106 lines
25 KiB
Plaintext
106 lines
25 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "99d6b339",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-08-01 21:12:17.069258: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import fetch_openml\n",
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"import matplotlib as mpl\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.linear_model import SGDClassifier\n",
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"from sklearn.model_selection import StratifiedKFold, cross_val_predict, train_test_split, StratifiedShuffleSplit,cross_val_score\n",
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"from sklearn.base import clone, BaseEstimator\n",
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"from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score, precision_recall_curve, roc_curve, roc_auc_score\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.svm import SVC\n",
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"from sklearn.multiclass import OneVsRestClassifier\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import tensorflow as tf\n",
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"\n",
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"import joblib"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "20c2c97e",
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"metadata": {},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "('Keyword argument not understood:', 'keepdims')",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"Input \u001b[0;32mIn [7]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m new_model \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeras\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mModel_1.h5\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/saving/save.py:206\u001b[0m, in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m load_context\u001b[38;5;241m.\u001b[39mload_context(options):\n\u001b[1;32m 204\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (h5py \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m 205\u001b[0m (\u001b[38;5;28misinstance\u001b[39m(filepath, h5py\u001b[38;5;241m.\u001b[39mFile) \u001b[38;5;129;01mor\u001b[39;00m h5py\u001b[38;5;241m.\u001b[39mis_hdf5(filepath))):\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhdf5_format\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model_from_hdf5\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 207\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 209\u001b[0m filepath \u001b[38;5;241m=\u001b[39m path_to_string(filepath)\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(filepath, six\u001b[38;5;241m.\u001b[39mstring_types):\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/saving/hdf5_format.py:183\u001b[0m, in \u001b[0;36mload_model_from_hdf5\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNo model found in config file.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 182\u001b[0m model_config \u001b[38;5;241m=\u001b[39m json_utils\u001b[38;5;241m.\u001b[39mdecode(model_config\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m--> 183\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_config_lib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_from_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[38;5;66;03m# set weights\u001b[39;00m\n\u001b[1;32m 187\u001b[0m load_weights_from_hdf5_group(f[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_weights\u001b[39m\u001b[38;5;124m'\u001b[39m], model\u001b[38;5;241m.\u001b[39mlayers)\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/saving/model_config.py:64\u001b[0m, in \u001b[0;36mmodel_from_config\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`model_from_config` expects a dictionary, not a list. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 61\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMaybe you meant to use \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`Sequential.from_config(config)`?\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdeserialize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/layers/serialization.py:173\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;124;03m\"\"\"Instantiates a layer from a config dictionary.\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \n\u001b[1;32m 164\u001b[0m \u001b[38;5;124;03mArguments:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;124;03m Layer instance (may be Model, Sequential, Network, Layer...)\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 172\u001b[0m populate_deserializable_objects()\n\u001b[0;32m--> 173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgeneric_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mLOCAL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_OBJECTS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43mprintable_module_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlayer\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/utils/generic_utils.py:354\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 351\u001b[0m custom_objects \u001b[38;5;241m=\u001b[39m custom_objects \u001b[38;5;129;01mor\u001b[39;00m {}\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcustom_objects\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m arg_spec\u001b[38;5;241m.