{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "572dc7fb", "metadata": {}, "outputs": [], "source": [ "from matplotlib import pyplot as plt\n", "from matplotlib.image import imread\n", "import pandas as pd\n", "from collections import Counter\n", "import json\n", "import os\n", "import re\n", "import tempfile\n", "import numpy as np\n", "from os.path import exists\n", "from imblearn.under_sampling import RandomUnderSampler\n", "from PIL import ImageFile\n", "import sklearn as sk\n", "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n", "import tensorflow as tf\n", "import tensorflow.keras\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Activation\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.optimizers import Adam\n", "# custom modules\n", "import image_faults\n", "\n", "ImageFile.LOAD_TRUNCATED_IMAGES = True" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def dict_pics_jup():\n", " '''\n", " {source:target} dict used to replace source urls with image location as input\n", " '''\n", " target_dir = os.getcwd() + os.sep + \"training_images\"\n", " with open('temp_pics_source_list.txt') as f:\n", " temp_pics_source_list = json.load(f)\n", " \n", " dict_pics = {}\n", " for k in temp_pics_source_list:\n", " patt_1 = re.search(r'[^/]+(?=/\\$_|.(\\.jpg|\\.jpeg|\\.png))', k, re.IGNORECASE)\n", " patt_2 = re.search(r'(\\.jpg|\\.jpeg|\\.png)', k, re.IGNORECASE)\n", " if patt_1 and patt_2 is not None:\n", " tag = patt_1.group() + patt_2.group().lower()\n", " file_name = target_dir + os.sep + tag\n", " dict_pics.update({k:file_name})\n", " print(\"{source:target} dictionary created @ \" + target_dir)\n", " return dict_pics" ] }, { "cell_type": "code", "execution_count": 3, "id": "a5c72863", "metadata": {}, "outputs": [], "source": [ "# image_faults.faulty_images() # removes faulty images\n", "df = pd.read_csv('expanded_class.csv', index_col=[0], low_memory=False)" ] }, { "cell_type": "code", "execution_count": 4, "id": "1057a442", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{source:target} dictionary created @ /tf/training_images\n" ] } ], "source": [ "dict_pics = dict_pics_jup()\n", "\n", "with open('women_cat_list.txt') as f:\n", " women_cats = json.load(f)\n", "with open('men_cat_list.txt') as f:\n", " men_cats = json.load(f)\n", " \n", "with open('temp_pics_source_list.txt') as f:\n", " tempics = json.load(f)\n", "# list of image urls that did not get named properly which will be removed from the dataframe\n", "drop_row_vals = []\n", "for pic in tempics:\n", " try:\n", " dict_pics[pic]\n", " except KeyError:\n", " drop_row_vals.append(pic)\n", "\n", "df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n", "ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n", "df = ddf[ddf.PrimaryCategoryID.isin(men_cats)==False] # removes rows of womens categories\n", "\n", "blah = pd.Series(df.PictureURL)\n", "df = df.drop(labels=['PictureURL'], axis=1)\n", "\n", "blah = blah.apply(lambda x: dict_pics[x])\n", "df = pd.concat([blah, df],axis=1)\n", "df = df.groupby('PrimaryCategoryID').filter(lambda x: len(x)>25) # removes cat outliers" ] }, { "cell_type": "code", "execution_count": 5, "id": "7a6146e6", "metadata": {}, "outputs": [], "source": [ "df=df.sample(frac=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "undersample = RandomUnderSampler(sampling_strategy='auto')\n", "train, y_under = undersample.fit_resample(df, df['PrimaryCategoryID'])\n", "#print(Counter(train['PrimaryCategoryID']))" ] }, { "cell_type": "code", "execution_count": 7, "id": "506aa5cf", "metadata": {}, "outputs": [], "source": [ "train, test = train_test_split(train, test_size=0.2, random_state=42)\n", "# stratify=train['PrimaryCategoryID']\n", "# train['PrimaryCategoryID'].value_counts()" ] }, { "cell_type": "code", "execution_count": 8, "id": "4d72eb90", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 4444 validated image filenames belonging to 7 classes.\n", "Found 1111 validated image filenames belonging to 7 classes.\n" ] } ], "source": [ "datagen = ImageDataGenerator(rescale=1./255., \n", " validation_split=.2,\n", " #samplewise_std_normalization=True,\n", " #horizontal_flip= True,\n", " #vertical_flip= True,\n", " #width_shift_range= 0.2,\n", " #height_shift_range= 0.2,\n", " #rotation_range= 90,\n", " preprocessing_function=tf.keras.applications.inception_resnet_v2.preprocess_input)\n", "train_generator=datagen.flow_from_dataframe(\n", " dataframe=train[:len(train)],\n", " directory='./training_images',\n", " x_col='PictureURL',\n", " y_col='PrimaryCategoryID',\n", " batch_size=32,\n", " seed=42,\n", " shuffle=True,\n", " target_size=(299,299),\n", " subset='training'\n", " )\n", "validation_generator=datagen.flow_from_dataframe(\n", " dataframe=train[:len(train)], # is using train right?