# -*- coding: utf-8 -*-
"""
Created on Mon Sep 30 07:24:20 2019

@author: askin
"""

import tensorflow as tf
import matplotlib.pyplot as plt

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = tf.keras.utils.normalize(x_train, axis = 1)
x_test = tf.keras.utils.normalize(x_test, axis = 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=3)

model.save('num_reader.model')

# loading - new_model = tf.keras.models.load_model('num_reader.model')
# predicitons = new_model.predict(x_test) # ALWAYS TAKES A LIST
# import numpy as np
# print(np.argmax(predictions[0]))
# plt.imshow(x_test[0])
# plot.show()

val_loss, val_acc = model.evaluate(x_test, y_test)
print(val_loss, val_acc)

plt.imshow(x_train[0], cmap = plt.cm.binary)
plt.show()