#!/usr/bin/python3

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

import random

print(tf.__version__)

fashion_mnist = keras.datasets.fashion_mnist

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

train_images = train_images / 255.0
test_images = test_images / 255.0

class_names = [
    "T-shirt/top",
    "Trouser",
    "Pullover",
    "Dress",
    "Coat",
    "Sandal",
    "Shirt",
    "Sneaker",
    "Bag",
    "Ankle boot",
]

model = keras.Sequential(
    [
        keras.layers.Flatten(input_shape=(28, 28)),
        keras.layers.Dense(128, activation=tf.nn.relu),
        keras.layers.Dense(10, activation=tf.nn.softmax),
    ]
)

model.compile(
    optimizer=tf.train.AdamOptimizer(),
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)

model.fit(train_images, train_labels, epochs=5)

test_loss, test_acc = model.evaluate(test_images, test_labels)

print("Test accuracy:", test_acc)