839 lines
56 KiB
Plaintext
839 lines
56 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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"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)\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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"def dict_pics_jup():\n",
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" '''\n",
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" {source:target} dict used to replace source urls with image location as input\n",
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" '''\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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" \n",
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" dict_pics = {}\n",
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" for k in temp_pics_source_list:\n",
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" try:\n",
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" \n",
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" patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n",
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" patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n",
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" if patt_1 and patt_2 is not None:\n",
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" tag = patt_1.group() + patt_2.group().lower()\n",
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" file_name = target_dir + os.sep + tag\n",
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" dict_pics.update({k:file_name})\n",
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" except TypeError:\n",
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" print(k)\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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]
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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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"nan\n",
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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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"dict_pics = dict_pics_jup()\n",
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"\n",
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"with open('women_cat_list.txt') as f:\n",
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" women_cats = json.load(f)\n",
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"with open('men_cat_list.txt') as f:\n",
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" men_cats = json.load(f)\n",
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" \n",
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"with open('temp_pics_source_list.txt') as f:\n",
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" tempics = json.load(f)\n",
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"# list of image urls that did not get named properly which will be removed from the dataframe\n",
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"drop_row_vals = []\n",
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"for pic in tempics:\n",
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" try:\n",
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" dict_pics[pic]\n",
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" except KeyError:\n",
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" drop_row_vals.append(pic)\n",
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"\n",
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"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
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"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
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"df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n",
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"\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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"\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"
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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=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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"id": "114cc3c0",
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"metadata": {},
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"outputs": [],
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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=0.1, 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 17660 validated image filenames belonging to 7 classes.\n",
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"Found 4414 validated image filenames belonging to 7 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.xception.preprocess_input)\n",
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"\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=64,\n",
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" seed=42,\n",
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" shuffle=True,\n",
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" target_size=(299,299),\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=64,\n",
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" seed=42,\n",
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" shuffle=True,\n",
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" target_size=(299,299),\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": "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": 14,
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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.xception.Xception(include_top=False, weights='imagenet', pooling='avg')\n",
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"#base_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": 15,
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"id": "fe06f2bf",
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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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"Model: \"model\"\n",
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"__________________________________________________________________________________________________\n",
