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added ML training projects in pytorch

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scott 2022-08-02 20:14:38 -07:00
parent 597372cad3
commit 263512ae7d
13 changed files with 4810 additions and 3116 deletions
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6269
Shoe Classifier_InceptionResNetV2.ipynb
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176
Shoe Classifier_VGG16.ipynb
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@ -5,7 +5,15 @@
"execution_count": 1,
"id": "572dc7fb",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-08-01 23:57:09.348119: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n"
]
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"from matplotlib.image import imread\n",
@ -36,6 +44,7 @@
{
"cell_type": "code",
"execution_count": 2,
"id": "6ea418cc",
"metadata": {},
"outputs": [],
"source": [
@ -72,7 +81,7 @@
"metadata": {},
"outputs": [],
"source": [
"# image_faults.faulty_images() # removes faulty images\n",
"image_faults.faulty_images() # removes faulty images\n",
"df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)"
]
},
@ -112,10 +121,16 @@
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{source:target} dictionary created @ /tf/training_images\n"
"ename": "TypeError",
"evalue": "expected string or bytes-like object",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-0009b269209e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdict_pics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict_pics_jup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'women_cat_list.txt'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mwomen_cats\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'men_cat_list.txt'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-4-4701772f6383>\u001b[0m in \u001b[0;36mdict_pics_jup\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mdict_pics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_pics_source_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mpatt_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIGNORECASE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mpatt_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'(\\.jpg|\\.jpeg|\\.png)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIGNORECASE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpatt_1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mpatt_2\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.8/re.py\u001b[0m in \u001b[0;36msearch\u001b[0;34m(pattern, string, flags)\u001b[0m\n\u001b[1;32m 199\u001b[0m \"\"\"Scan through string looking for a match to the pattern, returning\n\u001b[1;32m 200\u001b[0m a Match object, or None if no match was found.\"\"\"\n\u001b[0;32m--> 201\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 203\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: expected string or bytes-like object"
]
}
],
@ -153,17 +168,10 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "8a3a86a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({'11632': 6505, '45333': 6505, '53548': 6505, '53557': 6505, '55793': 6505, '62107': 6505, '95672': 6505})\n"
]
}
],
"outputs": [],
"source": [
"undersample = RandomUnderSampler(sampling_strategy='auto')\n",
"train, y_under = undersample.fit_resample(df, df['PrimaryCategoryID'])\n",
@ -172,7 +180,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "506aa5cf",
"metadata": {},
"outputs": [],
@ -184,19 +192,10 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "4d72eb90",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 29143 validated image filenames belonging to 7 classes.\n",
"Found 7285 validated image filenames belonging to 7 classes.\n"
]
}
],
"outputs": [],
"source": [
"datagen = ImageDataGenerator(rescale=1./255., \n",
" validation_split=.2,\n",
@ -233,7 +232,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "7b70f37f",
"metadata": {},
"outputs": [],
@ -243,7 +242,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"id": "1ed54bf5",
"metadata": {},
"outputs": [],
@ -260,7 +259,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"id": "85934565",
"metadata": {},
"outputs": [],
@ -271,18 +270,10 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"id": "6322bcad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"outputs": [],
"source": [
"physical_devices = tf.config.list_physical_devices('GPU')\n",
"print(len(physical_devices))\n",
@ -291,7 +282,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "b31af79e",
"metadata": {},
"outputs": [],
@ -301,7 +292,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"id": "fe06f2bf",
"metadata": {},
"outputs": [],
@ -313,7 +304,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "7d3cc82c",
"metadata": {},
"outputs": [],
@ -335,76 +326,12 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "c774d787",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n",
" \n",
" block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n",
" \n",
" block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n",
" \n",
" block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n",
" \n",
" block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n",
" \n",
" block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n",
" \n",
" block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n",
" \n",
" block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n",
" \n",
" block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n",
" \n",
" block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n",
" \n",
" block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n",
" \n",
" block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n",
" \n",
" block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n",
" \n",
" block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n",
" \n",
" block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n",
" \n",
" flatten (Flatten) (None, 25088) 0 \n",
" \n",
" fc1 (Dense) (None, 4096) 102764544 \n",
" \n",
" dropout (Dropout) (None, 4096) 0 \n",
" \n",
" fc2 (Dense) (None, 4096) 16781312 \n",
" \n",
" dropout_1 (Dropout) (None, 4096) 0 \n",
" \n",
" dense (Dense) (None, 7) 28679 \n",
" \n",
"=================================================================\n",
"Total params: 134,289,223\n",
"Trainable params: 134,289,223\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"outputs": [],
"source": [
"#model = add_regularization(model)\n",
"model.summary()\n"
@ -412,7 +339,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"id": "fd5d1246",
"metadata": {},
"outputs": [],
@ -428,28 +355,7 @@
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"911/911 [==============================] - 329s 356ms/step - loss: 1.8477 - accuracy: 0.2577 - val_loss: 1.6306 - val_accuracy: 0.3669\n",
"Epoch 2/30\n",
"911/911 [==============================] - 322s 353ms/step - loss: 1.4882 - accuracy: 0.4353 - val_loss: 1.4317 - val_accuracy: 0.4784\n",
"Epoch 3/30\n",
"911/911 [==============================] - 323s 354ms/step - loss: 1.3046 - accuracy: 0.5158 - val_loss: 1.2747 - val_accuracy: 0.5235\n",
"Epoch 4/30\n",
"911/911 [==============================] - 319s 350ms/step - loss: 1.1691 - accuracy: 0.5681 - val_loss: 1.2090 - val_accuracy: 0.5529\n",
"Epoch 5/30\n",
"911/911 [==============================] - 317s 348ms/step - loss: 1.0389 - accuracy: 0.6185 - val_loss: 1.1774 - val_accuracy: 0.5682\n",
"Epoch 6/30\n",
"911/911 [==============================] - 317s 348ms/step - loss: 0.9125 - accuracy: 0.6656 - val_loss: 1.2237 - val_accuracy: 0.5639\n",
"Epoch 7/30\n",
"147/911 [===>..........................] - ETA: 3:39 - loss: 0.7312 - accuracy: 0.7256"
]
}
],
"outputs": [],
"source": [
"model.fit(x=train_generator,\n",
" steps_per_epoch=len(train_generator),\n",
@ -470,7 +376,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@ -484,7 +390,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
"version": "3.9.12"
}
},
"nbformat": 4,

