ax.xaxis.set_major_locator(ticker.MultipleLocator(1)) ax.yaxis.set_major_locator(ticker.MultipleLocator(1)) # sphinx_gallery_thumbnail_number = 2 plt.show() if __name__ == '__main__': dataReader = load_data() eta = 0.005 max_epoch = 100 batch_size = 16 num_input = dataReader.num_feature num_hidden = 4 num_output = dataReader.num_category model = str.format("CharName_{0}_{1}_{2}_{3}_{4}_{5}", max_epoch, batch_size, num_input, num_hidden, num_output, eta) hp = HyperParameters_4_3(eta, max_epoch, batch_size, dataReader.max_step, num_input, num_hidden, num_output, OutputType.LastStep, NetType.MultipleClassifier) n = net(hp, model) n.train(dataReader, checkpoint=1) # last n.test(dataReader) # best n.load_parameters() dataReader.ResetPointer() n.test(dataReader)
x2 = X[idx, :, 1] print(" x1:", reverse(x1)) print("- x2:", reverse(x2)) print("------------------") print("true:", reverse(Y[idx])) print("pred:", reverse(result[idx])) print("====================") #end for def reverse(a): l = a.tolist() l.reverse() return l if __name__ == '__main__': dataReader = load_data() eta = 0.1 max_epoch = 100 batch_size = 1 num_step = 4 num_input = 2 num_output = 1 num_hidden = 8 hp = HyperParameters_4_3(eta, max_epoch, batch_size, num_step, num_input, num_hidden, num_output, NetType.Fitting) n = net(hp) n.train(dataReader, checkpoint=0.1) n.test(dataReader)
p1, = plt.plot(ra[0:200]) p2, = plt.plot(ry[0:200]) plt.legend([p1,p2], ["pred","true"]) plt.show() p1, = plt.plot(ra[1000:1200]) p2, = plt.plot(ry[1000:1200]) plt.legend([p1,p2], ["pred","true"]) plt.show() if __name__=='__main__': net_type = NetType.MultipleClassifier num_step = 8 #8 dataReader = load_data(net_type, num_step) eta = 0.1 max_epoch = 100 batch_size = 64 #64 num_input = dataReader.num_feature num_hidden = 16 # 16 num_output = dataReader.num_category model = str.format("Level3_{0}_{1}_{2}_{3}", max_epoch, batch_size, num_hidden, eta) hp = HyperParameters_4_3( eta, max_epoch, batch_size, num_step, num_input, num_hidden, num_output, net_type) n = net(hp, model) #n.load_parameters() n.train(dataReader, checkpoint=1)