\n", " directory='./training_images',\n", " x_col='PictureURL',\n", " y_col='PrimaryCategoryID',\n", " batch_size=32,\n", " seed=42,\n", " shuffle=True,\n", " target_size=(299,299),\n", " subset='validation'\n", " )" ] }, { "cell_type": "code", "execution_count": 9, "id": "7b70f37f", "metadata": {}, "outputs": [], "source": [ "imgs, labels = next(train_generator)" ] }, { "cell_type": "code", "execution_count": 10, "id": "1ed54bf5", "metadata": {}, "outputs": [], "source": [ "def plotImages(images_arr):\n", " fig, axes = plt.subplots(1, 10, figsize=(20,20))\n", " axes = axes.flatten()\n", " for img, ax in zip( images_arr, axes):\n", " ax.imshow(img)\n", " ax.axis('off')\n", " plt.tight_layout()\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "id": "85934565", "metadata": {}, "outputs": [], "source": [ "#plotImages(imgs)\n", "#print(labels)" ] }, { "cell_type": "code", "execution_count": 12, "id": "6322bcad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "physical_devices = tf.config.list_physical_devices('GPU')\n", "print(len(physical_devices))\n", "tf.config.experimental.set_memory_growth(physical_devices[0], True)" ] }, { "cell_type": "code", "execution_count": 13, "id": "07fd25c6", "metadata": {}, "outputs": [], "source": [ "# see https://www.kaggle.com/dmitrypukhov/cnn-with-imagedatagenerator-flow-from-dataframe for train/test/val split \n", "# example\n", "\n", "# may need to either create a test dataset from the original dataset or just download a new one" ] }, { "cell_type": "code", "execution_count": 14, "id": "b31af79e", "metadata": {}, "outputs": [], "source": [ "base_model = tf.keras.applications.inception_resnet_v2.InceptionResNetV2(include_top=False, weights='imagenet', pooling='avg')\n", "#base_model.summary()" ] }, { "cell_type": "code", "execution_count": 15, "id": "fe06f2bf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_1 (InputLayer) [(None, None, None, 0 [] \n", " 3)] \n", " \n", " conv2d (Conv2D) (None, None, None, 864 ['input_1[0][0]'] \n", " 32) \n", " \n", " batch_normalization (BatchNorm (None, None, None, 96 ['conv2d[0][0]'] \n", " alization) 32) \n", " \n", " activation (Activation) (None, None, None, 0 ['batch_normalization[0][0]'] \n", " 32) \n", " \n", " conv2d_1 (Conv2D) (None, None, None, 9216 ['activation[0][0]'] \n", " 32) \n", " \n", " batch_normalization_1 (BatchNo (None, None, None, 96 ['conv2d_1[0][0]'] \n", " rmalization) 32) \n", " \n", " activation_1 (Activation) (None, None, None, 0 ['batch_normalization_1[0][0]'] \n", " 32) \n", " \n", " conv2d_2 (Conv2D) (None, None, None, 18432 ['activation_1[0][0]'] \n", " 64) \n", " \n", " batch_normalization_2 (BatchNo (None, None, None, 192 ['conv2d_2[0][0]'] \n", " rmalization) 64) \n", " \n", " activation_2 (Activation) (None, None, None, 0 ['batch_normalization_2[0][0]'] \n", " 64) \n", " \n", " max_pooling2d (MaxPooling2D) (None, None, None, 0 ['activation_2[0][0]'] \n", " 64) \n", " \n", " conv2d_3 (Conv2D) (None, None, None, 5120 ['max_pooling2d[0][0]'] \n", " 80) \n", " \n", " batch_normalization_3 (BatchNo (None, None, None, 240 ['conv2d_3[0][0]'] \n", " rmalization) 80) \n", " \n", " activation_3 (Activation) (None, None, None, 0 ['batch_normalization_3[0][0]'] \n", " 80) \n", " \n", " conv2d_4 (Conv2D) (None, None, None, 138240 ['activation_3[0][0]'] \n", " 192) \n", " \n", " batch_normalization_4 (BatchNo (None, None, None, 576 ['conv2d_4[0][0]'] \n", " rmalization) 192) \n", " \n", " activation_4 (Activation) (None, None, None, 0 ['batch_normalization_4[0][0]'] \n", " 192) \n", " \n", " max_pooling2d_1 (MaxPooling2D) (None, None, None, 0 ['activation_4[0][0]'] \n", " 192) \n", " \n", " conv2d_8 (Conv2D) (None, None, None, 12288 ['max_pooling2d_1[0][0]'] \n", " 64) \n", " \n", " batch_normalization_8 (BatchNo (None, None, None, 192 ['conv2d_8[0][0]'] \n", " rmalization) 64) \n", " \n", " activation_8 (Activation) (None, None, None, 0 ['batch_normalization_8[0][0]'] \n", " 64) \n", " \n", " conv2d_6 (Conv2D) (None, None, None, 9216 ['max_pooling2d_1[0][0]'] \n", " 48) \n", " \n", " conv2d_9 (Conv2D) (None, None, None, 55296 ['activation_8[0][0]'] \n", " 96) \n", " \n", " batch_normalization_6 (BatchNo (None, None, None, 144 ['conv2d_6[0][0]'] \n", " rmalization) 48) \n", " \n", " batch_normalization_9 (BatchNo (None, None, None, 288 ['conv2d_9[0][0]'] \n", " rmalization) 96) \n", " \n", " activation_6 (Activation) (None, None, None, 0 ['batch_normalization_6[0][0]'] \n", " 48) \n", " \n", " activation_9 (Activation) (None, None, None, 0 ['batch_normalization_9[0][0]'] \n", " 96) \n", " \n", " average_pooling2d (AveragePool (None, None, None, 0 ['max_pooling2d_1[0][0]'] \n", " ing2D) 192) \n", " \n", " conv2d_5 (Conv2D) (None, None, None, 18432 ['max_pooling2d_1[0][0]'] \n", " 