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" Layer (type) Output Shape Param # Connected to \n",
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"==================================================================================================\n",
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" input_1 (InputLayer) [(None, None, None, 0 [] \n",
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" 3)] \n",
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" \n",
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" block1_conv1 (Conv2D) (None, None, None, 864 ['input_1[0][0]'] \n",
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" 32) \n",
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" \n",
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" block1_conv1_bn (BatchNormaliz (None, None, None, 128 ['block1_conv1[0][0]'] \n",
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" ation) 32) \n",
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" \n",
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" block1_conv1_act (Activation) (None, None, None, 0 ['block1_conv1_bn[0][0]'] \n",
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" 32) \n",
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" \n",
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" block1_conv2 (Conv2D) (None, None, None, 18432 ['block1_conv1_act[0][0]'] \n",
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" 64) \n",
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" \n",
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" block1_conv2_bn (BatchNormaliz (None, None, None, 256 ['block1_conv2[0][0]'] \n",
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" ation) 64) \n",
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" \n",
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" block1_conv2_act (Activation) (None, None, None, 0 ['block1_conv2_bn[0][0]'] \n",
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" 64) \n",
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" \n",
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" block2_sepconv1 (SeparableConv (None, None, None, 8768 ['block1_conv2_act[0][0]'] \n",
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" 2D) 128) \n",
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" \n",
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" block2_sepconv1_bn (BatchNorma (None, None, None, 512 ['block2_sepconv1[0][0]'] \n",
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" lization) 128) \n",
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" \n",
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" block2_sepconv2_act (Activatio (None, None, None, 0 ['block2_sepconv1_bn[0][0]'] \n",
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" n) 128) \n",
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" \n",
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" block2_sepconv2 (SeparableConv (None, None, None, 17536 ['block2_sepconv2_act[0][0]'] \n",
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" 2D) 128) \n",
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" \n",
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" block2_sepconv2_bn (BatchNorma (None, None, None, 512 ['block2_sepconv2[0][0]'] \n",
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" lization) 128) \n",
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" \n",
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" conv2d (Conv2D) (None, None, None, 8192 ['block1_conv2_act[0][0]'] \n",
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" 128) \n",
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" \n",
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" block2_pool (MaxPooling2D) (None, None, None, 0 ['block2_sepconv2_bn[0][0]'] \n",
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" 128) \n",
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" \n",
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" batch_normalization (BatchNorm (None, None, None, 512 ['conv2d[0][0]'] \n",
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" alization) 128) \n",
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" \n",
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" add (Add) (None, None, None, 0 ['block2_pool[0][0]', \n",
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" 128) 'batch_normalization[0][0]'] \n",
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" \n",
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" block3_sepconv1_act (Activatio (None, None, None, 0 ['add[0][0]'] \n",
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" n) 128) \n",
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" \n",
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" block3_sepconv1 (SeparableConv (None, None, None, 33920 ['block3_sepconv1_act[0][0]'] \n",
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" 2D) 256) \n",
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" \n",
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" block3_sepconv1_bn (BatchNorma (None, None, None, 1024 ['block3_sepconv1[0][0]'] \n",
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" lization) 256) \n",
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" \n",
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" block3_sepconv2_act (Activatio (None, None, None, 0 ['block3_sepconv1_bn[0][0]'] \n",
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" n) 256) \n",
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" \n",
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" block3_sepconv2 (SeparableConv (None, None, None, 67840 ['block3_sepconv2_act[0][0]'] \n",
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" 2D) 256) \n",
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" \n",
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" block3_sepconv2_bn (BatchNorma (None, None, None, 1024 ['block3_sepconv2[0][0]'] \n",
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" lization) 256) \n",
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" \n",
|
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" conv2d_1 (Conv2D) (None, None, None, 32768 ['add[0][0]'] \n",
|
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" 256) \n",
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" \n",
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" block3_pool (MaxPooling2D) (None, None, None, 0 ['block3_sepconv2_bn[0][0]'] \n",
