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Data_helper.py
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Data_helper.py
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import tensorflow
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
# load train and test dataset
def load_dataset():
# load dataset
(trainX, trainY), (testX, testY) = cifar10.load_data()
# one hot encode target values
trainY = to_categorical(trainY)
testY = to_categorical(testY)
print('yo')
return trainX, trainY, testX, testY
# summarize loaded dataset
def summarize_data():
trainX, trainY, testX, testY = load_dataset()
print('Train: X=%s, y=%s' % (trainX.shape, trainY.shape))
print('Test: X=%s, y=%s' % (testX.shape, testY.shape))
# plot first few images
for i in range(9):
# define subplot
plt.subplot(330 + 1 + i)
# plot raw pixel data
plt.imshow(trainX[i])
# show the figure
plt.show()
plt.close()
# normalize data
def normalize(train, test):
# convert from integers to floats
train_norm = train.astype('float32')
test_norm = test.astype('float32')
# normalize to range 0-1
train_norm = train_norm - train_norm.min() / train_norm.max() - train_norm.min()
test_norm = test_norm - test_norm.min() / test_norm.max() - test_norm.min()
# return normalized images
return train_norm, test_norm
load_dataset()