96) \n", " \n", " conv2d_7 (Conv2D) (None, None, None, 76800 ['activation_6[0][0]'] \n", " 64) \n", " \n", " conv2d_10 (Conv2D) (None, None, None, 82944 ['activation_9[0][0]'] \n", " 96) \n", " \n", " conv2d_11 (Conv2D) (None, None, None, 12288 ['average_pooling2d[0][0]'] \n", " 64) \n", " \n", " batch_normalization_5 (BatchNo (None, None, None, 288 ['conv2d_5[0][0]'] \n", " rmalization) 96) \n", " \n", " batch_normalization_7 (BatchNo (None, None, None, 192 ['conv2d_7[0][0]'] \n", " rmalization) 64) \n", " \n", " batch_normalization_10 (BatchN (None, None, None, 288 ['conv2d_10[0][0]'] \n", " ormalization) 96) \n", " \n", " batch_normalization_11 (BatchN (None, None, None, 192 ['conv2d_11[0][0]'] \n", " ormalization) 64) \n", " \n", " activation_5 (Activation) (None, None, None, 0 ['batch_normalization_5[0][0]'] \n", " 96) \n", " \n", " activation_7 (Activation) (None, None, None, 0 ['batch_normalization_7[0][0]'] \n", " 64) \n", " \n", " activation_10 (Activation) (None, None, None, 0 ['batch_normalization_10[0][0]'] \n", " 96) \n", " \n", " activation_11 (Activation) (None, None, None, 0 ['batch_normalization_11[0][0]'] \n", " 64) \n", " \n", " mixed_5b (Concatenate) (None, None, None, 0 ['activation_5[0][0]', \n", " 320) 'activation_7[0][0]', \n", " 'activation_10[0][0]', \n", " 'activation_11[0][0]'] \n", " \n", " conv2d_15 (Conv2D) (None, None, None, 10240 ['mixed_5b[0][0]'] \n", " 32) \n", " \n", " batch_normalization_15 (BatchN (None, None, None, 96 ['conv2d_15[0][0]'] \n", " ormalization) 32) \n", " \n", " activation_15 (Activation) (None, None, None, 0 ['batch_normalization_15[0][0]'] \n", " 32) \n", " \n", " conv2d_13 (Conv2D) (None, None, None, 10240 ['mixed_5b[0][0]'] \n", " 32) \n", " \n", " conv2d_16 (Conv2D) (None, None, None, 13824 ['activation_15[0][0]'] \n", " 48) \n", " \n", " batch_normalization_13 (BatchN (None, None, None, 96 ['conv2d_13[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_16 (BatchN (None, None, None, 144 ['conv2d_16[0][0]'] \n", " ormalization) 48) \n", " \n", " activation_13 (Activation) (None, None, None, 0 ['batch_normalization_13[0][0]'] \n", " 32) \n", " \n", " activation_16 (Activation) (None, None, None, 0 ['batch_normalization_16[0][0]'] \n", " 48) \n", " \n", " conv2d_12 (Conv2D) (None, None, None, 10240 ['mixed_5b[0][0]'] \n", " 32) \n", " \n", " conv2d_14 (Conv2D) (None, None, None, 9216 ['activation_13[0][0]'] \n", " 32) \n", " \n", " conv2d_17 (Conv2D) (None, None, None, 27648 ['activation_16[0][0]'] \n", " 64) \n", " \n", " batch_normalization_12 (BatchN (None, None, None, 96 ['conv2d_12[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_14 (BatchN (None, None, None, 96 ['conv2d_14[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_17 (BatchN (None, None, None, 192 ['conv2d_17[0][0]'] \n", " ormalization) 64) \n", " \n", " activation_12 (Activation) (None, None, None, 0 ['batch_normalization_12[0][0]'] \n", " 32) \n", " \n", " activation_14 (Activation) (None, None, None, 0 ['batch_normalization_14[0][0]'] \n", " 32) \n", " \n", " activation_17 (Activation) (None, None, None, 0 ['batch_normalization_17[0][0]'] \n", " 64) \n", " \n", " block35_1_mixed (Concatenate) (None, None, None, 0 ['activation_12[0][0]', \n", " 128) 'activation_14[0][0]', \n", " 'activation_17[0][0]'] \n", " \n", " block35_1_conv (Conv2D) (None, None, None, 41280 ['block35_1_mixed[0][0]'] \n", " 320) \n", " \n", " block35_1 (Lambda) (None, None, None, 0 ['mixed_5b[0][0]', \n", " 320) 'block35_1_conv[0][0]'] \n", " \n", " block35_1_ac (Activation) (None, None, None, 0 ['block35_1[0][0]'] \n", " 320) \n", " \n", " conv2d_21 (Conv2D) (None, None, None, 10240 ['block35_1_ac[0][0]'] \n", " 32) \n", " \n", " batch_normalization_21 (BatchN (None, None, None, 96 ['conv2d_21[0][0]'] \n", " ormalization) 32) \n", " \n", " activation_21 (Activation) (None, None, None, 0 ['batch_normalization_21[0][0]'] \n", " 32) \n", " \n", " conv2d_19 (Conv2D) (None, None, None, 10240 ['block35_1_ac[0][0]'] \n", " 32) \n", " \n", " conv2d_22 (Conv2D) (None, None, None, 13824 ['activation_21[0][0]'] \n", " 48) \n", " \n", " batch_normalization_19 (BatchN (None, None, None, 96 ['conv2d_19[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_22 (BatchN (None, None, None, 144 ['conv2d_22[0][0]'] \n", " ormalization) 48) \n", " \n", " activation_19 (Activation) (None, None, None, 0 ['batch_normalization_19[0][0]'] \n", " 32) \n", " \n", " activation_22 (Activation) (None, None, None, 0 ['batch_normalization_22[0][0]'] \n", " 48) \n", " \n", " conv2d_18 (Conv2D) (None, None, None, 10240 ['block35_1_ac[0][0]'] \n", " 32) \n", " \n", " conv2d_20 (Conv2D) (None, None, None, 9216 ['activation_19[0][0]'] \n", " 32) \n", " \n", " conv2d_23 (Conv2D) (None, None, None, 27648 ['activation_22[0][0]'] \n", " 