if l_in[0].get_value() != 0. and aes_in == []: raise ValueError("You setup the l_in but no aes in found.") if l_out[0].get_value() != 0. and aes_out == []: raise ValueError("You setup the l_out but no aes out found.") # Train criterion cost_type = CostType.MeanSquared # CostType.MeanSquared # Compile the functions # Momentum(0.9, nesterov_momentum=False, # imagenet=False, imagenetDecay=5e-4, # max_colm_norm=False) train_updates, eval_fn = theano_fns( model, aes_in, aes_out, l_in, l_out, l_sup, l_code, lr, cost_type, updaters={ "all": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "in": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "out": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "code": None}, max_colm_norm=False, max_norm=15.0, eye=False) # How to update the weight costs updater_wc = StaticAnnealedWeightRate(anneal_end=500, anneal_start=0) updater_wc_in = StaticAnnealedWeightRateSingle(anneal_end=500, down=True, init_vl=0., end_vl=0., anneal_start=100)
# max_colm_norm=False) in3D = True train_updates, eval_fn = theano_fns(model, aes_in, aes_out, l_in, l_out, l_sup, l_code, lr, cost_type, updaters={ "all": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "in": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "out": Momentum(0.9, nesterov_momentum=False, imagenet=False, imagenetDecay=5e-4, max_colm_norm=False), "code":