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{
"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",
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"from collections import Counter\n",
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"import json\n",
"import os\n",
"import re\n",
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"import tempfile\n",
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"import numpy as np\n",
"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",
"from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n",
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"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,
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"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",
"execution_count": 3,
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"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",
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"execution_count": 4,
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"id": "1057a442",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{source:target} dictionary created @ /tf/training_images\n"
]
}
],
"source": [
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"def dict_pics_jup():\n",
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" '''\n",
" {source:target} dict used to replace source urls with image location as input\n",
" '''\n",
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" 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",
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" \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",
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" print(\"{source:target} dictionary created @ \" + target_dir)\n",
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" return dict_pics"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
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"id": "7a6146e6",
"metadata": {},
"outputs": [],
"source": [
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"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",
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"df['PrimaryCategoryID'] = df['PrimaryCategoryID'].astype(str) # pandas thinks ids are ints\n",
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"ddf = df[df.PictureURL.isin(drop_row_vals)==False] # remove improperly named image files\n",
"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\n",
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"\n",
"df=df.sample(frac=1)"
]
},
{
"cell_type": "code",
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"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counter({'11498': 3913, '11504': 3913, '11505': 3913, '11632': 3913, '15709': 3913, '24087': 3913, '45333': 3913, '53120': 3913, '53548': 3913, '53557': 3913, '55793': 3913, '62107': 3913, '95672': 3913})\n"
]
}
],
"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,
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"id": "4d72eb90",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Found 32547 validated image filenames belonging to 13 classes.\n",
"Found 8136 validated image filenames belonging to 13 classes.\n"
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
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"/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 12 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
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" warnings.warn(\n"
]
}
],
"source": [
"datagen = ImageDataGenerator(rescale=1./255., \n",
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" validation_split=.2,\n",
" #samplewise_std_normalization=True,\n",
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" #horizontal_flip= True,\n",
" #vertical_flip= True,\n",
" #width_shift_range= 0.2,\n",
" #height_shift_range= 0.2,\n",
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" #rotation_range= 90,\n",
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" preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\n",
"train_generator=datagen.flow_from_dataframe(\n",
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" dataframe=train[:len(train)],\n",
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" directory='./training_images',\n",
" x_col='PictureURL',\n",
" y_col='PrimaryCategoryID',\n",
" batch_size=32,\n",
" seed=42,\n",
" shuffle=True,\n",
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" target_size=(244,244),\n",
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" subset='training'\n",
" )\n",
"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",
" x_col='PictureURL',\n",
" y_col='PrimaryCategoryID',\n",
" batch_size=32,\n",
" seed=42,\n",
" shuffle=True,\n",
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" target_size=(244,244),\n",
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" subset='validation'\n",
" )"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"id": "7b70f37f",
"metadata": {},
"outputs": [],
"source": [
"imgs, labels = next(train_generator)"
]
},
{
"cell_type": "code",
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"execution_count": 10,
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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": 11,
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"id": "85934565",
"metadata": {},
"outputs": [],
"source": [
"#plotImages(imgs)\n",
"#print(labels)"
]
},
{
"cell_type": "code",
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"execution_count": 12,
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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": "b31af79e",
"metadata": {},
"outputs": [],
"source": [
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"base_model = tf.keras.applications.vgg19.VGG19(weights='imagenet')"
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]
},
{
"cell_type": "code",
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"execution_count": 15,
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"id": "fe06f2bf",
"metadata": {},
"outputs": [],
"source": [
"model = Sequential()\n",
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"for layer in base_model.layers[:-1]:\n",
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" model.add(layer)"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"id": "7d3cc82c",
"metadata": {},
"outputs": [],
"source": [
"for layer in model.layers:\n",
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" layer.trainable = True\n",
" \n",
"#model.add(Dropout(.5))\n",
"#model.add(Dense(64, activation='softmax'))\n",
"# model.add(Dropout(.25))\n",
"\n",
