450 lines
27 KiB
Plaintext
450 lines
27 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "572dc7fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib import pyplot as plt\n",
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"from matplotlib.image import imread\n",
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"import pandas as pd\n",
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"import json\n",
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"import os\n",
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"import re\n",
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"import numpy as np\n",
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"from os.path import exists\n",
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"from PIL import ImageFile\n",
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"#import sklearn as sk\n",
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"import tensorflow as tf\n",
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"import tensorflow.keras\n",
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"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
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"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Activation\n",
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"from tensorflow.keras.models import Sequential\n",
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"from tensorflow.keras.optimizers import Adam\n",
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"# custom modules\n",
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"import image_faults\n",
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"\n",
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"ImageFile.LOAD_TRUNCATED_IMAGES = True"
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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": 2,
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"id": "a5c72863",
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"metadata": {},
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"outputs": [],
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"source": [
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"# image_faults.faulty_images() # removes faulty images\n",
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"df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)"
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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": 3,
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"id": "1057a442",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{source:target} dictionary created @ /tf/training_images\n"
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]
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}
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],
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"source": [
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"def dict_pics():\n",
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" target_dir = os.getcwd() + os.sep + \"training_images\"\n",
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" with open('temp_pics_source_list.txt') as f:\n",
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" temp_pics_source_list = json.load(f)\n",
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" dict_pics = {k:target_dir + os.sep + re.search(r'[^/]+(?=/\\$_|.jpg)', k, re.IGNORECASE).group() + '.jpg' for k in temp_pics_source_list}\n",
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" print(\"{source:target} dictionary created @ \" + target_dir)\n",
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" return dict_pics\n",
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"\n",
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"dict_pics = dict_pics()\n",
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"blah = pd.Series(df.PictureURL)\n",
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"df = df.drop(labels=['PictureURL'], axis=1)\n",
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"blah = blah.apply(lambda x: dict_pics[x])\n",
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"df = pd.concat([blah, df],axis=1)\n",
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"df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers\n",
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"# removes non-existent image paths"
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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": 4,
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"id": "7a6146e6",
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"metadata": {},
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"outputs": [],
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"source": [
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"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
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"\n",
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"df=df.sample(frac=1)"
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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": 5,
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"id": "4d72eb90",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Found 6217 validated image filenames belonging to 13 classes.\n",
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"Found 2664 validated image filenames belonging to 13 classes.\n"
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]
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},
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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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"/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"datagen = ImageDataGenerator(rescale=1./255., \n",
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" validation_split=.3,\n",
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" #featurewise_std_normalization=True,\n",
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" #horizontal_flip= True,\n",
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" #vertical_flip= True,\n",
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" #width_shift_range= 0.2,\n",
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" #height_shift_range= 0.2,\n",
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" #rotation_range= 180,\n",
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" preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\n",
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"train_generator=datagen.flow_from_dataframe(\n",
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" dataframe=df[:len(df)],\n",
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" directory='./training_images',\n",
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" x_col='PictureURL',\n",
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" y_col='PrimaryCategoryID',\n",
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" batch_size=32,\n",
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" seed=42,\n",
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" shuffle=True,\n",
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" target_size=(224,224),\n",
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" subset='training'\n",
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" )\n",