64) \n", " \n", " batch_normalization_18 (BatchN (None, None, None, 96 ['conv2d_18[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_20 (BatchN (None, None, None, 96 ['conv2d_20[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_23 (BatchN (None, None, None, 192 ['conv2d_23[0][0]'] \n", " ormalization) 64) \n", " \n", " activation_18 (Activation) (None, None, None, 0 ['batch_normalization_18[0][0]'] \n", " 32) \n", " \n", " activation_20 (Activation) (None, None, None, 0 ['batch_normalization_20[0][0]'] \n", " 32) \n", " \n", " activation_23 (Activation) (None, None, None, 0 ['batch_normalization_23[0][0]'] \n", " 64) \n", " \n", " block35_2_mixed (Concatenate) (None, None, None, 0 ['activation_18[0][0]', \n", " 128) 'activation_20[0][0]', \n", " 'activation_23[0][0]'] \n", " \n", " block35_2_conv (Conv2D) (None, None, None, 41280 ['block35_2_mixed[0][0]'] \n", " 320) \n", " \n", " block35_2 (Lambda) (None, None, None, 0 ['block35_1_ac[0][0]', \n", " 320) 'block35_2_conv[0][0]'] \n", " \n", " block35_2_ac (Activation) (None, None, None, 0 ['block35_2[0][0]'] \n", " 320) \n", " \n", " conv2d_27 (Conv2D) (None, None, None, 10240 ['block35_2_ac[0][0]'] \n", " 32) \n", " \n", " batch_normalization_27 (BatchN (None, None, None, 96 ['conv2d_27[0][0]'] \n", " ormalization) 32) \n", " \n", " activation_27 (Activation) (None, None, None, 0 ['batch_normalization_27[0][0]'] \n", " 32) \n", " \n", " conv2d_25 (Conv2D) (None, None, None, 10240 ['block35_2_ac[0][0]'] \n", " 32) \n", " \n", " conv2d_28 (Conv2D) (None, None, None, 13824 ['activation_27[0][0]'] \n", " 48) \n", " \n", " batch_normalization_25 (BatchN (None, None, None, 96 ['conv2d_25[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_28 (BatchN (None, None, None, 144 ['conv2d_28[0][0]'] \n", " ormalization) 48) \n", " \n", " activation_25 (Activation) (None, None, None, 0 ['batch_normalization_25[0][0]'] \n", " 32) \n", " \n", " activation_28 (Activation) (None, None, None, 0 ['batch_normalization_28[0][0]'] \n", " 48) \n", " \n", " conv2d_24 (Conv2D) (None, None, None, 10240 ['block35_2_ac[0][0]'] \n", " 32) \n", " \n", " conv2d_26 (Conv2D) (None, None, None, 9216 ['activation_25[0][0]'] \n", " 32) \n", " \n", " conv2d_29 (Conv2D) (None, None, None, 27648 ['activation_28[0][0]'] \n", " 64) \n", " \n", " batch_normalization_24 (BatchN (None, None, None, 96 ['conv2d_24[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_26 (BatchN (None, None, None, 96 ['conv2d_26[0][0]'] \n", " ormalization) 32) \n", " \n", " batch_normalization_29 (BatchN (None, None, 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ormalization) 192) \n", " \n", " batch_normalization_87 (BatchN (None, None, None, 576 ['conv2d_87[0][0]'] \n", " ormalization) 192) \n", " \n", " activation_84 (Activation) (None, None, None, 0 ['batch_normalization_84[0][0]'] \n", " 192) \n", " \n", " activation_87 (Activation) (None, None, None, 0 ['batch_normalization_87[0][0]'] \n", " 192) \n", " \n", " block17_3_mixed (Concatenate) (None, None, None, 0 ['activation_84[0][0]', \n", " 384) 'activation_87[0][0]'] \n", " \n", " block17_3_conv (Conv2D) (None, None, None, 418880 ['block17_3_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_3 (Lambda) (None, None, None, 0 ['block17_2_ac[0][0]', \n", " 1088) 'block17_3_conv[0][0]'] \n", " \n", " block17_3_ac (Activation) (None, None, None, 0 ['block17_3[0][0]'] \n", " 1088) \n", " \n", " conv2d_89 (Conv2D) (None, None, None, 139264 ['block17_3_ac[0][0]'] \n", " 128) \n", " \n", " batch_normalization_89 (BatchN (None, None, None, 384 ['conv2d_89[0][0]'] \n", " ormalization) 128) \n", " 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(Lambda) (None, None, None, 0 ['block17_4_ac[0][0]', \n", " 1088) 'block17_5_conv[0][0]'] \n", " \n", " block17_5_ac (Activation) (None, None, None, 0 ['block17_5[0][0]'] \n", " 1088) \n", " \n", " conv2d_97 (Conv2D) (None, None, None, 139264 ['block17_5_ac[0][0]'] \n", " 128) \n", " \n", " batch_normalization_97 (BatchN (None, None, None, 384 ['conv2d_97[0][0]'] \n", " ormalization) 128) \n", " \n", " activation_97 (Activation) (None, None, None, 0 ['batch_normalization_97[0][0]'] \n", " 128) \n", " \n", " conv2d_98 (Conv2D) (None, None, None, 143360 ['activation_97[0][0]'] \n", " 160) \n", " \n", " batch_normalization_98 (BatchN (None, None, None, 480 ['conv2d_98[0][0]'] \n", " ormalization) 160) \n", " \n", " activation_98 (Activation) (None, None, None, 0 ['batch_normalization_98[0][0]'] \n", " 160) \n", " \n", " conv2d_96 (Conv2D) (None, None, None, 208896 ['block17_5_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_99 (Conv2D) (None, None, None, 215040 ['activation_98[0][0]'] \n", " 192) \n", " \n", " batch_normalization_96 (BatchN (None, None, None, 576 ['conv2d_96[0][0]'] \n", " ormalization) 