def update_fit_plot(neural_network, expectations, training_result): print(training_result.error) fig = plt.figure("Fit Tracker") if len(fig.axes) > 0 and len(fig.axes[0].lines) > 1: ax = fig.axes[0] line = ax.lines[1] line.set_ydata(NeuralNetworkUtils.list_of_outputs(neural_network, [e.inputs for e in expectations])) fig.canvas.draw()
def show_fit_tracker(neural_network, expectations): x = [e.inputs[0] for e in expectations] goal = [e.outputs[0] for e in expectations] current = NeuralNetworkUtils.list_of_outputs(neural_network, [e.inputs for e in expectations]) fig = plt.figure("Fit Tracker") ax = fig.add_subplot(111) ax.plot(x, goal) ax.plot(x, current) fig.canvas.draw() fig.show()