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"model.add(Dense(units=7, activation='softmax'))"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"id": "c774d787",
"metadata": {
"scrolled": true
},
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" block1_conv1 (Conv2D) (None, 224, 224, 64) 1792 \n",
" \n",
" block1_conv2 (Conv2D) (None, 224, 224, 64) 36928 \n",
" \n",
" block1_pool (MaxPooling2D) (None, 112, 112, 64) 0 \n",
" \n",
" block2_conv1 (Conv2D) (None, 112, 112, 128) 73856 \n",
" \n",
" block2_conv2 (Conv2D) (None, 112, 112, 128) 147584 \n",
" \n",
" block2_pool (MaxPooling2D) (None, 56, 56, 128) 0 \n",
" \n",
" block3_conv1 (Conv2D) (None, 56, 56, 256) 295168 \n",
" \n",
" block3_conv2 (Conv2D) (None, 56, 56, 256) 590080 \n",
" \n",
" block3_conv3 (Conv2D) (None, 56, 56, 256) 590080 \n",
" \n",
" block3_pool (MaxPooling2D) (None, 28, 28, 256) 0 \n",
" \n",
" block4_conv1 (Conv2D) (None, 28, 28, 512) 1180160 \n",
" \n",
" block4_conv2 (Conv2D) (None, 28, 28, 512) 2359808 \n",
" \n",
" block4_conv3 (Conv2D) (None, 28, 28, 512) 2359808 \n",
" \n",
" block4_pool (MaxPooling2D) (None, 14, 14, 512) 0 \n",
" \n",
" block5_conv1 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_conv2 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_conv3 (Conv2D) (None, 14, 14, 512) 2359808 \n",
" \n",
" block5_pool (MaxPooling2D) (None, 7, 7, 512) 0 \n",
" \n",
" flatten (Flatten) (None, 25088) 0 \n",
" \n",
" fc1 (Dense) (None, 4096) 102764544 \n",
" \n",
" fc2 (Dense) (None, 4096) 16781312 \n",
" \n",
" dense (Dense) (None, 13) 53261 \n",
" \n",
"=================================================================\n",
"Total params: 134,313,805\n",
"Trainable params: 134,313,805\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
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"source": [
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"#model = add_regularization(model)\n",
"model.summary()\n"
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]
},
{
"cell_type": "code",
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"execution_count": 19,
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"id": "fd5d1246",
"metadata": {},
"outputs": [],
"source": [
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"model.compile(optimizer=Adam(learning_rate=.0001), loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"# sparse_categorical_crossentropy"
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]
},
{
"cell_type": "code",
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"execution_count": 20,
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"id": "9cd2ba27",
"metadata": {
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"scrolled": false
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Epoch 1/30\n",
"1018/1018 [==============================] - 391s 379ms/step - loss: 2.4329 - accuracy: 0.1571 - val_loss: 2.1902 - val_accuracy: 0.2525\n",
"Epoch 2/30\n",
"1018/1018 [==============================] - 379s 373ms/step - loss: 2.0578 - accuracy: 0.2960 - val_loss: 1.9160 - val_accuracy: 0.3330\n",
"Epoch 3/30\n",
"1018/1018 [==============================] - 381s 374ms/step - loss: 1.8221 - accuracy: 0.3681 - val_loss: 1.8588 - val_accuracy: 0.3535\n",
"Epoch 4/30\n",
"1018/1018 [==============================] - 383s 376ms/step - loss: 1.6406 - accuracy: 0.4272 - val_loss: 1.7819 - val_accuracy: 0.4028\n",
"Epoch 5/30\n",
"1018/1018 [==============================] - 383s 376ms/step - loss: 1.4577 - accuracy: 0.4920 - val_loss: 1.7216 - val_accuracy: 0.4158\n",
"Epoch 6/30\n",
"1018/1018 [==============================] - 379s 372ms/step - loss: 1.2528 - accuracy: 0.5607 - val_loss: 1.7924 - val_accuracy: 0.4140\n",
"Epoch 7/30\n",
"1018/1018 [==============================] - 378s 371ms/step - loss: 1.0030 - accuracy: 0.6469 - val_loss: 1.8017 - val_accuracy: 0.4303\n",
"Epoch 8/30\n",
"1018/1018 [==============================] - 379s 372ms/step - loss: 0.7405 - accuracy: 0.7420 - val_loss: 1.9863 - val_accuracy: 0.4453\n",
"Epoch 9/30\n",
"1018/1018 [==============================] - 379s 372ms/step - loss: 0.4704 - accuracy: 0.8354 - val_loss: 2.3988 - val_accuracy: 0.4263\n",
"Epoch 10/30\n",
"1018/1018 [==============================] - 379s 372ms/step - loss: 0.3059 - accuracy: 0.8944 - val_loss: 2.7526 - val_accuracy: 0.4303\n",
"Epoch 11/30\n",
"1018/1018 [==============================] - 377s 371ms/step - loss: 0.2160 - accuracy: 0.9278 - val_loss: 3.0618 - val_accuracy: 0.4250\n",
"Epoch 12/30\n",
" 437/1018 [===========>..................] - ETA: 2:53 - loss: 0.1370 - accuracy: 0.9536"
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]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-20-4cd4443bbf2a>\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",
"\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 1214\u001b[0m _r=1):\n\u001b[1;32m 1215\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1217\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1218\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 909\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 912\u001b[0m \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 940\u001b[0m \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 941\u001b[0m \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 943\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 944\u001b[0m \u001b[0;31m# Release the lock early so that multiple threads can perform the call\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/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3128\u001b[0m (graph_function,\n\u001b[1;32m 3129\u001b[0m filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m return graph_function._call_flat(\n\u001b[0m\u001b[1;32m 3131\u001b[0m filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access\n\u001b[1;32m 3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m 1957\u001b[0m and executing_eagerly):\n\u001b[1;32m 1958\u001b[0m \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m 1960\u001b[0m ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m 1961\u001b[0m forward_backward = self._select_forward_and_backward_functions(\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 597\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m outputs = execute.execute(\n\u001b[0m\u001b[1;32m 599\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m 59\u001b[0m inputs, attrs, num_outputs)\n\u001b[1;32m 60\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\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",
2022-01-03 20:32:42 +00:00
" epochs=30,\n",
" verbose=1)"
2021-12-31 22:08:48 +00:00
]
},
{
"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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