#%%
import tensorflow as tf

from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt


print(tf.__version__)
fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

plt.figure()
plt.imshow(train_images[10])
plt.colorbar()
plt.grid(False)
plt.show()

# scale
train_images = train_images / 255.0
test_images = test_images / 255.0


plt.figure(figsize=(10,10))

for i in range(25):
  plt.subplot(5,5, i+1)
  plt.xticks([])
  plt.yticks([])
  plt.imshow(train_images[i], cmap=plt.cm.Blues)
  plt.xlabel(class_names[train_labels[i]])
plt.show()

#%%