print '... building the model'
global MLPmodel
if MLPmodel==None:
MLPmodel=mf.build_mlp(5,1, 100,20)
parent_path = os.path.split(os.path.realpath(__file__))[0]
MLPmodel.load_weights(parent_path+'/MLP_weightsBest.hdf5')
return MLPmodel
MLPmodel=init()
def mean_square_error(predictions, targets):
return np.square(predictions - targets).mean(axis=0)
def absolute_percent_error(predictions, targets, targets_mean):
return (np.abs(predictions - targets) / np.abs(targets_mean)).mean(axis=0)
def absolute_error(predictions, targets):
return np.abs(predictions - targets).mean(axis=0)
def test(array):
#array=np.array([[ 0.09167325, 0.006 ,0,0,0]])
array=mf.normalize(array)
return MLPmodel.predict(array,array.shape[0])
if __name__=='__main__':
parent_path = os.path.split(os.path.realpath(__file__))[0]
array=np.array([[0.,0.,1.9,0.,2.0]])
print test(array)
mf.visual_test(MLPmodel)
if not MLPmodel.path==weight:
MLPmodel=mf.build_mlp(5,1, 100,20)
MLPmodel.path=weight
parent_path = os.path.split(os.path.realpath(__file__))[0]
MLPmodel.load_weights(parent_path+'/'+weight)
return MLPmodel
def mean_square_error(predictions, targets):
return np.square(predictions - targets).mean(axis=0)
def absolute_percent_error(predictions, targets, targets_mean):
return (np.abs(predictions - targets) / np.abs(targets_mean)).mean(axis=0)
def absolute_error(predictions, targets):
return np.abs(predictions - targets).mean(axis=0)
def test(array,weightfile='MLP_weightsMultispeed151226.hdf5'):
#array=np.array([[ 0.09167325, 0.006 ,0,0,0]])
MLPmodel=init(weight=weightfile)
array=mf.normalize(array)
return MLPmodel.predict(array,array.shape[0])
if __name__=='__main__':
parent_path = os.path.split(os.path.realpath(__file__))[0]
array=np.array([[0.,20.,1.9,0.,2.0]])
print test(array)
testset=loadgz(parent_path+'/dataset/testset.pkl.gz')
mf.visual_test(MLPmodel,sequence=testset[6000:8000])