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"validation_generator=datagen.flow_from_dataframe(\n",
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" dataframe=df[:len(df)],\n",
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" directory='./training_images',\n",
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" x_col='PictureURL',\n",
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" y_col='PrimaryCategoryID',\n",
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" batch_size=32,\n",
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" seed=42,\n",
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" shuffle=True,\n",
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" target_size=(224,224),\n",
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" subset='validation'\n",
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" )"
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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": 6,
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"id": "7b70f37f",
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"metadata": {},
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"outputs": [],
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"source": [
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"imgs, labels = next(train_generator)"
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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": "1ed54bf5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def plotImages(images_arr):\n",
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" fig, axes = plt.subplots(1, 10, figsize=(20,20))\n",
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" axes = axes.flatten()\n",
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" for img, ax in zip( images_arr, axes):\n",
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" ax.imshow(img)\n",
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" ax.axis('off')\n",
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" plt.tight_layout()\n",
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" plt.show()"
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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": 8,
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"id": "85934565",
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"metadata": {},
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"outputs": [],
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"source": [
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"#plotImages(imgs)\n",
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"#print(labels)"
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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": 9,
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"id": "6322bcad",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1\n"
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]
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}
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],
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"source": [
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"physical_devices = tf.config.list_physical_devices('GPU')\n",
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"print(len(physical_devices))\n",
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"tf.config.experimental.set_memory_growth(physical_devices[0], True)"
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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": 10,
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"id": "07fd25c6",
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"metadata": {},
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"outputs": [],
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"source": [
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"# see https://www.kaggle.com/dmitrypukhov/cnn-with-imagedatagenerator-flow-from-dataframe for train/test/val split \n",
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"# example\n",
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"\n",
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"# may need to either create a test dataset from the original dataset or just download a new one"
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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": 11,
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"id": "b31af79e",
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"metadata": {},
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"outputs": [],
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"source": [
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"vgg16_model = tf.keras.applications.vgg16.VGG16(weights='imagenet')\n",
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"#weights='imagenet'"
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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": 12,
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"id": "fe06f2bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = Sequential()\n",
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"for layer in vgg16_model.layers[:-1]:\n",
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" model.add(layer)"
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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": 13,
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"id": "7d3cc82c",
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"metadata": {},
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"outputs": [],
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"source": [
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"for layer in model.layers:\n",
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" layer.trainable = True"
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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": 14,
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"id": "ea620129",
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"metadata": {},
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"outputs": [],
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"source": [
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"model.add(Dense(units=13, activation='softmax'))"
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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": 15,
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"id": "c774d787",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"#model.summary()"
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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": 16,
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"id": "fd5d1246",
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"metadata": {},
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"outputs": [],
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"source": [
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"model.compile(optimizer=Adam(learning_rate=0.0001), loss='categorical_crossentropy',\n",
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" metrics=['accuracy'])"
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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": 17,
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"id": "9cd2ba27",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Epoch 1/100\n",
