","2/2 [==============================] - 1s 400ms/step - loss: 1807.9602 - mean_squared_error: 1807.9602 - val_loss: 2109.5989 - val_mean_squared_error: 2109.5989\n","Epoch 33/100\n","2/2 [==============================] - 1s 414ms/step - loss: 1771.3196 - mean_squared_error: 1771.3196 - val_loss: 2056.0750 - val_mean_squared_error: 2056.0750\n","Epoch 34/100\n","2/2 [==============================] - 1s 417ms/step - loss: 1736.9314 - mean_squared_error: 1736.9314 - val_loss: 2003.9677 - val_mean_squared_error: 2003.9677\n","Epoch 35/100\n","2/2 [==============================] - 1s 400ms/step - loss: 1702.3047 - mean_squared_error: 1702.3047 - val_loss: 1953.6107 - val_mean_squared_error: 1953.6107\n","Epoch 36/100\n","2/2 [==============================] - 1s 410ms/step - loss: 1667.9275 - mean_squared_error: 1667.9275 - val_loss: 1905.1207 - val_mean_squared_error: 1905.1207\n","Epoch 37/100\n","2/2 [==============================] - 1s 402ms/step - loss: 1635.9023 - mean_squared_error: 1635.9023 - val_loss: 1857.6215 - val_mean_squared_error: 1857.6215\n","Epoch 38/100\n","2/2 [==============================] - 1s 410ms/step - loss: 1608.2521 - mean_squared_error: 1608.2521 - val_loss: 1811.7723 - val_mean_squared_error: 1811.7723\n","Epoch 39/100\n","2/2 [==============================] - 1s 405ms/step - loss: 1579.7963 - mean_squared_error: 1579.7963 - val_loss: 1767.0033 - val_mean_squared_error: 1767.0033\n","Epoch 40/100\n","2/2 [==============================] - 1s 611ms/step - loss: 1548.4049 - mean_squared_error: 1548.4049 - val_loss: 1724.1866 - val_mean_squared_error: 1724.1866\n","Epoch 41/100\n","2/2 [==============================] - 1s 403ms/step - loss: 1520.3558 - mean_squared_error: 1520.3558 - val_loss: 1682.4364 - val_mean_squared_error: 1682.4364\n","Epoch 42/100\n","2/2 [==============================] - 1s 401ms/step - loss: 1496.1921 - mean_squared_error: 1496.1921 - val_loss: 1641.4740 - val_mean_squared_error: 1641.4740\n","Epoch 43/100\n","2/2 [==============================] - 1s 385ms/step - loss: 1468.8730 - mean_squared_error: 1468.8730 - val_loss: 1601.9985 - val_mean_squared_error: 1601.9985\n","Epoch 44/100\n","2/2 [==============================] - 1s 395ms/step - loss: 1447.7697 - mean_squared_error: 1447.7697 - val_loss: 1563.0297 - val_mean_squared_error: 1563.0297\n","Epoch 45/100\n","2/2 [==============================] - 1s 422ms/step - loss: 1420.0808 - mean_squared_error: 1420.0808 - val_loss: 1526.0684 - val_mean_squared_error: 1526.0684\n","Epoch 46/100\n","2/2 [==============================] - 1s 398ms/step - loss: 1398.1010 - mean_squared_error: 1398.1010 - val_loss: 1490.5677 - val_mean_squared_error: 1490.5677\n","Epoch 47/100\n","2/2 [==============================] - 1s 415ms/step - loss: 1377.1125 - mean_squared_error: 1377.1125 - val_loss: 1455.4325 - val_mean_squared_error: 1455.4325\n","Epoch 48/100\n","2/2 [==============================] - 1s 395ms/step - loss: 1352.4366 - mean_squared_error: 1352.4366 - val_loss: 1421.2598 - val_mean_squared_error: 1421.2598\n","Epoch 49/100\n","2/2 [==============================] - 1s 392ms/step - loss: 1333.6566 - mean_squared_error: 1333.6566 - val_loss: 1388.0576 - val_mean_squared_error: 1388.0576\n","Epoch 50/100\n","2/2 [==============================] - 1s 405ms/step - loss: 1316.4835 - mean_squared_error: 1316.4835 - val_loss: 1355.1874 - val_mean_squared_error: 1355.1874\n","Epoch 51/100\n","2/2 [==============================] - 1s 609ms/step - loss: 1294.0245 - mean_squared_error: 1294.0245 - val_loss: 1323.9388 - val_mean_squared_error: 1323.9388\n","Epoch 52/100\n","2/2 [==============================] - 1s 834ms/step - loss: 1275.1409 - mean_squared_error: 1275.1409 - val_loss: 1293.7743 - val_mean_squared_error: 1293.7743\n","Epoch 53/100\n","2/2 [==============================] - 1s 390ms/step - loss: 1259.6600 - mean_squared_error: 1259.6600 - val_loss: 1264.7725 - val_mean_squared_error: 1264.7725\n","Epoch 54/100\n","2/2 [==============================] - 1s 382ms/step - loss: 1238.4427 - mean_squared_error: 1238.4427 - val_loss: 1236.7733 - val_mean_squared_error: 1236.7733\n","Epoch 55/100\n","2/2 [==============================] - 1s 403ms/step - loss: 1222.3021 - mean_squared_error: 1222.3024 - val_loss: 1209.6188 - val_mean_squared_error: 1209.6188\n","Epoch 56/100\n","2/2 [==============================] - 1s 416ms/step - loss: 1208.2260 - mean_squared_error: 1208.2260 - val_loss: 1183.2069 - val_mean_squared_error: 1183.2069\n","Epoch 57/100\n","2/2 [==============================] - 1s 401ms/step - loss: 1191.2668 - mean_squared_error: 1191.2668 - val_loss: 1157.1088 - val_mean_squared_error: 1157.1088\n","Epoch 58/100\n","2/2 [==============================] - 1s 650ms/step - loss: 1175.9297 - mean_squared_error: 1175.9297 - val_loss: 1132.2362 - val_mean_squared_error: 1132.2362\n","Epoch 59/100\n","2/2 [==============================] - 1s 728ms/step - loss: 1163.0885 - mean_squared_error: 1163.0885 - val_loss: 1108.3154 - val_mean_squared_error: 1108.3154\n","Epoch 60/100\n","2/2 [==============================] - 1s 395ms/step - loss: 1148.9923 - mean_squared_error: 1148.9924 - val_loss: 1084.9581 - val_mean_squared_error: 1084.9581\n","Epoch 61/100\n","2/2 [==============================] - 1s 384ms/step - loss: 1136.4648 - mean_squared_error: 1136.4648 - val_loss: 1061.7523 - val_mean_squared_error: 1061.7523\n","Epoch 62/100\n","2/2 [==============================] - 1s 647ms/step - loss: 1121.6411 - mean_squared_error: 1121.6411 - val_loss: 1039.4952 - val_mean_squared_error: 1039.4952\n","Epoch 63/100\n","2/2 [==============================] - 1s 776ms/step - loss: 1109.1174 - mean_squared_error: 1109.1174 - val_loss: 1018.1611 - val_mean_squared_error: 1018.1611\n","Epoch 64/100\n","2/2 [==============================] - 1s 418ms/step - loss: 1097.5764 - mean_squared_error: 1097.5764 - val_loss: 997.5498 - val_mean_squared_error: 997.5498\n","Epoch 65/100\n","2/2 [==============================] - 1s 383ms/step - loss: 1088.1887 - mean_squared_error: 1088.1887 - val_loss: 977.9084 - val_mean_squared_error: 977.9084\n","Epoch 66/100\n","2/2 [==============================] - 1s 415ms/step - loss: 1075.2203 - mean_squared_error: 1075.2203 - val_loss: 958.6751 - val_mean_squared_error: 958.6751\n","Epoch 67/100\n","2/2 [==============================] - 1s 386ms/step - loss: 1064.8533 - mean_squared_error: 1064.8533 - val_loss: 940.0720 - val_mean_squared_error: 940.0720\n","Epoch 68/100\n","2/2 [==============================] - 1s 394ms/step - loss: 1055.3234 - mean_squared_error: 1055.3234 - val_loss: 922.0583 - val_mean_squared_error: 922.0583\n","Epoch 69/100\n","2/2 [==============================] - 1s 381ms/step - loss: 1046.2639 - mean_squared_error: 1046.2639 - val_loss: 905.2640 - val_mean_squared_error: 905.2640\n","Epoch 70/100\n","2/2 [==============================] - 1s 407ms/step - loss: 1035.5326 - mean_squared_error: 1035.5326 - val_loss: 888.2441 - val_mean_squared_error: 888.2441\n","Epoch 71/100\n","2/2 [==============================] - 1s 390ms/step - loss: 1030.2938 - mean_squared_error: 1030.2938 - val_loss: 871.7917 - val_mean_squared_error: 871.7917\n","Epoch 72/100\n","2/2 [==============================] - 1s 407ms/step - loss: 1020.3300 - mean_squared_error: 1020.3300 - val_loss: 856.1935 - val_mean_squared_error: 856.1935\n","Epoch 73/100\n","2/2 [==============================] - 1s 387ms/step - loss: 1009.2417 - mean_squared_error: 