append ( Y ) if len ( p ) = batch_size : yield np. permutation ( len ( X )) X = X Y = Y p, q =, for i in range ( len ( X )): p. random () > 0.5 : return x else : return x def data_generator ( X, Y, batch_size = 100 ): while True : idxs = np. x except Exception : pass import tensorflow as tf from tensorflow import keras from import Sequential from import Dense, Conv2D, MaxPool2D, Dropout, Flatten, BatchNormalization, Input, Lambda, GlobalAveragePooling2D from import to_categorical from import EarlyStopping from import Adadelta, Adam from import categorical_crossentropy from import MobileNet from _v2 import MobileNetV2 from import Model from import repeat_elements, expand_dims, resize_images from import ImageDataGenerator import keras.backend as K from scipy.stats import reciprocal ! pip install keras - tuner from kerastuner.tuners import HyperbandÄef random_reverse ( x ): if np. Import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import os import multiprocessing from statistics import mean from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV from trics import accuracy_score, f1_score from sklearn.preprocessing import MinMaxScaler try : # %tensorflow_version only exists in Colab.
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