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{
"cells": [
{
"cell_type": "code",
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"execution_count": 1,
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"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",
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"execution_count": 2,
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"id": "8d94196d",
"metadata": {},
"outputs": [],
"source": [
"def add_regularization(model, regularizer=tf.keras.regularizers.l2(0.0001)):\n",
"\n",
" if not isinstance(regularizer, tf.keras.regularizers.Regularizer):\n",
" print(\"Regularizer must be a subclass of tf.keras.regularizers.Regularizer\")\n",
" return model\n",
"\n",
" for layer in model.layers:\n",
" for attr in ['kernel_regularizer']:\n",
" if hasattr(layer, attr):\n",
" setattr(layer, attr, regularizer)\n",
"\n",
" # When we change the layers attributes, the change only happens in the model config file\n",
" model_json = model.to_json()\n",
"\n",
" # Save the weights before reloading the model.\n",
" tmp_weights_path = os.path.join(tempfile.gettempdir(), 'tmp_weights.h5')\n",
" model.save_weights(tmp_weights_path)\n",
"\n",
" # load the model from the config\n",
" model = tf.keras.models.model_from_json(model_json)\n",
" \n",
" # Reload the model weights\n",
" model.load_weights(tmp_weights_path, by_name=True)\n",
" return model"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"id": "a5c72863",
"metadata": {},
"outputs": [],
"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)"
]
},
{
"cell_type": "code",
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"execution_count": 4,
"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\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"id": "1057a442",
"metadata": {
"scrolled": true
},
"outputs": [
{
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"name": "stdout",
"output_type": "stream",
"text": [
"{source:target} dictionary created @ /tf/training_images\n"
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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",
" 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 = df[df.PictureURL.isin(drop_row_vals)==False]\n",
"# TODO drop men's or women's categories here\n",
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"blah = pd.Series(df.PictureURL)\n",
"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",
"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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]
},
{
"cell_type": "code",
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"execution_count": 6,
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"id": "7a6146e6",
"metadata": {},
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"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
"\n",
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"df=df.sample(frac=1)\n",
"len(drop_row_vals)"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"id": "114cc3c0",
"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",
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"execution_count": 8,
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"id": "506aa5cf",
"metadata": {},
"outputs": [],
"source": [
"train, test = train_test_split(train, test_size=0.1, random_state=42)\n",
"# stratify=train['PrimaryCategoryID']\n",
"# train['PrimaryCategoryID'].value_counts()"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"id": "4d72eb90",
"metadata": {},
"outputs": [
{
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"name": "stderr",
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"output_type": "stream",
"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",
" warnings.warn(\n"
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]
},
{
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"name": "stdout",
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"output_type": "stream",
"text": [
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"Found 53005 validated image filenames belonging to 13 classes.\n",
"Found 13251 validated image filenames belonging to 13 classes.\n"
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]
}
],
"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.xception.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=64,\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=64,\n",
" seed=42,\n",
" shuffle=True,\n",
" target_size=(299,299),\n",
" subset='validation'\n",
" )"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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"id": "7b70f37f",
"metadata": {},
"outputs": [],
"source": [
"imgs, labels = next(train_generator)"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"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",
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"execution_count": 12,
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"id": "85934565",
"metadata": {},
"outputs": [],
"source": [
"#plotImages(imgs)\n",
"#print(labels)"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"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",
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"execution_count": 14,
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"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",
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"execution_count": 15,
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"id": "b31af79e",
"metadata": {},
"outputs": [],
"source": [
"base_model = tf.keras.applications.xception.Xception(include_top=False, weights='imagenet', pooling='avg')\n",
