530 lines
33 KiB
Plaintext
530 lines
33 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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"from collections import Counter\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 tempfile\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 imblearn.under_sampling import RandomUnderSampler\n",
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"from PIL import ImageFile\n",
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"import sklearn as sk\n",
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"from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\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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"metadata": {},
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"outputs": [],
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"source": [
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"def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
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"\n",
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" if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
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" print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
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" return model\n",
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"\n",
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" for layer in model.layers:\n",
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" for attr in ['kernel_regularizer']:\n",
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" if hasattr(layer, attr):\n",
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" setattr(layer, attr, regularizer)\n",
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"\n",
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" # When we change the layers attributes, the change only happens in the model config file\n",
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" model_json = model.to_json()\n",
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"\n",
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" # Save the weights before reloading the model.\n",
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" tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
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" model.save_weights(tmp_weights_path)\n",
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"\n",
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" # load the model from the config\n",
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" model = tf.keras.models.model_from_json(model_json)\n",
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" \n",
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" # Reload the model weights\n",
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" model.load_weights(tmp_weights_path, by_name=True)\n",
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" return model"
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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": "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": 4,
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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": 5,
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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": 6,
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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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"Counter({'11498': 546, '11504': 546, '11505': 546, '11632': 546, '15709': 546, '24087': 546, '45333': 546, '53120': 546, '53548': 546, '53557': 546, '55793': 546, '62107': 546, '95672': 546})\n"
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]
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}
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],
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"source": [
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"undersample = RandomUnderSampler(sampling_strategy='auto')\n",
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"train, y_under = undersample.fit_resample(df, df['PrimaryCategoryID'])\n",
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"print(Counter(train['PrimaryCategoryID']))"
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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": "506aa5cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"train, test = train_test_split(train, test_size=0.2, random_state=42)\n",
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"# stratify=train['PrimaryCategoryID']\n",
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"# train['PrimaryCategoryID'].value_counts()"
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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": "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 4542 validated image filenames belonging to 13 classes.\n",
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"Found 1135 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=.2,\n",
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" #samplewise_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= 90,\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=train[:len(train)],\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=(244,244),\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=train[:len(train)], # is using train right?\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=(244,244),\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": 9,
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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": 10,
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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": 11,
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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": 12,
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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": 13,
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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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"base_model = tf.keras.applications.vgg16.VGG16(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": 14,
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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 base_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": 15,
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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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"# loop through layers, add Dropout after layers 'fc1' and 'fc2'\n",
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"updated_model = Sequential()\n",
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"for layer in base_model.layers[:-1]:\n",
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" updated_model.add(layer)\n",
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" if layer.name in ['fc1', 'fc2']:\n",
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" updated_model.add(Dropout(.75))\n",
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"\n",
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"model = updated_model\n",
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"\n",
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"for layer in model.layers:\n",
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" layer.trainable = True\n",
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"\n",
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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": 16,
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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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Model: \"sequential\"\n",
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"_________________________________________________________________\n",
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" Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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" block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n",
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" \n",
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" block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n",
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" \n",
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" block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n",
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" \n",
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" block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n",
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" \n",
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" block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n",
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" \n",
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" block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n",
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" \n",
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" block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n",
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" \n",
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" block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n",
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" \n",
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" block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n",
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" \n",
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" block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n",
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" \n",
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" block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n",
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" \n",
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" block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n",
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" \n",
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" block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n",
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" \n",
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" block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n",
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" \n",
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" block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n",
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" \n",
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" block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n",
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" \n",
