Neural Network.py

import keras
import numpy as np

def create_neural_network(input_shape, output_shape):
  model = keras.Sequential()
  model.add(keras.layers.Dense(output_shape, activation='softmax'))

  return model

def train_neural_network(model, data):
  model.compile(loss='categorical_crossentropy', optimizer='adam')
  model.fit(data, epochs=10)

def predict_neural_network(model, data):
  predictions = model.predict(data)
  return predictions

def main():
  # Create the neural network
  model = create_neural_network(input_shape=(784,), output_shape=(10,))

  # Train the neural network
  data = keras.datasets.mnist.load_data()
  x_train = data['x_train']
  y_train = data['y_train']

  model.fit(x_train, y_train, epochs=10)

  # Predict the labels of the test data
  x_test = data['x_test']
  y_test_predictions = model.predict(x_test)

  # Evaluate the model
  accuracy = accuracy_score(y_test, y_test_predictions)

  print('Accuracy:', accuracy)

if __name__ == '__main__':
  main()
# 3/30/2023 -Root