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" 256) \n",
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" \n",
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" batch_normalization_1 (BatchNo (None, None, None, 1024 ['conv2d_1[0][0]'] \n",
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" rmalization) 256) \n",
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" \n",
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" add_1 (Add) (None, None, None, 0 ['block3_pool[0][0]', \n",
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" 256) 'batch_normalization_1[0][0]'] \n",
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" \n",
|
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" block4_sepconv1_act (Activatio (None, None, None, 0 ['add_1[0][0]'] \n",
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" n) 256) \n",
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" \n",
|
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" block4_sepconv1 (SeparableConv (None, None, None, 188672 ['block4_sepconv1_act[0][0]'] \n",
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" 2D) 728) \n",
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" \n",
|
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" block4_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block4_sepconv1[0][0]'] \n",
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" lization) 728) \n",
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" \n",
|
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" block4_sepconv2_act (Activatio (None, None, None, 0 ['block4_sepconv1_bn[0][0]'] \n",
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" n) 728) \n",
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" \n",
|
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" block4_sepconv2 (SeparableConv (None, None, None, 536536 ['block4_sepconv2_act[0][0]'] \n",
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" 2D) 728) \n",
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" \n",
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" block4_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block4_sepconv2[0][0]'] \n",
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" lization) 728) \n",
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" \n",
|
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" conv2d_2 (Conv2D) (None, None, None, 186368 ['add_1[0][0]'] \n",
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" 728) \n",
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" \n",
|
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" block4_pool (MaxPooling2D) (None, None, None, 0 ['block4_sepconv2_bn[0][0]'] \n",
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" 728) \n",
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" \n",
|
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" batch_normalization_2 (BatchNo (None, None, None, 2912 ['conv2d_2[0][0]'] \n",
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" rmalization) 728) \n",
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" \n",
|
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" add_2 (Add) (None, None, None, 0 ['block4_pool[0][0]', \n",
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" 728) 'batch_normalization_2[0][0]'] \n",
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" \n",
|
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" block5_sepconv1_act (Activatio (None, None, None, 0 ['add_2[0][0]'] \n",
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" n) 728) \n",
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" \n",
|
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" block5_sepconv1 (SeparableConv (None, None, None, 536536 ['block5_sepconv1_act[0][0]'] \n",
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" 2D) 728) \n",
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" \n",
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" block5_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block5_sepconv1[0][0]'] \n",
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" lization) 728) \n",
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" \n",
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" block5_sepconv2_act (Activatio (None, None, None, 0 ['block5_sepconv1_bn[0][0]'] \n",
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" n) 728) \n",
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" \n",
|
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" block5_sepconv2 (SeparableConv (None, None, None, 536536 ['block5_sepconv2_act[0][0]'] \n",
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" 2D) 728) \n",
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" \n",
|
|
" block5_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block5_sepconv2[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block5_sepconv3_act (Activatio (None, None, None, 0 ['block5_sepconv2_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block5_sepconv3 (SeparableConv (None, None, None, 536536 ['block5_sepconv3_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block5_sepconv3_bn (BatchNorma (None, None, None, 2912 ['block5_sepconv3[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" add_3 (Add) (None, None, None, 0 ['block5_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_2[0][0]'] \n",
|
|
" \n",
|
|
" block6_sepconv1_act (Activatio (None, None, None, 0 ['add_3[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block6_sepconv1 (SeparableConv (None, None, None, 536536 ['block6_sepconv1_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block6_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block6_sepconv1[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block6_sepconv2_act (Activatio (None, None, None, 0 ['block6_sepconv1_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block6_sepconv2 (SeparableConv (None, None, None, 536536 ['block6_sepconv2_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block6_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block6_sepconv2[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block6_sepconv3_act (Activatio (None, None, None, 0 ['block6_sepconv2_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block6_sepconv3 (SeparableConv (None, None, None, 536536 ['block6_sepconv3_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block6_sepconv3_bn (BatchNorma (None, None, None, 2912 ['block6_sepconv3[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" add_4 (Add) (None, None, None, 0 ['block6_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_3[0][0]'] \n",
