hidden = Layer(NUM_HIDDEN_NODES, input_data.shape[0], expit)
output = Layer(len(input_files), NUM_HIDDEN_NODES, softmax)
average_errors = np.ones(len(input_files))
accepted_errors = np.full(len(input_files), ACCEPTED_ERROR)
while not np.all(np.less(average_errors, accepted_errors)):
# get a random date
input_data, expected_value = get_input_data(input_files)
# process inputs
outputs = output.process(hidden.process(input_data))
# calculate errors
output.errors = expected_value - outputs
hidden.errors = expit_prime(hidden.h) * np.dot(output.errors, output.weights)
# update weights and biases
output.update()
hidden.update()
average_errors = update_ema(output.errors, average_errors)
print('last errors:\n', output.errors)
print('last outputs:\n', outputs)
print('last outputs rounded:\n', outputs.round())
#index = expected_value.nonzero()[0][0]
#print('index of class:', index)
#print('output of class:', outputs[index])