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" block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n",
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" \n",
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" block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n",
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" \n",
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" flatten (Flatten) (None, 25088) 0 \n",
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" \n",
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" fc1 (Dense) (None, 4096) 102764544 \n",
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" \n",
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" dropout (Dropout) (None, 4096) 0 \n",
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" \n",
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" fc2 (Dense) (None, 4096) 16781312 \n",
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" \n",
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" dropout_1 (Dropout) (None, 4096) 0 \n",
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" \n",
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" dense (Dense) (None, 13) 53261 \n",
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" \n",
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"=================================================================\n",
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"Total params: 134,313,805\n",
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"Trainable params: 134,313,805\n",
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"Non-trainable params: 0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"#model = add_regularization(model)\n",
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"model.summary()\n"
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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": "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=.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": 18,
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"id": "9cd2ba27",
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"metadata": {
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"scrolled": false
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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 1/30\n",
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"142/142 [==============================] - 62s 404ms/step - loss: 2.7986 - accuracy: 0.0771 - val_loss: 2.5716 - val_accuracy: 0.0670\n",
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"Epoch 2/30\n",
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"142/142 [==============================] - 53s 370ms/step - loss: 2.6351 - accuracy: 0.0790 - val_loss: 2.5728 - val_accuracy: 0.0802\n",
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"Epoch 3/30\n",
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"142/142 [==============================] - 53s 370ms/step - loss: 2.6157 - accuracy: 0.0852 - val_loss: 2.5786 - val_accuracy: 0.0705\n",
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"Epoch 4/30\n",
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"142/142 [==============================] - 53s 371ms/step - loss: 2.6087 - accuracy: 0.0821 - val_loss: 2.5501 - val_accuracy: 0.1013\n",
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"Epoch 5/30\n",
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"142/142 [==============================] - 53s 371ms/step - loss: 2.5420 - accuracy: 0.1182 - val_loss: 2.4237 - val_accuracy: 0.1401\n",
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"Epoch 6/30\n",
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"142/142 [==============================] - 52s 368ms/step - loss: 2.4275 - accuracy: 0.1640 - val_loss: 2.3050 - val_accuracy: 0.1982\n",
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"Epoch 7/30\n",
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"142/142 [==============================] - 52s 368ms/step - loss: 2.3270 - accuracy: 0.2008 - val_loss: 2.2103 - val_accuracy: 0.2273\n",
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"Epoch 8/30\n",
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"142/142 [==============================] - 54s 378ms/step - loss: 2.2011 - accuracy: 0.2294 - val_loss: 2.0607 - val_accuracy: 0.2872\n",
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"Epoch 9/30\n",
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"142/142 [==============================] - 54s 378ms/step - loss: 2.0790 - accuracy: 0.2781 - val_loss: 1.9376 - val_accuracy: 0.3419\n",
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"Epoch 10/30\n",
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"142/142 [==============================] - 52s 368ms/step - loss: 1.8708 - accuracy: 0.3417 - val_loss: 2.0407 - val_accuracy: 0.3198\n",
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"Epoch 11/30\n",
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"142/142 [==============================] - 52s 368ms/step - loss: 1.7588 - accuracy: 0.3743 - val_loss: 1.8968 - val_accuracy: 0.3401\n",
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"Epoch 12/30\n",
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"142/142 [==============================] - 52s 369ms/step - loss: 1.5927 - accuracy: 0.4251 - val_loss: 1.8735 - val_accuracy: 0.3542\n",
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"Epoch 13/30\n",
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"142/142 [==============================] - 53s 369ms/step - loss: 1.4704 - accuracy: 0.4626 - val_loss: 1.8489 - val_accuracy: 0.3753\n",
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"Epoch 14/30\n",
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"142/142 [==============================] - 52s 367ms/step - loss: 1.2452 - accuracy: 0.5484 - val_loss: 1.9595 - val_accuracy: 0.3656\n",
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"Epoch 15/30\n",
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"142/142 [==============================] - 52s 370ms/step - loss: 1.1410 - accuracy: 0.5885 - val_loss: 1.9159 - val_accuracy: 0.3930\n",
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"Epoch 16/30\n",
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"142/142 [==============================] - 52s 367ms/step - loss: 0.9705 - accuracy: 0.6510 - val_loss: 2.1161 - val_accuracy: 0.3921\n",
|
|
"Epoch 17/30\n",
|
|
"142/142 [==============================] - 52s 368ms/step - loss: 0.7992 - accuracy: 0.7041 - val_loss: 2.1271 - val_accuracy: 0.4097\n",
|
|
"Epoch 18/30\n",
|
|
"142/142 [==============================] - 52s 369ms/step - loss: 0.7544 - accuracy: 0.7305 - val_loss: 2.0093 - val_accuracy: 0.4361\n",
|
|
"Epoch 19/30\n",
|
|
"142/142 [==============================] - 52s 367ms/step - loss: 0.5651 - accuracy: 0.7961 - val_loss: 2.4199 - val_accuracy: 0.3850\n",
|
|
"Epoch 20/30\n",
|
|
"142/142 [==============================] - 52s 366ms/step - loss: 0.4566 - accuracy: 0.8342 - val_loss: 2.4058 - val_accuracy: 0.4352\n",
|
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"Epoch 21/30\n",
|
|
"142/142 [==============================] - 52s 364ms/step - loss: 0.3841 - accuracy: 0.8622 - val_loss: 2.5407 - val_accuracy: 0.4141\n",
|
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"Epoch 22/30\n",
|
|
"142/142 [==============================] - 52s 366ms/step - loss: 0.2496 - accuracy: 0.9086 - val_loss: 3.0696 - val_accuracy: 0.4088\n",
|
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"Epoch 23/30\n",
|
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"142/142 [==============================] - 52s 368ms/step - loss: 0.2847 - accuracy: 0.8985 - val_loss: 2.7929 - val_accuracy: 0.4273\n",
|
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"Epoch 24/30\n",
|
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"142/142 [==============================] - 52s 369ms/step - loss: 0.2035 - accuracy: 0.9293 - val_loss: 3.0300 - val_accuracy: 0.4132\n",
|
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"Epoch 25/30\n",
|
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"142/142 [==============================] - ETA: 0s - loss: 0.1872 - accuracy: 0.9377"
|
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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-18-4cd4443bbf2a>\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;36m30\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 1250\u001b[0m \u001b[0mmodel\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[0m\n\u001b[1;32m 1251\u001b[0m steps_per_execution=self._steps_per_execution)\n\u001b[0;32m-> 1252\u001b[0;31m val_logs = self.evaluate(\n\u001b[0m\u001b[1;32m 1253\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1254\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_y\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;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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 1536\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_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-> 1537\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_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 1538\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 1539\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 947\u001b[0m \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 949\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 950\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mALLOW_DYNAMIC_VARIABLE_CREATION\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 951\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\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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]
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}
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],
|
|
"source": [
|
|
"model.fit(x=train_generator,\n",
|
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" steps_per_epoch=len(train_generator),\n",
|
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" validation_data=validation_generator,\n",
|
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" validation_steps=len(validation_generator),\n",
|
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" epochs=30,\n",
|
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" verbose=1)"
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": null,
|
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"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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],
|
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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",
|
|
"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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}
|