script.py

#!/usr/bin/env python3
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=3)



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

prediction = model.predict([x_test])
print(prediction)

print(np.argmax(prediction[100]))
plt.imshow(x_test[100])
plt.show()