def makernnset(steps):
train=loadgz('/Users/subercui/Git/BaikeBalance/RNN/dataset/trainset.pkl.gz')
test=loadgz('/Users/subercui/Git/BaikeBalance/RNN/dataset/testset.pkl.gz')
trainstarts=sigment(train,4)
teststarts=sigment(test,1.5)
rnntrain=makedata(train,trainstarts,steps)
rnntest=makedata(test,teststarts,steps)
return rnntrain,rnntest
#plt.plot(3.*sequence[:,1],label='lean angle rate')
plt.plot(predictions,label='offline network out')
plt.grid()
plt.legend(loc='best', fancybox=True, framealpha=0.5)
plt.xlabel('time(10ms)')
plt.ylabel('Value')
plt.show()
print 'abs error',np.mean(np.abs(predictions-targets))
def convert_norm(X):
return X*np.array([[7.7,10.0,1,0.03,0.5]])+np.array([[0.,0.,1.9,0.,2.]])
if __name__=='__main__':
parent_path = os.path.split(os.path.realpath(__file__))[0]
trainset=loadgz(parent_path+'/dataset/trainset.pkl.gz')
testset=loadgz(parent_path+'/dataset/testset.pkl.gz')
trainset=add_simu_data(trainset,trainset.shape[0]/6)
testset=add_simu_data(testset,testset.shape[0]/6)
X_train, y_train, X_valid, y_valid = build_mlp_dataset(data=trainset,test=testset)
MLPmodel=build_mlp(X_train.shape[-1] ,y_train.shape[-1], 100,20)
#是否加载pretrain model
#MLPmodel.load_weights(parent_path+'/MLP_weightsBest.hdf5')
#print X_train[:1024],y_train[:1024],X_valid[:1024],y_valid[:1024]
#print X_train[:1024].mean(axis=0)
train(X_train, y_train, X_valid, y_valid, MLPmodel, batch_size=128)
#下面这个函数里可以选择可视化哪个数据文件
visual_test(MLPmodel,sequence=testset)
'''只是改变了训练数据的比例，就得到了一组更好的模型'''
plt.legend(loc='best', fancybox=True, framealpha=0.5)
plt.xlabel('time(20ms)')
plt.ylabel('steering angle (degree)')
plt.show()
print 'abs error',np.mean(np.abs(predictions-targets))
return predictions
def convert_norm(X):
return X*np.array([[7.7,10.0,1,0.03,0.5]])+np.array([[0.,0.,1.9,0.,2.]])
if __name__=='__main__':
parent_path = os.path.split(os.path.realpath(__file__))[0]
X_train, y_train, X_valid, y_valid = build_rnn_dataset(10)
RNNmodel=build_rnn(X_train.shape[-1] ,y_train.shape[-1], 100,20)
#是否加载pretrain model
#preweights=load_weights(parent_path+'/MLP_weightsMultispeed151226.hdf5')
#preweights.insert(1,np.zeros((100,100),dtype='float32'))#recurrent weight
#RNNmodel.set_weights(preweights)
RNNmodel.load_weights(parent_path+'/RNN_weights10REGU1.0.hdf5')
#train(X_train, y_train, X_valid, y_valid, RNNmodel, batch_size=128)
#下面这个函数里可以选择可视化哪个数据文件
testseq=loadgz('/Users/subercui/Git/BaikeBalance/RNN/dataset/testset.pkl.gz')
predictions=visual_test(RNNmodel,testseq[6000:8000])
#visual_test(RNNmodel,testseq)
'''可以调steps,optimizer'''
