I had tested deepGPy with my regression problem. The problem data has 15 feature columns. I tested both one-hidden layer GP and two-hidden layer GP that both provides almost identical mean predictions. The same problem was tested with both full GPR and sparse GPR. The results looked quite alright.
The network configuration for one-hidden layer is shown as follows:
--> Z is a m x 15 inducing set, m=20
D = X.shape[1] # which is 15
layer_X = InputLayerFixed(X, input_dim=D, output_dim=D, kern=GPy.kern.RBF(D, ARD=False), Z=Z, beta=0.01, name='layerX')
layer_Y = ObservedLayer(Y, input_dim=D, output_dim=1, kern=GPy.kern.RBF(D, ARD=False), Z=Z, beta=0.01, name='layerY')
layer_X.add_layer(layer_Y)
m = ColDeep([layer_X, layer_Y])
layer_X.Z.fix()
--> Predict
mu = m.predict_means(X_test)[0]
I wondered if there are something I did wrongly for my deep GP network?
Hi guys,
I had tested deepGPy with my regression problem. The problem data has 15 feature columns. I tested both one-hidden layer GP and two-hidden layer GP that both provides almost identical mean predictions. The same problem was tested with both full GPR and sparse GPR. The results looked quite alright.
The network configuration for one-hidden layer is shown as follows:
--> Z is a m x 15 inducing set, m=20 D = X.shape[1] # which is 15 layer_X = InputLayerFixed(X, input_dim=D, output_dim=D, kern=GPy.kern.RBF(D, ARD=False), Z=Z, beta=0.01, name='layerX') layer_Y = ObservedLayer(Y, input_dim=D, output_dim=1, kern=GPy.kern.RBF(D, ARD=False), Z=Z, beta=0.01, name='layerY') layer_X.add_layer(layer_Y) m = ColDeep([layer_X, layer_Y]) layer_X.Z.fix()
--> Predict mu = m.predict_means(X_test)[0]
I wondered if there are something I did wrongly for my deep GP network?
Many thanks!