1009.2417 - val_loss: 841.3454 - val_mean_squared_error: 841.3454\n","Epoch 74/100\n","2/2 [==============================] - 1s 765ms/step - loss: 1004.2427 - mean_squared_error: 1004.2427 - val_loss: 826.4108 - val_mean_squared_error: 826.4108\n","Epoch 75/100\n","2/2 [==============================] - 1s 513ms/step - loss: 995.8069 - mean_squared_error: 995.8069 - val_loss: 811.6464 - val_mean_squared_error: 811.6464\n","Epoch 76/100\n","2/2 [==============================] - 1s 405ms/step - loss: 988.1187 - mean_squared_error: 988.1187 - val_loss: 797.6833 - val_mean_squared_error: 797.6833\n","Epoch 77/100\n","2/2 [==============================] - 1s 404ms/step - loss: 981.7147 - mean_squared_error: 981.7147 - val_loss: 784.2955 - val_mean_squared_error: 784.2955\n","Epoch 78/100\n","2/2 [==============================] - 1s 404ms/step - loss: 976.8073 - mean_squared_error: 976.8073 - val_loss: 772.0049 - val_mean_squared_error: 772.0049\n","Epoch 79/100\n","2/2 [==============================] - 1s 391ms/step - loss: 969.3925 - mean_squared_error: 969.3925 - val_loss: 760.0928 - val_mean_squared_error: 760.0928\n","Epoch 80/100\n","2/2 [==============================] - 1s 867ms/step - loss: 963.6844 - mean_squared_error: 963.6844 - val_loss: 747.7418 - val_mean_squared_error: 747.7418\n","Epoch 81/100\n","2/2 [==============================] - 1s 394ms/step - loss: 956.9199 - mean_squared_error: 956.9199 - val_loss: 735.6999 - val_mean_squared_error: 735.6999\n","Epoch 82/100\n","2/2 [==============================] - 1s 387ms/step - loss: 950.9134 - mean_squared_error: 950.9134 - val_loss: 724.5380 - val_mean_squared_error: 724.5380\n","Epoch 83/100\n","2/2 [==============================] - 1s 416ms/step - loss: 947.2040 - mean_squared_error: 947.2040 - val_loss: 713.4021 - val_mean_squared_error: 713.4021\n","Epoch 84/100\n","2/2 [==============================] - 1s 413ms/step - loss: 940.8250 - mean_squared_error: 940.8250 - val_loss: 703.5672 - val_mean_squared_error: 703.5672\n","Epoch 85/100\n","2/2 [==============================] - 1s 748ms/step - loss: 935.7889 - mean_squared_error: 935.7889 - val_loss: 694.4514 - val_mean_squared_error: 694.4514\n","Epoch 86/100\n","2/2 [==============================] - 1s 561ms/step - loss: 930.7137 - mean_squared_error: 930.7137 - val_loss: 685.5537 - val_mean_squared_error: 685.5537\n","Epoch 87/100\n","2/2 [==============================] - 1s 392ms/step - loss: 928.1353 - mean_squared_error: 928.1353 - val_loss: 677.0076 - val_mean_squared_error: 677.0076\n","Epoch 88/100\n","2/2 [==============================] - 1s 405ms/step - loss: 922.1590 - mean_squared_error: 922.1590 - val_loss: 668.1911 - val_mean_squared_error: 668.1911\n","Epoch 89/100\n","2/2 [==============================] - 1s 407ms/step - loss: 918.1265 - mean_squared_error: 918.1265 - val_loss: 659.7023 - val_mean_squared_error: 659.7023\n","Epoch 90/100\n","2/2 [==============================] - 1s 417ms/step - loss: 915.1844 - mean_squared_error: 915.1844 - val_loss: 652.1702 - val_mean_squared_error: 652.1702\n","Epoch 91/100\n","2/2 [==============================] - 1s 409ms/step - loss: 909.9463 - mean_squared_error: 909.9463 - val_loss: 644.1989 - val_mean_squared_error: 644.1989\n","Epoch 92/100\n","2/2 [==============================] - 1s 409ms/step - loss: 907.8376 - mean_squared_error: 907.8376 - val_loss: 636.7742 - val_mean_squared_error: 636.7742\n","Epoch 93/100\n","2/2 [==============================] - 1s 400ms/step - loss: 902.3389 - mean_squared_error: 902.3389 - val_loss: 629.6817 - val_mean_squared_error: 629.6817\n","Epoch 94/100\n","2/2 [==============================] - 1s 399ms/step - loss: 898.9902 - mean_squared_error: 898.9902 - val_loss: 622.8334 - val_mean_squared_error: 622.8334\n","Epoch 95/100\n","2/2 [==============================] - 1s 564ms/step - loss: 897.0092 - mean_squared_error: 897.0092 - val_loss: 616.3785 - val_mean_squared_error: 616.3785\n","Epoch 96/100\n","2/2 [==============================] - 1s 715ms/step - loss: 893.9476 - mean_squared_error: 893.9476 - val_loss: 609.7012 - val_mean_squared_error: 609.7012\n","Epoch 97/100\n","2/2 [==============================] - 1s 404ms/step - loss: 889.9082 - mean_squared_error: 889.9082 - val_loss: 602.9794 - val_mean_squared_error: 602.9794\n","Epoch 98/100\n","2/2 [==============================] - 1s 411ms/step - loss: 887.0675 - mean_squared_error: 887.0675 - val_loss: 596.6309 - val_mean_squared_error: 596.6309\n","Epoch 99/100\n","2/2 [==============================] - 1s 405ms/step - loss: 883.5991 - mean_squared_error: 883.5991 - val_loss: 591.0876 - val_mean_squared_error: 591.0876\n","Epoch 100/100\n","2/2 [==============================] - 1s 406ms/step - loss: 880.9360 - mean_squared_error: 880.9360 - val_loss: 585.6343 - val_mean_squared_error: 585.6343\n","1/1 [==============================] - 0s 136ms/step - loss: 6902.0151 - mean_squared_error: 6902.0151\n","Test error: 6902.02\n"]}],"source":["history, model = launch_experiment(model)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"okTJYdA9qRzA","outputId":"88041285-05f3-4056-84e3-3a3a1ba9454b"},"outputs":[{"name":"stderr","output_type":"stream","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":"qMqSnaSkqRzA","outputId":"02504574-966c-4dff-d8dc-ea210a31c639"},"outputs":[{"name":"stdout","output_type":"stream","text":["1/1 [==============================] - 2s 2s/step\n"]}],"source":["pred = model.predict(test_ds)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1C044ioRqRzA"},"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":"65uKXjECqRzB"},"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":"KQ2MPJJaqRzB"},"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":"7_HlEguTqRzB","outputId":"d68f175b-d5bb-4177-a66f-44c0fc737a92"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":"ykuuU8y5qRzC","outputId":"7c5d5369-ad10-47b5-a737-305c128cbf2c"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 800x400 with 2 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","text/plain":["<Figure size 800x400 with 2 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","text/plain":["<Figure size 800x400 with 2 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","text/plain":["<Figure size 800x400 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":"gtOHNt8pqRzC"},"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":"LUtd08TUqRzC","outputId":"b679578d-a8fb-452e-96a9-ea7447e125ce"},"outputs":[{"name":"stdout","output_type":"stream","text":["===== gaps results =====\n","MAE:  12.882802022298177\n","MSE:  201.83780731523396\n","RMSE:  14.206963338983948\n","r2_score:  -0.912936583568998\n","MSLE:  5.555733217519911\n","MAPE:  5.0841131337209176e+16\n","===== flank_wear results =====\n","MAE:  100.42209370930989\n","MSE:  20454.260762126774\n","RMSE:  143.01839309028324\n","r2_score:  -0.2766075984465146\n","MSLE:  0.7180707705854537\n","MAPE:  0.6205054000585758\n","===== overhang results =====\n","MAE:  5.49625213623047\n","MSE:  49.94443870215418\n","RMSE:  7.067137942771047\n","r2_score:  -1.4758716684203637\n","MSLE:  0.2703574453329243\n","MAPE:  0.6286610587609353\n","===== multi results =====\n","MAE:  39.60038262261285\n","MSE:  6902.014336048054\n","RMSE:  54.76416479067941\n","r2_score:  -0.8884719501452921\n","MSLE:  2.1813871444794293\n","MAPE:  1.6947043779069726e+16\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":{"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}