\u001b[39margs:\n\u001b[0;32m--> 354\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 355\u001b[0m \u001b[43m \u001b[49m\u001b[43mcls_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 356\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 357\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m_GLOBAL_CUSTOM_OBJECTS\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\n\u001b[1;32m 358\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 359\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m CustomObjectScope(custom_objects):\n\u001b[1;32m 360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_config(cls_config)\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/engine/functional.py:668\u001b[0m, in \u001b[0;36mFunctional.from_config\u001b[0;34m(cls, config, custom_objects)\u001b[0m\n\u001b[1;32m 652\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 653\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_config\u001b[39m(\u001b[38;5;28mcls\u001b[39m, config, custom_objects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 654\u001b[0m \u001b[38;5;124;03m\"\"\"Instantiates a Model from its config (output of `get_config()`).\u001b[39;00m\n\u001b[1;32m 655\u001b[0m \n\u001b[1;32m 656\u001b[0m \u001b[38;5;124;03m Arguments:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[38;5;124;03m ValueError: In case of improperly formatted config dict.\u001b[39;00m\n\u001b[1;32m 667\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 668\u001b[0m input_tensors, output_tensors, created_layers \u001b[38;5;241m=\u001b[39m \u001b[43mreconstruct_from_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 669\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 670\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(inputs\u001b[38;5;241m=\u001b[39minput_tensors, outputs\u001b[38;5;241m=\u001b[39moutput_tensors,\n\u001b[1;32m 671\u001b[0m name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 672\u001b[0m connect_ancillary_layers(model, created_layers)\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/engine/functional.py:1275\u001b[0m, in \u001b[0;36mreconstruct_from_config\u001b[0;34m(config, custom_objects, created_layers)\u001b[0m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# First, we create all layers and enqueue nodes to be processed\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer_data \u001b[38;5;129;01min\u001b[39;00m config[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlayers\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n\u001b[0;32m-> 1275\u001b[0m \u001b[43mprocess_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlayer_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1276\u001b[0m \u001b[38;5;66;03m# Then we process nodes in order of layer depth.\u001b[39;00m\n\u001b[1;32m 1277\u001b[0m \u001b[38;5;66;03m# Nodes that cannot yet be processed (if the inbound node\u001b[39;00m\n\u001b[1;32m 1278\u001b[0m \u001b[38;5;66;03m# does not yet exist) are re-enqueued, and the process\u001b[39;00m\n\u001b[1;32m 1279\u001b[0m \u001b[38;5;66;03m# is repeated until all nodes are processed.\u001b[39;00m\n\u001b[1;32m 1280\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m unprocessed_nodes:\n",
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/engine/functional.py:1257\u001b[0m, in \u001b[0;36mreconstruct_from_config.<locals>.process_layer\u001b[0;34m(layer_data)\u001b[0m\n\u001b[1;32m 1253\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1254\u001b[0m \u001b[38;5;66;03m# Instantiate layer.\u001b[39;00m\n\u001b[1;32m 1255\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlayers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deserialize \u001b[38;5;28;01mas\u001b[39;00m deserialize_layer \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top\u001b[39;00m\n\u001b[0;32m-> 1257\u001b[0m layer \u001b[38;5;241m=\u001b[39m \u001b[43mdeserialize_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlayer_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1258\u001b[0m created_layers[layer_name] \u001b[38;5;241m=\u001b[39m layer\n\u001b[1;32m 1260\u001b[0m node_count_by_layer[layer] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(_should_skip_first_node(layer))\n",
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||
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"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/layers/serialization.py:173\u001b[0m, in \u001b[0;36mdeserialize\u001b[0;34m(config, custom_objects)\u001b[0m\n\u001b[1;32m 162\u001b[0m \u001b[38;5;124;03m\"\"\"Instantiates a layer from a config dictionary.\u001b[39;00m\n\u001b[1;32m 163\u001b[0m \n\u001b[1;32m 164\u001b[0m \u001b[38;5;124;03mArguments:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;124;03m Layer instance (may be Model, Sequential, Network, Layer...)\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 172\u001b[0m populate_deserializable_objects()\n\u001b[0;32m--> 173\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgeneric_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeserialize_keras_object\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mLOCAL\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_OBJECTS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 176\u001b[0m \u001b[43m \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 177\u001b[0m \u001b[43m \u001b[49m\u001b[43mprintable_module_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlayer\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
|
||
|