|
|
" \n",
|
|
" block7_sepconv1_act (Activatio (None, None, None, 0 ['add_4[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block7_sepconv1 (SeparableConv (None, None, None, 536536 ['block7_sepconv1_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block7_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block7_sepconv1[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block7_sepconv2_act (Activatio (None, None, None, 0 ['block7_sepconv1_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block7_sepconv2 (SeparableConv (None, None, None, 536536 ['block7_sepconv2_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block7_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block7_sepconv2[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block7_sepconv3_act (Activatio (None, None, None, 0 ['block7_sepconv2_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block7_sepconv3 (SeparableConv (None, None, None, 536536 ['block7_sepconv3_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block7_sepconv3_bn (BatchNorma (None, None, None, 2912 ['block7_sepconv3[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" add_5 (Add) (None, None, None, 0 ['block7_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_4[0][0]'] \n",
|
|
" \n",
|
|
" block8_sepconv1_act (Activatio (None, None, None, 0 ['add_5[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block8_sepconv1 (SeparableConv (None, None, None, 536536 ['block8_sepconv1_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block8_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block8_sepconv1[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block8_sepconv2_act (Activatio (None, None, None, 0 ['block8_sepconv1_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block8_sepconv2 (SeparableConv (None, None, None, 536536 ['block8_sepconv2_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block8_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block8_sepconv2[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block8_sepconv3_act (Activatio (None, None, None, 0 ['block8_sepconv2_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block8_sepconv3 (SeparableConv (None, None, None, 536536 ['block8_sepconv3_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block8_sepconv3_bn (BatchNorma (None, None, None, 2912 ['block8_sepconv3[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" add_6 (Add) (None, None, None, 0 ['block8_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_5[0][0]'] \n",
|
|
" \n",
|
|
" block9_sepconv1_act (Activatio (None, None, None, 0 ['add_6[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block9_sepconv1 (SeparableConv (None, None, None, 536536 ['block9_sepconv1_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block9_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block9_sepconv1[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block9_sepconv2_act (Activatio (None, None, None, 0 ['block9_sepconv1_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block9_sepconv2 (SeparableConv (None, None, None, 536536 ['block9_sepconv2_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block9_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block9_sepconv2[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block9_sepconv3_act (Activatio (None, None, None, 0 ['block9_sepconv2_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block9_sepconv3 (SeparableConv (None, None, None, 536536 ['block9_sepconv3_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block9_sepconv3_bn (BatchNorma (None, None, None, 2912 ['block9_sepconv3[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" add_7 (Add) (None, None, None, 0 ['block9_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_6[0][0]'] \n",
|
|
" \n",
|
|
" block10_sepconv1_act (Activati (None, None, None, 0 ['add_7[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block10_sepconv1 (SeparableCon (None, None, None, 536536 ['block10_sepconv1_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block10_sepconv1_bn (BatchNorm (None, None, None, 2912 ['block10_sepconv1[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block10_sepconv2_act (Activati (None, None, None, 0 ['block10_sepconv1_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block10_sepconv2 (SeparableCon (None, None, None, 536536 ['block10_sepconv2_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block10_sepconv2_bn (BatchNorm (None, None, None, 2912 ['block10_sepconv2[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block10_sepconv3_act (Activati (None, None, None, 0 ['block10_sepconv2_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block10_sepconv3 (SeparableCon (None, None, None, 536536 ['block10_sepconv3_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block10_sepconv3_bn (BatchNorm (None, None, None, 2912 ['block10_sepconv3[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" add_8 (Add) (None, None, None, 0 ['block10_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_7[0][0]'] \n",
|
|
" \n",
|
|
" block11_sepconv1_act (Activati (None, None, None, 0 ['add_8[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block11_sepconv1 (SeparableCon (None, None, None, 536536 ['block11_sepconv1_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block11_sepconv1_bn (BatchNorm (None, None, None, 2912 ['block11_sepconv1[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block11_sepconv2_act (Activati (None, None, None, 0 ['block11_sepconv1_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block11_sepconv2 (SeparableCon (None, None, None, 536536 ['block11_sepconv2_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block11_sepconv2_bn (BatchNorm (None, None, None, 2912 ['block11_sepconv2[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block11_sepconv3_act (Activati (None, None, None, 0 ['block11_sepconv2_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block11_sepconv3 (SeparableCon (None, None, None, 536536 ['block11_sepconv3_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block11_sepconv3_bn (BatchNorm (None, None, None, 2912 ['block11_sepconv3[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" add_9 (Add) (None, None, None, 0 ['block11_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_8[0][0]'] \n",
