In [1]:

    

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
import glob
from shutil import copyfile
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np


    

    




Using TensorFlow backend.

In [2]:

    

(X_train, y_train), (X_test, y_test) = cifar10.load_data()


    

    




Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 148s 1us/step

In [4]:

    

train_datagen = ImageDataGenerator(
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode="nearest"
)
train_iter = train_datagen.flow(
    X_train, y_train,
    batch_size=9)
plt.rcParams['figure.figsize'] = (10,10)
for i in range(9):
    images, labels = next(train_iter)
    plt.subplot(3,3,i+1)
    plt.imshow((images[0] - np.min(images[0])) / np.ptp(images[0]), interpolation='none')
    plt.title("label {}".format(labels[0]))


    

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