def main():
brain = NeuralNetwork(784, 3000, 10)
brain.set_learning_rate(.001)
counter = 0
training_images_norm = normalize_images(training_images)
testing_images_norm = normalize_images(testing_images)
for image, label in zip(training_images_norm, training_labels):
counter += 1
print(
f"Backward propogating the {counter} image, which was a {label} with array: {labels_dict[label]}."
)
brain.train_tanh(image.ravel(), labels_dict[label])
#learning_rate_annealing_cyclic(brain, counter) #brain, iteration
#print(f"Learning Rate: {brain.learning_rate}")
if counter >= 1000:
break
correct = 0
for i in range(len(testing_images_norm)):
output = brain.feedforward_tanh(testing_images_norm[i].ravel())
output = output.ravel()
#print(testing_images[i].ravel())
print(output)
print(
f"The letter was thought to be {output.argmax()} by the Neural network, but was actually {testing_labels[i]}."
)
if output.argmax() == testing_labels[i]:
correct += 1
print("IT WAS CORRECT!")
print(correct / i)
