def checkpoint0(dataset):
if dataset == 'MNIST':
continuous=True
else:
RuntimeError('Case not implemented')
load_dataset_from_name(dataset)
model = VA(
numberOfInducingPoints, # Number of inducing ponts in sparse GP
batchSize, # Size of mini batch
dimX, # Dimensionality of the latent co-ordinates
dimZ, # Dimensionality of the latent variables
x_train, # [NxP] matrix of observations
kernelType=kernelType,
encoderType_qX=encoderType_qX, # 'FreeForm', 'MLP', 'Kernel'.
encoderType_rX=encoderType_rX, # 'FreeForm', 'MLP', 'Kernel', 'NoEncoding'.
encoderType_ru=encoderType_ru, # 'FreeForm', 'MLP', 'NoEncoding'
Xu_optimise=Xu_optimise,
numHiddenUnits_encoder=numHiddenUnits_encoder,
numHiddentUnits_decoder=numHiddentUnits_decoder,
continuous=continuous
)
model.construct_L_using_r()
model.setKernelParameters(0.01, 5*np.ones((2,)),
1e-100, 0.5,
[1e-10,1e-10], [10,10] )
model.randomise()
model.constructUpdateFunction()
model = model.randomise()
srng = utils.srng()
return model, srng
continuous=True ) va.randomise() # va.Xz.set_value() va.init_Xu_from_Xz() theta = np.ones((1,dimX+1))*10 va.setKernelParameters(theta) #va.printMemberTypes() #va.printSharedVariables() #va.printTheanoVariables() va.construct_L() va.constructUpdateFunction() print "Training" learning_rate = 1e-3 <<<<<<< HEAD numberOfEpochs = 5 ======= numberOfEpochs = 1 >>>>>>> 239b2283cdf928f132f53629f00311930d891f57 va.train(numberOfEpochs=numberOfEpochs, printDiagnostics=10) y_reconstr = va.create_new_data_function()
