index.py

import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

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=1)

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

print("==============After train================")

print(val_loss,val_acc)









predictions = model.predict([x_test])
print(np.argmax(predictions[15]))


plt.imshow(x_test[15])
plt.show()