letter_prediction_nn.py

import tensorflow as tf  
import numpy as np
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)  

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

model.save('epic_num_reader.model') 
new_model = tf.keras.models.load_model('epic_num_reader.model') 
predictions = new_model.predict(x_test) 
print(predictions)


print(np.argmax(predictions[1])) 
plt.imshow(x_test[1],cmap=plt.cm.binary) 
plt.show()