import tensorflow as tf
import os

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()

print(x_train[0])

x_train = tf.keras.utils.normalize(x_train, axis=0)
x_test = tf.keras.utils.normalize(x_test, axis=0)

print(x_test[0].size)

#path = "training_1/sp.ckpt"
#checkpoint_dir = os.path.dirname(path)

#cp_callback = tf.keras.callbacks.ModelCheckpoint(path, save_weights_only = True, verbose = 1)

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))

model.compile(optimizer = "adam",
              loss="sparse_categorical_crossentropy",
              metrics= ["accuracy"])


model.fit(x_train, y_train,  epochs = 1)
model.save("number.h5")