![]() ![]() With the newly introduced native VTKPythonAlgorithmBase interface, it is no longer necessary. For generating such XML files, I used a Python package pvpyfilter. Previously before 5.6 only plugins defined by a XML file (or compiled library files) can be loaded. These plugins can then be loaded through Tools / Manage Plugins / Load New under ParaView and can be served as custom sources, readers, writers and filters. Recently I discoved the Python VTKPythonAlgorithmBase interface introduced in the ParaView 5.6 version for defining user plugins with pure Python files. costs.weight_decay(params=model.params,coeff=float(args))Ĭost = costs.adversarial_training(x,t,model.Generic mesh readers under ParaView via the new Python interface Model = FNN_MNIST(layer_sizes=layer_sizes)Ĭost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train)Ĭost = costs.cross_entropy_loss(x=x,t=t,forward_func=model.forward_train) \ Monitor_cost() if monitoring_cost_during_training or epoch_counter = n_epochs else NoneĬain_KLs_std = train_LDSs_stdĬlassifier.valid_KLs_std = valid_LDSs_stdĭataset = load_data.load_mnist_for_validation(n_v=int(args)) Ul_rand_ind = numpy.asarray(rng.permutation(ul_train_set_x.get_value().shape), dtype="int32") Rand_ind = numpy.asarray(rng.permutation(train_set_x.get_value().shape), dtype="int32") L_index = (l_index 1) if ((l_index 1) < numpy.int(n_train_batches)) else 0 ![]() Ul_index = (ul_index 1) if ((ul_index 1) < numpy.int(n_ul_train_batches)) else 0 Print "epoch:" str(epoch_counter) " train neg ll:" str(train_nlls) " valid neg ll:" str( ) " valid_KL:" str(valid_LDSs) " std:" str(valid_LDSs_std) Print "epoch:" str(epoch_counter) " train_KL:" str(train_LDSs) " std:" str( Valid_LDSs_std.append(numpy.std(validation_LDSs)) ![]() Train_LDSs_std.append(numpy.std(training_LDSs)) Inputs=, outputs=model_learning_rate, updates=".format(Įpoch_counter, this_training_errors, this_validation_errors, model_learning_rate.get_value(borrow=True) Model_learning_rate = theano.shared(numpy.asarray(initial_model_learning_rate, dtype=))ĭecay_model_learning_rate = theano.function( Raise ValueError("cost_type:" cost_type " is not defined") N_ul_train_batches = numpy.ceil((ul_train_set_x.get_value(borrow=True).shape) / numpy.float(m_ul_batch_size)) N_valid_batches = numpy.ceil((valid_set_x.get_value(borrow=True).shape) / numpy.float(m_batch_size)) N_train_batches = numpy.ceil((train_set_x.get_value(borrow=True).shape) / numpy.float(m_batch_size)) Train_set_x, train_set_y, ul_train_set_x = dataset Rng = (random_seed)ĭataset = load_mnist_for_validation(n_l=n_l, n_v=n_v, rng=rng) Monitoring_cost_during_training=False, # Monitoring transitions of cost during training. N_v=10000, # Number of validation samples.įull_train=False, # Training with all of training samples ( for evaluation on test samples ) Semi_supervised=False, # Experiment on semi-supervised learning or not. Norm_constraint="L2", # Specification of norm constraint. Num_power_iter=1, # Number of iterations of power method. 'mle' is no regularization, 'at' is Adversarial training, 'vat' is Virtual Adversarial training (ours)Įpsilon=0.05, # Norm constraint parameter. M_ul_batch_size=250, # Number of mini-batch size for calculation of LDS (semi-supervised learning only)Ĭost_type="vat", # Cost type. M_batch_size=100, # Number of mini-batch size. N_it_batches, # Number of parameter update of mini-batch stochastic gradient in each epoch. Learning_rate_decay, # Learning rate decay of ADAM. Initial_model_learning_rate, # Initial learning rate of ADAM. For example, layer_sizes = indicates 784 input nodes and 2 hidden layers and 1200 hidden nodes and 10 output nodes.Īctivations, # Specification of activation functions. Layer_sizes, # Layer sizes of neural network. ![]()
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