import tensorflow as tf
# import matplotlib.pyplot as plt
print tf.__version__

mnist = tf.keras.datasets.fashion_mnist
# load (train images set)
(training_images, training_labels), (test_images, test_labels) = mnist.load_data()
# plt.imshow(training_images[0])
# print training_labels[0]
# print training_images[0]

training_images = training_images / 255.0
test_images = test_images / 255.0

model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),
                                    tf.keras.layers.Dense(128, activation=tf.nn.relu),
                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)])
model.compile(optimizer = tf.keras.optimizers.Adam(),
              loss = 'sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(training_images, training_labels, epochs=5)
model.evaluate(test_images, test_labels)
classifications = model.predict(test_images)

print(classifications[0])
print(test_labels[0])
