sequentialnn.py

import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

mnist = tf.keras.datasets.mnist
#we'll unpack our tuple here
(x_train, y_train), (x_test, y_test) = mnist.load_data()

#normalize training data(the pixels) from 0 to 1
x_train =  tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis = 1)

#well just be using a basic sequntial neural network for the model type
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten(input_shape = (28, 28)))
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
model.add(tf.keras.layers.Dense(128, activation = 'relu'))
model.add(tf.keras.layers.Dense(10, activation = 'softmax'))

model.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])
model.fit(x_train, y_train, epochs = 10)
model.save('handwritten.keras')