192) \n", " \n", " batch_normalization_99 (BatchN (None, None, None, 576 ['conv2d_99[0][0]'] \n", " ormalization) 192) \n", " \n", " activation_96 (Activation) (None, None, None, 0 ['batch_normalization_96[0][0]'] \n", " 192) \n", " \n", " activation_99 (Activation) (None, None, None, 0 ['batch_normalization_99[0][0]'] \n", " 192) \n", " \n", " block17_6_mixed (Concatenate) (None, None, None, 0 ['activation_96[0][0]', \n", " 384) 'activation_99[0][0]'] \n", " \n", " block17_6_conv (Conv2D) (None, None, None, 418880 ['block17_6_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_6 (Lambda) (None, None, None, 0 ['block17_5_ac[0][0]', \n", " 1088) 'block17_6_conv[0][0]'] \n", " \n", " block17_6_ac (Activation) (None, None, None, 0 ['block17_6[0][0]'] \n", " 1088) \n", " \n", " conv2d_101 (Conv2D) (None, None, None, 139264 ['block17_6_ac[0][0]'] \n", " 128) \n", " \n", " 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['activation_105[0][0]'] \n", " 160) \n", " \n", " batch_normalization_106 (Batch (None, None, None, 480 ['conv2d_106[0][0]'] \n", " Normalization) 160) \n", " \n", " activation_106 (Activation) (None, None, None, 0 ['batch_normalization_106[0][0]']\n", " 160) \n", " \n", " conv2d_104 (Conv2D) (None, None, None, 208896 ['block17_7_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_107 (Conv2D) (None, None, None, 215040 ['activation_106[0][0]'] \n", " 192) \n", " \n", " batch_normalization_104 (Batch (None, None, None, 576 ['conv2d_104[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_107 (Batch (None, None, None, 576 ['conv2d_107[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_104 (Activation) (None, None, None, 0 ['batch_normalization_104[0][0]']\n", " 192) \n", " \n", " activation_107 (Activation) (None, None, None, 0 ['batch_normalization_107[0][0]']\n", " 192) \n", " \n", " block17_8_mixed (Concatenate) (None, None, None, 0 ['activation_104[0][0]', \n", " 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Normalization) 192) \n", " \n", " batch_normalization_115 (Batch (None, None, None, 576 ['conv2d_115[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_112 (Activation) (None, None, None, 0 ['batch_normalization_112[0][0]']\n", " 192) \n", " \n", " activation_115 (Activation) (None, None, None, 0 ['batch_normalization_115[0][0]']\n", " 192) \n", " \n", " block17_10_mixed (Concatenate) (None, None, None, 0 ['activation_112[0][0]', \n", " 384) 'activation_115[0][0]'] \n", " \n", " block17_10_conv (Conv2D) (None, None, None, 418880 ['block17_10_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_10 (Lambda) (None, None, None, 0 ['block17_9_ac[0][0]', \n", " 1088) 'block17_10_conv[0][0]'] \n", " \n", " block17_10_ac (Activation) (None, None, None, 0 ['block17_10[0][0]'] \n", " 1088) \n", " \n", " conv2d_117 (Conv2D) (None, None, None, 139264 ['block17_10_ac[0][0]'] \n", " 128) \n", " \n", " batch_normalization_117 (Batch (None, None, None, 384 ['conv2d_117[0][0]'] \n", " Normalization) 128) \n", " \n", " activation_117 (Activation) (None, None, None, 0 ['batch_normalization_117[0][0]']\n", " 128) \n", " \n", " conv2d_118 (Conv2D) (None, None, None, 143360 ['activation_117[0][0]'] \n", " 160) \n", " \n", " batch_normalization_118 (Batch (None, None, None, 480 ['conv2d_118[0][0]'] \n", " Normalization) 160) \n", " \n", " activation_118 (Activation) (None, None, None, 0 ['batch_normalization_118[0][0]']\n", " 160) \n", " \n", " conv2d_116 (Conv2D) (None, None, None, 208896 ['block17_10_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_119 (Conv2D) (None, None, None, 215040 ['activation_118[0][0]'] \n", " 192) \n", " \n", " batch_normalization_116 (Batch (None, None, None, 576 ['conv2d_116[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_119 (Batch (None, None, None, 576 ['conv2d_119[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_116 (Activation) (None, None, None, 0 ['batch_normalization_116[0][0]']\n", " 192) \n", " \n", " activation_119 (Activation) (None, None, None, 0 ['batch_normalization_119[0][0]']\n", " 192) \n", " \n", " block17_11_mixed (Concatenate) (None, None, None, 0 ['activation_116[0][0]', \n", " 384) 'activation_119[0][0]'] \n", " \n", " block17_11_conv (Conv2D) (None, None, None, 418880 ['block17_11_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_11 (Lambda) (None, None, None, 0 ['block17_10_ac[0][0]', \n", " 1088) 'block17_11_conv[0][0]'] \n", " \n", " block17_11_ac (Activation) (None, None, None, 0 ['block17_11[0][0]'] \n", " 1088) \n", " \n", " conv2d_121 (Conv2D) (None, None, None, 139264 ['block17_11_ac[0][0]'] \n", " 128) \n", " \n", " batch_normalization_121 (Batch (None, None, None, 384 ['conv2d_121[0][0]'] \n", " Normalization) 128) \n", " \n", " activation_121 (Activation) (None, None, None, 