|
|
" \n",
|
|
" block12_sepconv1_act (Activati (None, None, None, 0 ['add_9[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block12_sepconv1 (SeparableCon (None, None, None, 536536 ['block12_sepconv1_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block12_sepconv1_bn (BatchNorm (None, None, None, 2912 ['block12_sepconv1[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block12_sepconv2_act (Activati (None, None, None, 0 ['block12_sepconv1_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block12_sepconv2 (SeparableCon (None, None, None, 536536 ['block12_sepconv2_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block12_sepconv2_bn (BatchNorm (None, None, None, 2912 ['block12_sepconv2[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block12_sepconv3_act (Activati (None, None, None, 0 ['block12_sepconv2_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block12_sepconv3 (SeparableCon (None, None, None, 536536 ['block12_sepconv3_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block12_sepconv3_bn (BatchNorm (None, None, None, 2912 ['block12_sepconv3[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" add_10 (Add) (None, None, None, 0 ['block12_sepconv3_bn[0][0]', \n",
|
|
" 728) 'add_9[0][0]'] \n",
|
|
" \n",
|
|
" block13_sepconv1_act (Activati (None, None, None, 0 ['add_10[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block13_sepconv1 (SeparableCon (None, None, None, 536536 ['block13_sepconv1_act[0][0]'] \n",
|
|
" v2D) 728) \n",
|
|
" \n",
|
|
" block13_sepconv1_bn (BatchNorm (None, None, None, 2912 ['block13_sepconv1[0][0]'] \n",
|
|
" alization) 728) \n",
|
|
" \n",
|
|
" block13_sepconv2_act (Activati (None, None, None, 0 ['block13_sepconv1_bn[0][0]'] \n",
|
|
" on) 728) \n",
|
|
" \n",
|
|
" block13_sepconv2 (SeparableCon (None, None, None, 752024 ['block13_sepconv2_act[0][0]'] \n",
|
|
" v2D) 1024) \n",
|
|
" \n",
|
|
" block13_sepconv2_bn (BatchNorm (None, None, None, 4096 ['block13_sepconv2[0][0]'] \n",
|
|
" alization) 1024) \n",
|
|
" \n",
|
|
" conv2d_3 (Conv2D) (None, None, None, 745472 ['add_10[0][0]'] \n",
|
|
" 1024) \n",
|
|
" \n",
|
|
" block13_pool (MaxPooling2D) (None, None, None, 0 ['block13_sepconv2_bn[0][0]'] \n",
|
|
" 1024) \n",
|
|
" \n",
|
|
" batch_normalization_3 (BatchNo (None, None, None, 4096 ['conv2d_3[0][0]'] \n",
|
|
" rmalization) 1024) \n",
|
|
" \n",
|
|
" add_11 (Add) (None, None, None, 0 ['block13_pool[0][0]', \n",
|
|
" 1024) 'batch_normalization_3[0][0]'] \n",
|
|
" \n",
|
|
" block14_sepconv1 (SeparableCon (None, None, None, 1582080 ['add_11[0][0]'] \n",
|
|
" v2D) 1536) \n",
|
|
" \n",
|
|
" block14_sepconv1_bn (BatchNorm (None, None, None, 6144 ['block14_sepconv1[0][0]'] \n",
|
|
" alization) 1536) \n",
|
|
" \n",
|
|
" block14_sepconv1_act (Activati (None, None, None, 0 ['block14_sepconv1_bn[0][0]'] \n",
|
|
" on) 1536) \n",
|
|
" \n",
|
|
" block14_sepconv2 (SeparableCon (None, None, None, 3159552 ['block14_sepconv1_act[0][0]'] \n",
|
|
" v2D) 2048) \n",
|
|
" \n",
|
|
" block14_sepconv2_bn (BatchNorm (None, None, None, 8192 ['block14_sepconv2[0][0]'] \n",
|
|
" alization) 2048) \n",
|
|
" \n",
|
|
" block14_sepconv2_act (Activati (None, None, None, 0 ['block14_sepconv2_bn[0][0]'] \n",
|
|
" on) 2048) \n",
|
|
" \n",
|
|
" global_average_pooling2d (Glob (None, 2048) 0 ['block14_sepconv2_act[0][0]'] \n",
|
|
" alAveragePooling2D) \n",
|
|
" \n",
|
|
" dense (Dense) (None, 7) 14343 ['global_average_pooling2d[0][0]'\n",
|
|
" ] \n",
|
|
" \n",
|
|
"==================================================================================================\n",
|
|
"Total params: 20,875,823\n",
|
|
"Trainable params: 20,821,295\n",
|
|
"Non-trainable params: 54,528\n",
|
|
"__________________________________________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for layer in base_model.layers:\n",
|
|
" layer.trainable = True\n",
|
|
" \n",
|
|
"output = Dense(7, activation='softmax')(base_model.output)\n",
|
|
"model = tf.keras.Model(base_model.input, output)\n",
|
|
"#model = add_regularization(model)\n",
|
|
"model.summary()\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "fd5d1246",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=Adam(learning_rate=.0001), loss='categorical_crossentropy',\n",
|
|
" metrics=['accuracy'])\n",
|
|
"# sparse_categorical_crossentropy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "9cd2ba27",
|
|
"metadata": {
|
|
"scrolled": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/6\n",
|
|
"276/276 [==============================] - 254s 903ms/step - loss: 0.9800 - accuracy: 0.6524 - val_loss: 1.3301 - val_accuracy: 0.5258\n",
|
|
"Epoch 2/6\n",
|
|
"276/276 [==============================] - 246s 891ms/step - loss: 0.4296 - accuracy: 0.8554 - val_loss: 0.8291 - val_accuracy: 0.7175\n",
|
|
"Epoch 3/6\n",
|
|
"276/276 [==============================] - 245s 885ms/step - loss: 0.1091 - accuracy: 0.9716 - val_loss: 0.9532 - val_accuracy: 0.7288\n",
|
|
"Epoch 4/6\n",
|
|
"276/276 [==============================] - 248s 895ms/step - loss: 0.0216 - accuracy: 0.9971 - val_loss: 1.0324 - val_accuracy: 0.7331\n",
|
|
"Epoch 5/6\n",
|
|
"276/276 [==============================] - 249s 900ms/step - loss: 0.0072 - accuracy: 0.9993 - val_loss: 1.1318 - val_accuracy: 0.7295\n",
|
|
"Epoch 6/6\n",
|
|
"276/276 [==============================] - 249s 899ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 1.1304 - val_accuracy: 0.7388\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<keras.callbacks.History at 0x7fd5404d6580>"
|
|
]
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},
|
|
"execution_count": 17,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
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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=6,\n",
|
|
" verbose=1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "63f791af",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
|
|
" layer_config = serialize_layer_fn(layer)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"model.save(\"Model_1.h5\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.10"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|