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()
