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cifar.py
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import tensorflow as tf
import numpy as np
IMG_SIZE = 32
def fMNIST():
(train_data, train_label), (test_data, test_label) = tf.keras.datasets.fashion_mnist.load_data()
(train_data, train_label) = (np.expand_dims(train_data.astype(float), axis=1), train_label.astype(np.int32))
(test_data, test_label) = (np.expand_dims(test_data.astype(float), axis=1), test_label.astype(np.int32))
print(train_data.shape)
print(test_data.shape)
return (train_data, train_label), (test_data, test_label)
def MNIST():
(train_data, train_label), (test_data, test_label) = tf.keras.datasets.mnist.load_data()
(train_data, train_label) = (np.expand_dims(train_data.astype(float), axis=1), train_label.astype(np.int32))
(test_data, test_label) = (np.expand_dims(test_data.astype(float), axis=1), test_label.astype(np.int32))
print(train_data.shape)
print(test_data.shape)
return (train_data, train_label), (test_data, test_label)
def CIFAR10():
(train_data, train_label), (test_data, test_label) = tf.keras.datasets.cifar10.load_data()
(train_data, train_label) = (train_data.astype(float), train_label.astype(np.int32))
(test_data, test_label) = (test_data.astype(float), test_label.astype(np.int32))
print(train_data.shape)
print(test_data.shape)
return (train_data, train_label), (test_data, test_label)
def CIFAR100():
(train_data, train_label), (test_data, test_label) = tf.keras.datasets.cifar100.load_data()
(train_data, train_label) = (train_data.astype(float), train_label.astype(np.int32))
(test_data, test_label) = (test_data.astype(float), test_label.astype(np.int32))
return (train_data, train_label), (test_data, test_label)