tf1.py

import tensorflow as tf
import datetime

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


def create_model():
    return tf.keras.models.Sequential([

        tf.keras.layers.Flatten(input_shape=(28, 28)),

        tf.keras.layers.Dense(512, activation='relu'),

        tf.keras.layers.Dropout(0.2),

        tf.keras.layers.Dense(10, activation='softmax')

    ])


model = create_model()

model.compile(optimizer='adam',

                  loss='sparse_categorical_crossentropy',

                  metrics=['accuracy'])

log_dir = "logs_data/fit/" + datetime.datetime.now().strftime("%m-%d--%H%M%S")
tensorboard_callback = tf.compat.v1.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_grads=True)

model.fit(x=x_train,
              y=y_train,
              epochs=20,
              validation_data=(x_test, y_test),
              callbacks=[tensorboard_callback])