from keras.preprocessing.image import ImageDataGenerator
from pnslib import utils
from pnslib import ml
datagen = ImageDataGenerator(featurewise_center=True,
featurewise_std_normalization=True,
rotation_range=20,
width_shift_range=0.2,
height_shift_range=0.2,
horizontal_flip=True)
# Load all the ten classes from Fashion MNIST
# complete label description is at
# https://github.com/zalandoresearch/fashion-mnist#labels
(train_x, train_y, test_x, test_y) = utils.fashion_mnist_load(data_type="full",
flatten=False)
num_classes = 10
print("[MESSAGE] Dataset is loaded.")
# preprocessing for training and testing images
train_x = train_x.astype("float32") / 255. # rescale image
mean_train_x = np.mean(train_x, axis=0) # compute the mean across pixels
train_x -= mean_train_x # remove the mean pixel value from image
test_x = test_x.astype("float32") / 255.
test_x -= mean_train_x
print("[MESSAGE] Dataset is preprocessed.")
print(test_x.shape)
"""Demonstrate the usage of Fashion-MNIST. Author: Yuhuang Hu Email : [email protected] """ from __future__ import print_function, absolute_import import matplotlib.pyplot as plt import pnslib.utils as utils (train_x, train_y, test_x, test_y) = utils.fashion_mnist_load("full") print(train_x.shape) print(train_y.shape) print(test_x.shape) print(test_y.shape) plt.figure() for idx in range(100): plt.imshow(test_x[idx, ..., 0], cmap="gray") plt.show()