"#base_model.summary()"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"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",
" block1_conv1 (Conv2D) (None, None, None, 864 ['input_1[0][0]'] \n",
" 32) \n",
" \n",
" block1_conv1_bn (BatchNormaliz (None, None, None, 128 ['block1_conv1[0][0]'] \n",
" ation) 32) \n",
" \n",
" block1_conv1_act (Activation) (None, None, None, 0 ['block1_conv1_bn[0][0]'] \n",
" 32) \n",
" \n",
" block1_conv2 (Conv2D) (None, None, None, 18432 ['block1_conv1_act[0][0]'] \n",
" 64) \n",
" \n",
" block1_conv2_bn (BatchNormaliz (None, None, None, 256 ['block1_conv2[0][0]'] \n",
" ation) 64) \n",
" \n",
" block1_conv2_act (Activation) (None, None, None, 0 ['block1_conv2_bn[0][0]'] \n",
" 64) \n",
" \n",
" block2_sepconv1 (SeparableConv (None, None, None, 8768 ['block1_conv2_act[0][0]'] \n",
" 2D) 128) \n",
" \n",
" block2_sepconv1_bn (BatchNorma (None, None, None, 512 ['block2_sepconv1[0][0]'] \n",
" lization) 128) \n",
" \n",
" block2_sepconv2_act (Activatio (None, None, None, 0 ['block2_sepconv1_bn[0][0]'] \n",
" n) 128) \n",
" \n",
" block2_sepconv2 (SeparableConv (None, None, None, 17536 ['block2_sepconv2_act[0][0]'] \n",
" 2D) 128) \n",
" \n",
" block2_sepconv2_bn (BatchNorma (None, None, None, 512 ['block2_sepconv2[0][0]'] \n",
" lization) 128) \n",
" \n",
" conv2d (Conv2D) (None, None, None, 8192 ['block1_conv2_act[0][0]'] \n",
" 128) \n",
" \n",
" block2_pool (MaxPooling2D) (None, None, None, 0 ['block2_sepconv2_bn[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization (BatchNorm (None, None, None, 512 ['conv2d[0][0]'] \n",
" alization) 128) \n",
" \n",
" add (Add) (None, None, None, 0 ['block2_pool[0][0]', \n",
" 128) 'batch_normalization[0][0]'] \n",
" \n",
" block3_sepconv1_act (Activatio (None, None, None, 0 ['add[0][0]'] \n",
" n) 128) \n",
" \n",
" block3_sepconv1 (SeparableConv (None, None, None, 33920 ['block3_sepconv1_act[0][0]'] \n",
" 2D) 256) \n",
" \n",
" block3_sepconv1_bn (BatchNorma (None, None, None, 1024 ['block3_sepconv1[0][0]'] \n",
" lization) 256) \n",
" \n",
" block3_sepconv2_act (Activatio (None, None, None, 0 ['block3_sepconv1_bn[0][0]'] \n",
" n) 256) \n",
" \n",
" block3_sepconv2 (SeparableConv (None, None, None, 67840 ['block3_sepconv2_act[0][0]'] \n",
" 2D) 256) \n",
" \n",
" block3_sepconv2_bn (BatchNorma (None, None, None, 1024 ['block3_sepconv2[0][0]'] \n",
" lization) 256) \n",
" \n",
" conv2d_1 (Conv2D) (None, None, None, 32768 ['add[0][0]'] \n",
" 256) \n",
" \n",
" block3_pool (MaxPooling2D) (None, None, None, 0 ['block3_sepconv2_bn[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_1 (BatchNo (None, None, None, 1024 ['conv2d_1[0][0]'] \n",
" rmalization) 256) \n",
" \n",
" add_1 (Add) (None, None, None, 0 ['block3_pool[0][0]', \n",
" 256) 'batch_normalization_1[0][0]'] \n",
" \n",
" block4_sepconv1_act (Activatio (None, None, None, 0 ['add_1[0][0]'] \n",
" n) 256) \n",
" \n",
" block4_sepconv1 (SeparableConv (None, None, None, 188672 ['block4_sepconv1_act[0][0]'] \n",
" 2D) 728) \n",
" \n",
" block4_sepconv1_bn (BatchNorma (None, None, None, 2912 ['block4_sepconv1[0][0]'] \n",
" lization) 728) \n",
" \n",
" block4_sepconv2_act (Activatio (None, None, None, 0 ['block4_sepconv1_bn[0][0]'] \n",
" n) 728) \n",
" \n",
" block4_sepconv2 (SeparableConv (None, None, None, 536536 ['block4_sepconv2_act[0][0]'] \n",
" 2D) 728) \n",
" \n",
" block4_sepconv2_bn (BatchNorma (None, None, None, 2912 ['block4_sepconv2[0][0]'] \n",
" lization) 728) \n",
" \n",
" conv2d_2 (Conv2D) (None, None, None, 186368 ['add_1[0][0]'] \n",
" 728) \n",
" \n",
" block4_pool (MaxPooling2D) (None, None, None, 0 ['block4_sepconv2_bn[0][0]'] \n",
" 728) \n",
" \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",
2022-01-13 05:25:40 +00:00
"execution_count": 17,
2022-01-03 20:32:42 +00:00
"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",
2022-01-13 05:25:40 +00:00
"execution_count": 18,
2022-01-03 20:32:42 +00:00
"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",
2022-01-13 05:25:40 +00:00
"execution_count": 19,
2022-01-03 20:32:42 +00:00
"id": "9cd2ba27",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
2022-01-13 05:25:40 +00:00
"829/829 [==============================] - 786s 942ms/step - loss: 1.5037 - accuracy: 0.4896 - val_loss: 1.2946 - val_accuracy: 0.5520\n",
2022-01-03 20:32:42 +00:00
"Epoch 2/30\n",
2022-01-13 05:25:40 +00:00
"829/829 [==============================] - 726s 875ms/step - loss: 0.8550 - accuracy: 0.7117 - val_loss: 1.3593 - val_accuracy: 0.5593\n",
2022-01-03 20:32:42 +00:00
"Epoch 3/30\n",
2022-01-13 05:25:40 +00:00
"829/829 [==============================] - 750s 905ms/step - loss: 0.3322 - accuracy: 0.8993 - val_loss: 1.5304 - val_accuracy: 0.5542\n",
2022-01-03 20:32:42 +00:00
"Epoch 4/30\n",
2022-01-13 05:25:40 +00:00
"172/829 [=====>........................] - ETA: 7:57 - loss: 0.1030 - accuracy: 0.9787"
2022-01-03 20:32:42 +00:00
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
2022-01-13 05:25:40 +00:00
"\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",
2022-01-03 20:32:42 +00:00
"\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",
"\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/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/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",
"\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",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"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
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