1051
Shoe Classifier_Xception.ipynb
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105
conf_mx_test.ipynb Normal file
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@ -0,0 +1,105 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "99d6b339",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
}
],
"source": [
"from sklearn.datasets import fetch_openml\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import SGDClassifier\n",
"from sklearn.model_selection import StratifiedKFold, cross_val_predict, train_test_split, StratifiedShuffleSplit,cross_val_score\n",
"from sklearn.base import clone, BaseEstimator\n",
"from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score, precision_recall_curve, roc_curve, roc_auc_score\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"\n",
"import joblib"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "20c2c97e",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "('Keyword argument not understood:', 'keepdims')",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"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",
"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",
"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",
"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",
"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: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",
"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",
"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",
"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",
"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",
"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')"
]
}
],
"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
}

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@ -2,45 +2,15 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 5,
"id": "7eea0d4d",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-12-24 22:16:08.715996: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num GPUs Available: 1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2021-12-24 22:16:11.102972: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
"2021-12-24 22:16:11.103554: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
"2021-12-24 22:16:11.157717: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.157972: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
"pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5\n",
"coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 5.79GiB deviceMemoryBandwidth: 312.97GiB/s\n",
"2021-12-24 22:16:11.157995: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-12-24 22:16:11.191221: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
"2021-12-24 22:16:11.191428: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
"2021-12-24 22:16:11.222375: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
"2021-12-24 22:16:11.226481: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
"2021-12-24 22:16:11.258066: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
"2021-12-24 22:16:11.264224: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
"2021-12-24 22:16:11.324727: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
"2021-12-24 22:16:11.325101: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.325903: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.326485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n"
"Num GPUs Available: 2\n"
]
}
],
@ -51,48 +21,44 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 6,
"id": "33d18ebd",
"metadata": {},
"outputs": [
{
"name": "stderr",
"name": "stdout",
"output_type": "stream",
"text": [
"2021-12-24 22:16:11.339696: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2021-12-24 22:16:11.340741: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
"2021-12-24 22:16:11.340920: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.341179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
"pciBusID: 0000:01:00.0 name: NVIDIA GeForce RTX 2060 computeCapability: 7.5\n",
"coreClock: 1.2GHz coreCount: 30 deviceMemorySize: 5.79GiB deviceMemoryBandwidth: 312.97GiB/s\n",
"2021-12-24 22:16:11.341221: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-12-24 22:16:11.341261: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n",
"2021-12-24 22:16:11.341281: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.10\n",
"2021-12-24 22:16:11.341293: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
"2021-12-24 22:16:11.341304: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
"2021-12-24 22:16:11.341315: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
"2021-12-24 22:16:11.341326: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.10\n",
"2021-12-24 22:16:11.341336: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.7\n",
"2021-12-24 22:16:11.341433: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.341629: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:11.341750: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
"2021-12-24 22:16:11.342051: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.10.1\n",
"2021-12-24 22:16:12.482371: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
"2021-12-24 22:16:12.482394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 \n",
"2021-12-24 22:16:12.482399: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N \n",
"2021-12-24 22:16:12.482832: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:12.483044: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:12.483236: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
"2021-12-24 22:16:12.483356: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 5358 MB memory) -> physical GPU (device: 0, name: NVIDIA GeForce RTX 2060, pci bus id: 0000:01:00.0, compute capability: 7.5)\n",
"2021-12-24 22:16:12.487174: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.10\n"
"2 Physical GPU, 3 Logical GPUs\n"
]
},
}
],
"source": [
"gpus = tf.config.list_physical_devices('GPU')\n",
"if gpus:\n",
" # Create 2 virtual GPUs with 1GB memory each\n",
" try:\n",
" tf.config.set_logical_device_configuration(\n",
" gpus[0],\n",
" [tf.config.LogicalDeviceConfiguration(memory_limit=1024),\n",
" tf.config.LogicalDeviceConfiguration(memory_limit=1024)])\n",
" logical_gpus = tf.config.list_logical_devices('GPU')\n",
" print(len(gpus), \"Physical GPU,\", len(logical_gpus), \"Logical GPUs\")\n",
" except RuntimeError as e:\n",
" # Virtual devices must be set before GPUs have been initialized\n",
" print(e)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2b9ca96e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0\n",
"tf.Tensor(\n",
"[[22. 28.]\n",
" [49. 64.]], shape=(2, 2), dtype=float32)\n"
@ -113,16 +79,30 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "2b9ca96e",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": []
"source": [
"from keras.models import load_model\n",
"\n",
"# returns a compiled model\n",
"# identical to the previous one\n",
"model = load_model('Model_1.h5')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.predict_generator()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
@ -136,7 +116,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
"version": "3.8.10"
}
},
"nbformat": 4,