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"195/195 - 83s - loss: 2.5928 - accuracy: 0.1139 - val_loss: 2.3657 - val_accuracy: 0.1674 - 83s/epoch - 426ms/step\n",
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"Epoch 2/100\n",
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"195/195 - 77s - loss: 2.1473 - accuracy: 0.2582 - val_loss: 1.9276 - val_accuracy: 0.3281 - 77s/epoch - 394ms/step\n",
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"Epoch 3/100\n",
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"195/195 - 78s - loss: 1.7234 - accuracy: 0.3973 - val_loss: 1.6724 - val_accuracy: 0.4050 - 78s/epoch - 400ms/step\n",
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"Epoch 4/100\n",
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"195/195 - 78s - loss: 1.4692 - accuracy: 0.4843 - val_loss: 1.5583 - val_accuracy: 0.4662 - 78s/epoch - 402ms/step\n",
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"Epoch 5/100\n",
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"195/195 - 79s - loss: 1.2598 - accuracy: 0.5477 - val_loss: 1.5135 - val_accuracy: 0.4944 - 79s/epoch - 403ms/step\n",
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"Epoch 6/100\n",
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"195/195 - 79s - loss: 1.0220 - accuracy: 0.6376 - val_loss: 1.5566 - val_accuracy: 0.4962 - 79s/epoch - 404ms/step\n",
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"Epoch 7/100\n",
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"195/195 - 78s - loss: 0.8021 - accuracy: 0.7084 - val_loss: 1.7647 - val_accuracy: 0.4711 - 78s/epoch - 398ms/step\n",
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"Epoch 8/100\n",
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"195/195 - 78s - loss: 0.5998 - accuracy: 0.7804 - val_loss: 1.8439 - val_accuracy: 0.4869 - 78s/epoch - 400ms/step\n",
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"Epoch 9/100\n",
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"195/195 - 77s - loss: 0.3910 - accuracy: 0.8631 - val_loss: 2.1197 - val_accuracy: 0.4872 - 77s/epoch - 397ms/step\n",
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"Epoch 10/100\n",
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"195/195 - 78s - loss: 0.2600 - accuracy: 0.9094 - val_loss: 2.3586 - val_accuracy: 0.4703 - 78s/epoch - 402ms/step\n",
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"Epoch 11/100\n",
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"195/195 - 77s - loss: 0.2066 - accuracy: 0.9279 - val_loss: 2.5012 - val_accuracy: 0.4632 - 77s/epoch - 397ms/step\n",
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"Epoch 12/100\n",
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"195/195 - 77s - loss: 0.1266 - accuracy: 0.9571 - val_loss: 2.5961 - val_accuracy: 0.4854 - 77s/epoch - 395ms/step\n",
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"Epoch 13/100\n",
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"195/195 - 77s - loss: 0.1098 - accuracy: 0.9648 - val_loss: 2.9123 - val_accuracy: 0.4602 - 77s/epoch - 395ms/step\n",
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"Epoch 14/100\n",
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"195/195 - 77s - loss: 0.0794 - accuracy: 0.9744 - val_loss: 2.9060 - val_accuracy: 0.4752 - 77s/epoch - 395ms/step\n",
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"Epoch 15/100\n",
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"195/195 - 77s - loss: 0.1061 - accuracy: 0.9656 - val_loss: 2.7269 - val_accuracy: 0.4658 - 77s/epoch - 396ms/step\n",
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"Epoch 16/100\n",
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"195/195 - 77s - loss: 0.0908 - accuracy: 0.9712 - val_loss: 2.9450 - val_accuracy: 0.4621 - 77s/epoch - 396ms/step\n",
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"Epoch 17/100\n",
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"195/195 - 77s - loss: 0.0820 - accuracy: 0.9736 - val_loss: 2.8900 - val_accuracy: 0.4703 - 77s/epoch - 396ms/step\n",
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"Epoch 18/100\n",
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"195/195 - 77s - loss: 0.0827 - accuracy: 0.9722 - val_loss: 2.9304 - val_accuracy: 0.4745 - 77s/epoch - 395ms/step\n",
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"Epoch 19/100\n",
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"195/195 - 77s - loss: 0.0726 - accuracy: 0.9757 - val_loss: 3.2673 - val_accuracy: 0.4580 - 77s/epoch - 393ms/step\n",
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"Epoch 20/100\n",
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"195/195 - 77s - loss: 0.0560 - accuracy: 0.9821 - val_loss: 3.0101 - val_accuracy: 0.4670 - 77s/epoch - 394ms/step\n",
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"Epoch 21/100\n",
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"195/195 - 77s - loss: 0.0355 - accuracy: 0.9907 - val_loss: 3.2575 - val_accuracy: 0.4568 - 77s/epoch - 395ms/step\n",
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"Epoch 22/100\n",
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"195/195 - 78s - loss: 0.0950 - accuracy: 0.9693 - val_loss: 3.0716 - val_accuracy: 0.4568 - 78s/epoch - 399ms/step\n",
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"Epoch 23/100\n",
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"195/195 - 78s - loss: 0.0307 - accuracy: 0.9910 - val_loss: 3.6348 - val_accuracy: 0.4741 - 78s/epoch - 401ms/step\n",
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"Epoch 24/100\n",
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"195/195 - 77s - loss: 0.0315 - accuracy: 0.9903 - val_loss: 3.2422 - val_accuracy: 0.4711 - 77s/epoch - 396ms/step\n",
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"Epoch 25/100\n",
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"195/195 - 78s - loss: 0.0640 - accuracy: 0.9789 - val_loss: 3.5402 - val_accuracy: 0.4298 - 78s/epoch - 402ms/step\n",
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"Epoch 26/100\n",
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"195/195 - 78s - loss: 0.0916 - accuracy: 0.9715 - val_loss: 3.1916 - val_accuracy: 0.4520 - 78s/epoch - 399ms/step\n",
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"Epoch 27/100\n",
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"195/195 - 77s - loss: 0.0634 - accuracy: 0.9799 - val_loss: 3.2234 - val_accuracy: 0.4602 - 77s/epoch - 394ms/step\n",
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"Epoch 28/100\n",
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"195/195 - 78s - loss: 0.0673 - accuracy: 0.9788 - val_loss: 3.4435 - val_accuracy: 0.4647 - 78s/epoch - 400ms/step\n",
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"Epoch 29/100\n",
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"195/195 - 78s - loss: 0.0440 - accuracy: 0.9870 - val_loss: 3.3068 - val_accuracy: 0.4598 - 78s/epoch - 400ms/step\n",
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"Epoch 30/100\n",
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"195/195 - 78s - loss: 0.0165 - accuracy: 0.9950 - val_loss: 3.6233 - val_accuracy: 0.4681 - 78s/epoch - 401ms/step\n",
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"Epoch 31/100\n",
