How to change input data set for character recognition program?

[jefsummers]

I made a variety of modifications. This runs

# from keras.datasets import mnist
# (train_images, train_labels), (test_images, test_labels) = mnist.load_data()
# Instead of using MNdist data, I use my own numeric data as below.
import pandas as pd
import numpy as np
import tensorflow as tf

train_images = [[0, 1],
                [1, 0],
                [0, 1]]
train_labels = [9,0,9]
test_images = [[0, 1],
               [0, 1],
               [0, 1]]
test_labels = [[0, 10],
               [0, 10],
               [0, 10]]

train_images = np.array(train_images) 
train_labels = np.array(train_labels)
test_images = np.array(test_images)
test_labels = np.array(test_labels)

from keras import models
from keras import layers
 
network = models.Sequential()
network.add(layers.Dense(512, activation='relu'))
network.add(layers.Dense(10, activation='softmax'))
 
network.compile(optimizer='adam',loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
 
#train_images = train_images.reshape((3, 2))
#train_images = train_images.astype('float32') / 255
#test_images = test_images.reshape((3, 2))
#test_images = test_images.astype('float32') / 255
 
# from keras.utils import to_categorical
from tensorflow.keras.utils import to_categorical
 
#train_labels = to_categorical(train_labels)
#test_labels = to_categorical(test_labels)
network.fit(train_images, train_labels, epochs=5)