|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/utils/generic_utils.py:360\u001b[0m, in \u001b[0;36mdeserialize_keras_object\u001b[0;34m(identifier, module_objects, custom_objects, printable_module_name)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mfrom_config(\n\u001b[1;32m 355\u001b[0m cls_config,\n\u001b[1;32m 356\u001b[0m custom_objects\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 357\u001b[0m \u001b[38;5;28mlist\u001b[39m(_GLOBAL_CUSTOM_OBJECTS\u001b[38;5;241m.\u001b[39mitems()) \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m 358\u001b[0m \u001b[38;5;28mlist\u001b[39m(custom_objects\u001b[38;5;241m.\u001b[39mitems())))\n\u001b[1;32m 359\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m CustomObjectScope(custom_objects):\n\u001b[0;32m--> 360\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcls_config\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 361\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 362\u001b[0m \u001b[38;5;66;03m# Then `cls` may be a function returning a class.\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;66;03m# in this case by convention `config` holds\u001b[39;00m\n\u001b[1;32m 364\u001b[0m \u001b[38;5;66;03m# the kwargs of the function.\u001b[39;00m\n\u001b[1;32m 365\u001b[0m custom_objects \u001b[38;5;241m=\u001b[39m custom_objects \u001b[38;5;129;01mor\u001b[39;00m {}\n",
|
||
|
|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer.py:720\u001b[0m, in \u001b[0;36mLayer.from_config\u001b[0;34m(cls, config)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfrom_config\u001b[39m(\u001b[38;5;28mcls\u001b[39m, config):\n\u001b[1;32m 706\u001b[0m \u001b[38;5;124;03m\"\"\"Creates a layer from its config.\u001b[39;00m\n\u001b[1;32m 707\u001b[0m \n\u001b[1;32m 708\u001b[0m \u001b[38;5;124;03m This method is the reverse of `get_config`,\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 718\u001b[0m \u001b[38;5;124;03m A layer instance.\u001b[39;00m\n\u001b[1;32m 719\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 720\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
|
|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/layers/pooling.py:862\u001b[0m, in \u001b[0;36mGlobalPooling2D.__init__\u001b[0;34m(self, data_format, **kwargs)\u001b[0m\n\u001b[1;32m 861\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, data_format\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 862\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mGlobalPooling2D\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 863\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_format \u001b[38;5;241m=\u001b[39m conv_utils\u001b[38;5;241m.\u001b[39mnormalize_data_format(data_format)\n\u001b[1;32m 864\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_spec \u001b[38;5;241m=\u001b[39m InputSpec(ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n",
|
||
|
|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/training/tracking/base.py:517\u001b[0m, in \u001b[0;36mno_automatic_dependency_tracking.<locals>._method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m 516\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 517\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 518\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_self_setattr_tracking \u001b[38;5;241m=\u001b[39m previous_value \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n",
|
||
|
|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer.py:340\u001b[0m, in \u001b[0;36mLayer.__init__\u001b[0;34m(self, trainable, name, dtype, dynamic, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m allowed_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m 330\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_dim\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 331\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_shape\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 337\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mimplementation\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 338\u001b[0m }\n\u001b[1;32m 339\u001b[0m \u001b[38;5;66;03m# Validate optional keyword arguments.\u001b[39;00m\n\u001b[0;32m--> 340\u001b[0m \u001b[43mgeneric_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_kwargs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallowed_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 342\u001b[0m \u001b[38;5;66;03m# Mutable properties\u001b[39;00m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;66;03m# Indicates whether the layer's weights are updated during training\u001b[39;00m\n\u001b[1;32m 344\u001b[0m \u001b[38;5;66;03m# and whether the layer's updates are run during training.\u001b[39;00m\n\u001b[1;32m 345\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_trainable \u001b[38;5;241m=\u001b[39m trainable\n",
|
||
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|
"File \u001b[0;32m~/miniconda3/envs/tensorflow-cuda/lib/python3.9/site-packages/tensorflow/python/keras/utils/generic_utils.py:808\u001b[0m, in \u001b[0;36mvalidate_kwargs\u001b[0;34m(kwargs, allowed_kwargs, error_message)\u001b[0m\n\u001b[1;32m 806\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m kwarg \u001b[38;5;129;01min\u001b[39;00m kwargs:\n\u001b[1;32m 807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwarg \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allowed_kwargs:\n\u001b[0;32m--> 808\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(error_message, kwarg)\n",
|
||
|
|
"\u001b[0;31mTypeError\u001b[0m: ('Keyword argument not understood:', 'keepdims')"
|
||
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|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"new_model = tf.keras.models.load_model('Model_1.h5')\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "664cf629",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"language": "python",
|
||
|
|
"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": "3.9.12"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|