import random

import tensorflow as tf

RUN_NAME = 'run_{}'.format(random.getrandbits(64))

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

tensorboard = tf.keras.callbacks.TensorBoard(log_dir='./logs/{}'.format(RUN_NAME),
                                             write_graph=True,
                                             write_images=False)

model.fit(x_train, y_train, epochs=5, callbacks=[tensorboard])