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"195/195 - 79s - loss: 0.0303 - accuracy: 0.9910 - val_loss: 3.7398 - val_accuracy: 0.4572 - 79s/epoch - 403ms/step\n",
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"Epoch 32/100\n",
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"195/195 - 78s - loss: 0.0554 - accuracy: 0.9815 - val_loss: 3.5560 - val_accuracy: 0.4369 - 78s/epoch - 399ms/step\n",
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"Epoch 33/100\n",
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"195/195 - 78s - loss: 0.0556 - accuracy: 0.9829 - val_loss: 3.6555 - val_accuracy: 0.4610 - 78s/epoch - 401ms/step\n",
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"Epoch 34/100\n",
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"195/195 - 78s - loss: 0.0624 - accuracy: 0.9809 - val_loss: 3.3500 - val_accuracy: 0.4617 - 78s/epoch - 401ms/step\n",
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"Epoch 35/100\n",
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"195/195 - 77s - loss: 0.0367 - accuracy: 0.9886 - val_loss: 3.5968 - val_accuracy: 0.4625 - 77s/epoch - 394ms/step\n",
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"Epoch 36/100\n",
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"195/195 - 78s - loss: 0.0301 - accuracy: 0.9912 - val_loss: 3.5188 - val_accuracy: 0.4643 - 78s/epoch - 399ms/step\n",
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"Epoch 37/100\n",
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"195/195 - 79s - loss: 0.0491 - accuracy: 0.9850 - val_loss: 3.3079 - val_accuracy: 0.4471 - 79s/epoch - 403ms/step\n",
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"Epoch 38/100\n",
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"195/195 - 78s - loss: 0.0577 - accuracy: 0.9821 - val_loss: 3.4042 - val_accuracy: 0.4381 - 78s/epoch - 400ms/step\n",
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"Epoch 39/100\n",
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"195/195 - 78s - loss: 0.0431 - accuracy: 0.9878 - val_loss: 3.4389 - val_accuracy: 0.4550 - 78s/epoch - 399ms/step\n",
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"Epoch 40/100\n",
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"195/195 - 77s - loss: 0.0218 - accuracy: 0.9940 - val_loss: 3.1989 - val_accuracy: 0.4854 - 77s/epoch - 397ms/step\n",
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"Epoch 41/100\n",
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"195/195 - 77s - loss: 0.0296 - accuracy: 0.9921 - val_loss: 3.2759 - val_accuracy: 0.4651 - 77s/epoch - 394ms/step\n",
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"Epoch 42/100\n",
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"195/195 - 78s - loss: 0.0176 - accuracy: 0.9947 - val_loss: 3.2391 - val_accuracy: 0.4745 - 78s/epoch - 398ms/step\n",
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"Epoch 43/100\n",
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"195/195 - 79s - loss: 0.0099 - accuracy: 0.9965 - val_loss: 3.5696 - val_accuracy: 0.4553 - 79s/epoch - 405ms/step\n",
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"Epoch 44/100\n",
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"195/195 - 78s - loss: 0.0516 - accuracy: 0.9852 - val_loss: 3.3857 - val_accuracy: 0.4482 - 78s/epoch - 402ms/step\n",
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"Epoch 45/100\n",
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"195/195 - 79s - loss: 0.0412 - accuracy: 0.9860 - val_loss: 3.3717 - val_accuracy: 0.4580 - 79s/epoch - 404ms/step\n",
|
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"Epoch 46/100\n"
|
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]
|
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},
|
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{
|
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"ename": "KeyboardInterrupt",
|
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"evalue": "",
|
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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;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-17-1a4715fb06ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\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 3\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\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[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\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 5\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 65\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m 1214\u001b[0m _r=1):\n\u001b[1;32m 1215\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\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 1217\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\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",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\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;32m--> 910\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\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",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 940\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 943\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\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[1;32m 944\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3128\u001b[0m (graph_function,\n\u001b[1;32m 3129\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 3131\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1957\u001b[0m and executing_eagerly):\n\u001b[1;32m 1958\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1960\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1961\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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 597\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\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--> 598\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\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 600\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\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;32m---> 58\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 59\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 60\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
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"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.fit(x=train_generator,\n",
|
|
" steps_per_epoch=len(train_generator),\n",
|
|
" validation_data=validation_generator,\n",
|
|
" validation_steps=len(validation_generator),\n",
|
|
" epochs=100,\n",
|
|
" verbose=2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "63f791af",
|
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"metadata": {},
|
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"outputs": [],
|
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"source": []
|
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}
|
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],
|
|
"metadata": {
|
|
"kernelspec": {
|
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"display_name": "Python 3",
|
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"language": "python",
|
|
"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
|
"name": "ipython",
|
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"version": 3
|
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},
|
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|