0 ['batch_normalization_121[0][0]']\n", " 128) \n", " \n", " conv2d_122 (Conv2D) (None, None, None, 143360 ['activation_121[0][0]'] \n", " 160) \n", " \n", " batch_normalization_122 (Batch 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192) \n", " \n", " conv2d_127 (Conv2D) (None, None, None, 215040 ['activation_126[0][0]'] \n", " 192) \n", " \n", " batch_normalization_124 (Batch (None, None, None, 576 ['conv2d_124[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_127 (Batch (None, None, None, 576 ['conv2d_127[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_124 (Activation) (None, None, None, 0 ['batch_normalization_124[0][0]']\n", " 192) \n", " \n", " activation_127 (Activation) (None, None, None, 0 ['batch_normalization_127[0][0]']\n", " 192) \n", " \n", " block17_13_mixed (Concatenate) (None, None, None, 0 ['activation_124[0][0]', \n", " 384) 'activation_127[0][0]'] \n", " \n", " block17_13_conv (Conv2D) (None, None, None, 418880 ['block17_13_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_13 (Lambda) (None, None, None, 0 ['block17_12_ac[0][0]', \n", " 1088) 'block17_13_conv[0][0]'] \n", " \n", " block17_13_ac (Activation) (None, None, None, 0 ['block17_13[0][0]'] \n", " 1088) \n", 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activation_138 (Activation) (None, None, None, 0 ['batch_normalization_138[0][0]']\n", " 160) \n", " \n", " conv2d_136 (Conv2D) (None, None, None, 208896 ['block17_15_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_139 (Conv2D) (None, None, None, 215040 ['activation_138[0][0]'] \n", " 192) \n", " \n", " batch_normalization_136 (Batch (None, None, None, 576 ['conv2d_136[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_139 (Batch (None, None, None, 576 ['conv2d_139[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_136 (Activation) (None, None, None, 0 ['batch_normalization_136[0][0]']\n", " 192) \n", " \n", " activation_139 (Activation) (None, None, None, 0 ['batch_normalization_139[0][0]']\n", " 192) \n", " \n", " block17_16_mixed (Concatenate) (None, None, None, 0 ['activation_136[0][0]', \n", " 384) 'activation_139[0][0]'] \n", " \n", " block17_16_conv (Conv2D) (None, None, None, 418880 ['block17_16_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_16 (Lambda) 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\n", " 192) \n", " \n", " batch_normalization_140 (Batch (None, None, None, 576 ['conv2d_140[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_143 (Batch (None, None, None, 576 ['conv2d_143[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_140 (Activation) (None, None, None, 0 ['batch_normalization_140[0][0]']\n", " 192) \n", " \n", " activation_143 (Activation) (None, None, None, 0 ['batch_normalization_143[0][0]']\n", " 192) \n", " \n", " block17_17_mixed (Concatenate) (None, None, None, 0 ['activation_140[0][0]', \n", " 384) 'activation_143[0][0]'] \n", " \n", " block17_17_conv (Conv2D) (None, None, None, 418880 ['block17_17_mixed[0][0]'] \n", " 1088) \n", " \n", " block17_17 (Lambda) (None, None, None, 0 ['block17_16_ac[0][0]', \n", " 1088) 'block17_17_conv[0][0]'] \n", " \n", " block17_17_ac (Activation) (None, None, None, 0 ['block17_17[0][0]'] \n", " 1088) \n", " \n", " conv2d_145 (Conv2D) (None, None, None, 139264 ['block17_17_ac[0][0]'] \n", " 128) \n", " \n", " batch_normalization_145 (Batch (None, None, None, 384 ['conv2d_145[0][0]'] \n", " Normalization) 128) \n", " \n", " activation_145 (Activation) (None, None, None, 0 ['batch_normalization_145[0][0]']\n", " 128) \n", " \n", " conv2d_146 (Conv2D) (None, None, None, 143360 ['activation_145[0][0]'] \n", " 160) \n", " \n", " batch_normalization_146 (Batch (None, None, None, 480 ['conv2d_146[0][0]'] \n", " Normalization) 160) \n", " \n", " activation_146 (Activation) (None, None, None, 0 ['batch_normalization_146[0][0]']\n", " 160) \n", " \n", " conv2d_144 (Conv2D) (None, None, None, 208896 ['block17_17_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_147 (Conv2D) (None, None, None, 215040 ['activation_146[0][0]'] \n", " 192) \n", " \n", " batch_normalization_144 (Batch (None, None, None, 576 ['conv2d_144[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_147 (Batch (None, None, None, 576 ['conv2d_147[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_144 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conv2d_186 (Conv2D) (None, None, None, 172032 ['activation_185[0][0]'] \n", " 256) \n", " \n", " batch_normalization_183 (Batch (None, None, None, 576 ['conv2d_183[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_186 (Batch (None, None, None, 768 ['conv2d_186[0][0]'] \n", " Normalization) 256) \n", " \n", " activation_183 (Activation) (None, None, None, 0 ['batch_normalization_183[0][0]']\n", " 192) \n", " \n", " activation_186 (Activation) (None, None, None, 0 ['batch_normalization_186[0][0]']\n", " 256) \n", " \n", " block8_6_mixed (Concatenate) (None, None, None, 0 ['activation_183[0][0]', \n", " 448) 'activation_186[0][0]'] \n", " \n", " block8_6_conv (Conv2D) (None, None, None, 933920 ['block8_6_mixed[0][0]'] \n", " 2080) \n", " \n", " block8_6 (Lambda) (None, None, None, 0 ['block8_5_ac[0][0]', \n", " 2080) 'block8_6_conv[0][0]'] \n", " \n", " block8_6_ac (Activation) (None, None, None, 0 ['block8_6[0][0]'] \n", " 2080) \n", " \n", " conv2d_188 (Conv2D) (None, None, None, 399360 ['block8_6_ac[0][0]'] \n", " 192) \n", " \n", " batch_normalization_188 (Batch (None, None, None, 576 ['conv2d_188[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_188 (Activation) (None, None, None, 0 ['batch_normalization_188[0][0]']\n", " 192) \n", " \n", " conv2d_189 (Conv2D) (None, None, None, 129024 ['activation_188[0][0]'] \n", " 224) \n", " \n", " batch_normalization_189 (Batch (None, None, None, 672 ['conv2d_189[0][0]'] \n", " Normalization) 224) \n", " \n", " activation_189 (Activation) (None, None, None, 0 ['batch_normalization_189[0][0]']\n", " 224) \n", " \n", " conv2d_187 (Conv2D) (None, None, None, 399360 ['block8_6_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_190 (Conv2D) (None, None, None, 172032 ['activation_189[0][0]'] \n", " 256) \n", " \n", " batch_normalization_187 (Batch (None, None, None, 576 ['conv2d_187[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_190 (Batch (None, None, None, 768 ['conv2d_190[0][0]'] \n", " Normalization) 256) \n", " \n", " activation_187 (Activation) (None, None, None, 0 ['batch_normalization_187[0][0]']\n", " 192) \n", " \n", " activation_190 (Activation) (None, None, None, 0 ['batch_normalization_190[0][0]']\n", " 256) \n", " \n", " block8_7_mixed (Concatenate) (None, None, None, 0 ['activation_187[0][0]', \n", " 448) 'activation_190[0][0]'] \n", " \n", " block8_7_conv (Conv2D) (None, None, None, 933920 ['block8_7_mixed[0][0]'] \n", " 2080) \n", " \n", " block8_7 (Lambda) (None, None, None, 0 ['block8_6_ac[0][0]', \n", " 2080) 'block8_7_conv[0][0]'] \n", " \n", " block8_7_ac (Activation) (None, None, None, 0 ['block8_7[0][0]'] \n", " 2080) \n", " \n", " conv2d_192 (Conv2D) (None, None, None, 399360 ['block8_7_ac[0][0]'] \n", " 192) \n", " \n", " batch_normalization_192 (Batch (None, None, None, 576 ['conv2d_192[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_192 (Activation) (None, None, None, 0 ['batch_normalization_192[0][0]']\n", " 192) \n", " \n", " conv2d_193 (Conv2D) (None, None, None, 129024 ['activation_192[0][0]'] \n", " 224) \n", " \n", " batch_normalization_193 (Batch (None, None, None, 672 ['conv2d_193[0][0]'] \n", " Normalization) 224) \n", " \n", " activation_193 (Activation) (None, None, None, 0 ['batch_normalization_193[0][0]']\n", " 224) \n", " \n", " conv2d_191 (Conv2D) (None, None, None, 399360 ['block8_7_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_194 (Conv2D) (None, None, None, 172032 ['activation_193[0][0]'] \n", " 256) \n", " \n", " batch_normalization_191 (Batch (None, None, None, 576 ['conv2d_191[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_194 (Batch (None, None, None, 768 ['conv2d_194[0][0]'] \n", " Normalization) 256) \n", " \n", " activation_191 (Activation) (None, None, None, 0 ['batch_normalization_191[0][0]']\n", " 192) \n", " \n", " activation_194 (Activation) (None, None, None, 0 ['batch_normalization_194[0][0]']\n", " 256) \n", " \n", " block8_8_mixed (Concatenate) (None, None, None, 0 ['activation_191[0][0]', \n", " 448) 'activation_194[0][0]'] \n", " \n", " block8_8_conv (Conv2D) (None, None, None, 933920 ['block8_8_mixed[0][0]'] \n", " 2080) \n", " \n", " block8_8 (Lambda) (None, None, None, 0 ['block8_7_ac[0][0]', \n", " 2080) 'block8_8_conv[0][0]'] \n", " \n", " block8_8_ac (Activation) (None, None, None, 0 ['block8_8[0][0]'] \n", " 2080) \n", " \n", " conv2d_196 (Conv2D) (None, None, None, 399360 ['block8_8_ac[0][0]'] \n", " 192) \n", " \n", " batch_normalization_196 (Batch (None, None, None, 576 ['conv2d_196[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_196 (Activation) (None, None, None, 0 ['batch_normalization_196[0][0]']\n", " 192) \n", " \n", " conv2d_197 (Conv2D) (None, None, None, 129024 ['activation_196[0][0]'] \n", " 224) \n", " \n", " batch_normalization_197 (Batch (None, None, None, 672 ['conv2d_197[0][0]'] \n", " Normalization) 224) \n", " \n", " activation_197 (Activation) (None, None, None, 0 ['batch_normalization_197[0][0]']\n", " 224) \n", " \n", " conv2d_195 (Conv2D) (None, None, None, 399360 ['block8_8_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_198 (Conv2D) (None, None, None, 172032 ['activation_197[0][0]'] \n", " 256) \n", " \n", " batch_normalization_195 (Batch (None, None, None, 576 ['conv2d_195[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_198 (Batch (None, None, None, 768 ['conv2d_198[0][0]'] \n", " Normalization) 256) \n", " \n", " activation_195 (Activation) (None, None, None, 0 ['batch_normalization_195[0][0]']\n", " 192) \n", " \n", " activation_198 (Activation) (None, None, None, 0 ['batch_normalization_198[0][0]']\n", " 256) \n", " \n", " block8_9_mixed (Concatenate) (None, None, None, 0 ['activation_195[0][0]', \n", " 448) 'activation_198[0][0]'] \n", " \n", " block8_9_conv (Conv2D) (None, None, None, 933920 ['block8_9_mixed[0][0]'] \n", " 2080) \n", " \n", " block8_9 (Lambda) (None, None, None, 0 ['block8_8_ac[0][0]', \n", " 2080) 'block8_9_conv[0][0]'] \n", " \n", " block8_9_ac (Activation) (None, None, None, 0 ['block8_9[0][0]'] \n", " 2080) \n", " \n", " conv2d_200 (Conv2D) (None, None, None, 399360 ['block8_9_ac[0][0]'] \n", " 192) \n", " \n", " batch_normalization_200 (Batch (None, None, None, 576 ['conv2d_200[0][0]'] \n", " Normalization) 192) \n", " \n", " activation_200 (Activation) (None, None, None, 0 ['batch_normalization_200[0][0]']\n", " 192) \n", " \n", " conv2d_201 (Conv2D) (None, None, None, 129024 ['activation_200[0][0]'] \n", " 224) \n", " \n", " batch_normalization_201 (Batch (None, None, None, 672 ['conv2d_201[0][0]'] \n", " Normalization) 224) \n", " \n", " activation_201 (Activation) (None, None, None, 0 ['batch_normalization_201[0][0]']\n", " 224) \n", " \n", " conv2d_199 (Conv2D) (None, None, None, 399360 ['block8_9_ac[0][0]'] \n", " 192) \n", " \n", " conv2d_202 (Conv2D) (None, None, None, 172032 ['activation_201[0][0]'] \n", " 256) \n", " \n", " batch_normalization_199 (Batch (None, None, None, 576 ['conv2d_199[0][0]'] \n", " Normalization) 192) \n", " \n", " batch_normalization_202 (Batch (None, None, None, 768 ['conv2d_202[0][0]'] \n", " Normalization) 256) \n", " \n", " activation_199 (Activation) (None, None, None, 0 ['batch_normalization_199[0][0]']\n", " 192) \n", " \n", " activation_202 (Activation) (None, None, None, 0 ['batch_normalization_202[0][0]']\n", " 256) \n", " \n", " block8_10_mixed (Concatenate) (None, None, None, 0 ['activation_199[0][0]', \n", " 448) 'activation_202[0][0]'] \n", " \n", " block8_10_conv (Conv2D) (None, None, None, 933920 ['block8_10_mixed[0][0]'] \n", " 2080) \n", " \n", " block8_10 (Lambda) (None, None, None, 0 ['block8_9_ac[0][0]', \n", " 2080) 'block8_10_conv[0][0]'] \n", " \n", " conv_7b (Conv2D) (None, None, None, 3194880 ['block8_10[0][0]'] \n", " 1536) \n", " \n", " conv_7b_bn (BatchNormalization (None, None, None, 4608 ['conv_7b[0][0]'] \n", " ) 1536) \n", " \n", " conv_7b_ac (Activation) (None, None, None, 0 ['conv_7b_bn[0][0]'] \n", " 1536) \n", " \n", " global_average_pooling2d (Glob (None, 1536) 0 ['conv_7b_ac[0][0]'] \n", " alAveragePooling2D) \n", " \n", " dense (Dense) (None, 7) 10759 ['global_average_pooling2d[0][0]'\n", " ] \n", " \n", "==================================================================================================\n", "Total params: 54,347,495\n", "Trainable params: 54,286,951\n", "Non-trainable params: 60,544\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": "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": 17, "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": null, "id": "9cd2ba27", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/30\n", "139/139 [==============================] - 65s 390ms/step - loss: 1.0030 - accuracy: 0.6427 - val_loss: 2.1331 - val_accuracy: 0.1620\n", "Epoch 2/30\n", "139/139 [==============================] - 51s 365ms/step - loss: 0.3751 - accuracy: 0.8756 - val_loss: 1.7818 - val_accuracy: 0.4707\n", "Epoch 3/30\n", "139/139 [==============================] - 50s 359ms/step - loss: 0.1193 - accuracy: 0.9622 - val_loss: 1.5616 - val_accuracy: 0.6166\n", "Epoch 4/30\n", "139/139 [==============================] - 51s 363ms/step - loss: 0.0830 - accuracy: 0.9750 - val_loss: 1.3374 - val_accuracy: 0.6922\n", "Epoch 5/30\n", "139/139 [==============================] - 50s 360ms/step - loss: 0.0609 - accuracy: 0.9815 - val_loss: 1.3717 - val_accuracy: 0.6976\n", "Epoch 6/30\n", "139/139 [==============================] - 51s 366ms/step - loss: 0.0593 - accuracy: 0.9818 - val_loss: 1.2600 - val_accuracy: 0.7192\n", "Epoch 7/30\n", "139/139 [==============================] - 51s 363ms/step - loss: 0.0468 - accuracy: 0.9860 - val_loss: 1.1707 - val_accuracy: 0.7264\n", "Epoch 8/30\n", " 89/139 [==================>...........] - ETA: 14s - loss: 0.0309 - accuracy: 0.9905" ] } ], "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=30,\n", " verbose=1)" ] }, { "cell_type": "code", "execution_count": null, "id": "63f791af", "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 }