# -*- coding: utf-8 -*-
"""ann.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1PnRbff22bgnNQHb81ETPvIT-lI7oLN9p
"""

import tensorflow.keras as keras
import tensorflow as tf
print(tf.__version__)

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

import matplotlib.pyplot as plt
plt.imshow(x_train[1],cmap=plt.cm.binary)
plt.show()

x_train=tf.keras.utils.normalize(x_train, axis=1)
x_test=tf.keras.utils.normalize(x_test, axis=1)

print(x_train[0])

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

#build the model
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)

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

predictions=model.predict(x_test)
print(predictions)

import numpy as np
print(np.argmax(predictions[0]))

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

y_test[10]