209
torch_training_version.ipynb Normal file
View File

@ -0,0 +1,209 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"id": "a43c3ccb",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torchvision.models as models\n",
"import pandas as pd\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from torchvision import transforms, utils\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.image import imread\n",
"import pandas as pd\n",
"from collections import Counter\n",
"import json\n",
"import os\n",
"import re\n",
"import tempfile\n",
"from os.path import exists\n",
"from PIL import ImageFile\n",
"import sklearn as sk\n",
"from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
"import image_faults"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6c7577a6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.cuda.device_count()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c7e9b947",
"metadata": {},
"outputs": [],
"source": [
"resnet18 = models.resnet18(pretrained=True)\n",
"vgg16 = models.vgg16(pretrained=True)\n",
"inception = models.inception_v3(pretrained=True)\n",
"resnext50_32x4d = models.resnext50_32x4d(pretrained=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eabc61b2",
"metadata": {},
"outputs": [],
"source": [
"class Shoes(Dataset):\n",
" def __init__(self, csvfile, root_dir, transform=None):\n",
" self.shoes_df = pd.read_csv(csvfile)\n",
" self.root_dir = root_dir\n",
" self.transform = transform\n",
" \n",
" def __getitem__(self, index):\n",
" self.shoes_df.iloc[index]\n",
" \n",
" \n",
" def __getitem__(self, idx):\n",
" if torch.is_tensor(idx):\n",
" idx = idx.tolist()\n",
"\n",
" img_name = os.path.join(self.root_dir,\n",
" self.data.iloc[idx, 0])\n",
" image = io.imread(img_name)\n",
" data = self.data.iloc[idx, 1:]\n",
" data = np.array([data])\n",
" data = data.astype('float').reshape(-1, 2)\n",
" sample = {'image': image, 'landmarks': data}\n",
"\n",
" if self.transform:\n",
" sample = self.transform(sample)\n",
"\n",
" return sample\n",
" \n",
" def __len__(self):\n",
" return len(self.data)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a0fc66b0",
"metadata": {},
"outputs": [],
"source": [
"something = pd.read_csv('expanded_class.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ed2aceeb",
"metadata": {},
"outputs": [],
"source": [
"def dict_pics_jup():\n",
" '''\n",
" {source:target} dict used to replace source urls with image location as input\n",
" '''\n",
" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
" with open('temp_pics_source_list.txt') as f:\n",
" temp_pics_source_list = json.load(f)\n",
" \n",
" dict_pics = {}\n",
" for k in temp_pics_source_list:\n",
" try: \n",
" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
" if patt_1 and patt_2 is not None:\n",
" tag = patt_1.group() + patt_2.group().lower()\n",
" file_name = target_dir + os.sep + tag\n",
" dict_pics.update({k:file_name})\n",
" except TypeError:\n",
" print(k)\n",
" print(\"{source:target} dictionary created @ \" + target_dir)\n",
" return dict_pics"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "0095fa33",
"metadata": {},
"outputs": [],
"source": [
"def cleanup():\n",
" with open('women_cat_list.txt') as f:\n",
" women_cats = json.load(f)\n",
" with open('men_cat_list.txt') as f:\n",
" men_cats = json.load(f)\n",
"\n",
" with open('temp_pics_source_list.txt') as f:\n",
" tempics = json.load(f)\n",
" # list of image urls that did not get named properly which will be removed from the dataframe\n",
" drop_row_vals = []\n",
" for pic in tempics:\n",
" try:\n",
" dict_pics[pic]\n",
" except KeyError:\n",
" drop_row_vals.append(pic)\n",
"\n",
" df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
" df = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
" df = df[df.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
"\n",
" blah = pd.Series(df.PictureURL)\n",
" df = df.drop(labels=['PictureURL'], axis=1)\n",
"\n",
" blah = blah.apply(lambda x: dict_pics[x])\n",
" df = pd.concat([blah, df],axis=1)\n",
" df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "edd196dc",
"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": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}