createModel.py

# Used to train and save a neural network on the mnist dataset
import install_requirements

import keras
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)

for train in range(len(x_train)):
    for row in range(28):
        for x in range(28):
            if x_train[train][row][x] != 0:
                x_train[train][row][x] = 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('epic_num_reader.model')

print("Model saved")