878 lines
75 KiB
Plaintext
878 lines
75 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": "8d94196d",
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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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"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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" 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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" 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": 5,
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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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"dict_pics = dict_pics_jup()\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 = df[df.PictureURL.isin(drop_row_vals)==False]\n",
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"# TODO drop men's or women's categories here\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": 6,
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"id": "7a6146e6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"17"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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)\n",
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"len(drop_row_vals)"
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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": "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": 8,
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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": 9,
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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": "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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Found 53005 validated image filenames belonging to 13 classes.\n",
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"Found 13251 validated image filenames belonging to 13 classes.\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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"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": 10,
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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": 11,
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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": 12,
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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": 13,
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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": 14,
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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": 15,
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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": 16,
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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",
|
|
" batch_normalization_2 (BatchNo (None, None, None, 2912 ['conv2d_2[0][0]'] \n",
|
|
" rmalization) 728) \n",
|
|
" \n",
|
|
" add_2 (Add) (None, None, None, 0 ['block4_pool[0][0]', \n",
|
|
" 728) 'batch_normalization_2[0][0]'] \n",
|
|
" \n",
|
|
" block5_sepconv1_act (Activatio (None, None, None, 0 ['add_2[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block5_sepconv1 (SeparableConv (None, None, None, 536536 ['block5_sepconv1_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \n",
|
|
" block5_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block5_sepconv1[0][0]'] \n",
|
|
" lization) 728) \n",
|
|
" \n",
|
|
" block5_sepconv2_act (Activatio (None, None, None, 0 ['block5_sepconv1_bn[0][0]'] \n",
|
|
" n) 728) \n",
|
|
" \n",
|
|
" block5_sepconv2 (SeparableConv (None, None, None, 536536 ['block5_sepconv2_act[0][0]'] \n",
|
|
" 2D) 728) \n",
|
|
" \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, 13) 26637 ['global_average_pooling2d[0][0]'\n",
|
|
" ] \n",
|
|
" \n",
|
|
"==================================================================================================\n",
|
|
"Total params: 20,888,117\n",
|
|
"Trainable params: 20,833,589\n",
|
|
"Non-trainable params: 54,528\n",
|
|
"__________________________________________________________________________________________________\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"for layer in base_model.layers:\n",
|
|
" layer.trainable = True\n",
|
|
" \n",
|
|
"output = Dense(13, 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": 17,
|
|
"id": "ea620129",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#model.add(Dropout(.5))\n",
|
|
"#model.add(Dense(64, activation='softmax'))\n",
|
|
"# model.add(Dropout(.25))\n",
|
|
"#model = add_regularization(model)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "fd5d1246",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"model.compile(optimizer=Adam(learning_rate=.0001), loss='categorical_crossentropy',\n",
|
|
" metrics=['accuracy'])\n",
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"# sparse_categorical_crossentropy"
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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": 19,
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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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"829/829 [==============================] - 786s 942ms/step - loss: 1.5037 - accuracy: 0.4896 - val_loss: 1.2946 - val_accuracy: 0.5520\n",
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"Epoch 2/30\n",
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"829/829 [==============================] - 726s 875ms/step - loss: 0.8550 - accuracy: 0.7117 - val_loss: 1.3593 - val_accuracy: 0.5593\n",
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"Epoch 3/30\n",
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"829/829 [==============================] - 750s 905ms/step - loss: 0.3322 - accuracy: 0.8993 - val_loss: 1.5304 - val_accuracy: 0.5542\n",
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"Epoch 4/30\n",
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"172/829 [=====>........................] - ETA: 7:57 - loss: 0.1030 - accuracy: 0.9787"
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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-19-4cd4443bbf2a>\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 1219\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp_logs\u001b[0m \u001b[0;31m# No error, now safe to assign to logs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0mend_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_increment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1221\u001b[0;31m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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 1222\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1223\u001b[0m \u001b[0;32mbreak\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/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 434\u001b[0m \"\"\"\n\u001b[1;32m 435\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_call_train_batch_hooks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlogs\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 437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 438\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_begin_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'end'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_end_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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 296\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m raise ValueError(\n",
|
|
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 316\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_batch_hook_helper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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 317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_times\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_batches_for_timing_check\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 354\u001b[0;31m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timing\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/callbacks.py\u001b[0m in \u001b[0;36mon_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_train_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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-> 1032\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_batch_update_progbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\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 1033\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mon_test_batch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/keras/callbacks.py\u001b[0m in \u001b[0;36m_batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m 1102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\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[0m\n\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Only block async when verbose = 1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1104\u001b[0;31m \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msync_to_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\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 1105\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1106\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/tf_utils.py\u001b[0m in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 554\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_structure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensors\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 555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 556\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/nest.py\u001b[0m in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 868\u001b[0m return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 870\u001b[0m expand_composites=expand_composites)\n\u001b[1;32m 871\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/nest.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 868\u001b[0m return pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0;31m \u001b[0mstructure\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mentries\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 870\u001b[0m expand_composites=expand_composites)\n\u001b[1;32m 871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/tf_utils.py\u001b[0m in \u001b[0;36m_to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m 548\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_to_single_numpy_or_python_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\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 549\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\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--> 550\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 551\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mt\u001b[0m \u001b[0;31m# Don't turn ragged or sparse tensors to NumPy.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1147\u001b[0m \"\"\"\n\u001b[1;32m 1148\u001b[0m \u001b[0;31m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m \u001b[0mmaybe_arr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1150\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmaybe_arr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mmaybe_arr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1151\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/framework/ops.py\u001b[0m in \u001b[0;36m_numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_numpy\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 1114\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-> 1115\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numpy_internal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1116\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;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1117\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_to_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# pylint: disable=protected-access\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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],
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"source": [
|
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"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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{
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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",
|
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
|
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"name": "ipython",
|
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
|