def train(): """ This examples implements the variational autoencoder from the paper Auto-Encoding Variational Bayes by Diederik P Kingma, Max Welling, arXiv:1312.6114 """ # build dataset data = Mnist(batch_size=100, binary=False, train_valid_test_ratio=[5,1,1]) # for autoencoder, the output will be equal to input data.set_train(X=data.get_train().X, y=data.get_train().X) data.set_valid(X=data.get_valid().X, y=data.get_valid().X) # build model model = Sequential(input_var=T.matrix(), output_var=T.matrix()) model.add(VariationalAutoencoder(input_dim=28*28, bottlenet_dim=200, z_dim=20)) # build learning method learning_method = SGD(learning_rate=0.0001, momentum=0.9, lr_decay_factor=0.9, decay_batch=10000) # put everything into the train object train_object = TrainObject(model = model, log = None, dataset = data, train_cost = SGVB_bin, valid_cost = SGVB_bin, learning_method = learning_method, stop_criteria = {'max_epoch' : 10, 'epoch_look_back' : 5, 'percent_decrease' : 0.01} ) # finally run the code train_object.setup() train_object.run()
def train(): """ This examples implements the variational autoencoder from the paper Auto-Encoding Variational Bayes by Diederik P Kingma, Max Welling, arXiv:1312.6114 """ # build dataset data = Mnist(batch_size=100, binary=False, train_valid_test_ratio=[5,1,1]) # for autoencoder, the output will be equal to input data.set_train(X=data.get_train().X, y=data.get_train().X) data.set_valid(X=data.get_valid().X, y=data.get_valid().X) # build model model = Sequential(input_var=T.matrix(), output_var=T.matrix()) model.add(VariationalAutoencoder(input_dim=28*28, bottlenet_dim=200, z_dim=20)) # build learning method learning_method = SGD(learning_rate=0.0001, momentum=0.9, lr_decay_factor=0.9, decay_batch=10000) # put everything into the train object train_object = TrainObject(model = model, log = None, dataset = data, train_cost = SGVB_bin, valid_cost = SGVB_bin, learning_method = learning_method, stop_criteria = {'max_epoch' : 10, 'epoch_look_back' : 5, 'percent_decrease' : 0.01} ) # finally run the code train_object.setup() train_object.run()
def train(): # build dataset data = Mnist(batch_size=64, train_valid_test_ratio=[5, 1, 1]) # for autoencoder, the output will be equal to input data.set_train(X=data.get_train().X, y=data.get_train().X) data.set_valid(X=data.get_valid().X, y=data.get_valid().X) # build model model = Sequential(input_var=T.matrix(), output_var=T.matrix()) # build encoder model.add(Gaussian()) encode_layer1 = Linear(prev_dim=28 * 28, this_dim=200) model.add(encode_layer1) model.add(RELU()) encode_layer2 = Linear(prev_dim=200, this_dim=50) model.add(encode_layer2) model.add(Tanh()) # build decoder decode_layer1 = Linear(prev_dim=50, this_dim=200, W=encode_layer2.W.T) model.add(decode_layer1) model.add(RELU()) decode_layer2 = Linear(prev_dim=200, this_dim=28 * 28, W=encode_layer1.W.T) model.add(decode_layer2) model.add(Sigmoid()) # build learning method learning_method = AdaGrad(learning_rate=0.01, momentum=0.9, lr_decay_factor=0.9, decay_batch=10000) # put everything into the train object train_object = TrainObject(model=model, log=None, dataset=data, train_cost=entropy, valid_cost=entropy, learning_method=learning_method, stop_criteria={ 'max_epoch': 10, 'epoch_look_back': 5, 'percent_decrease': 0.01 }) # finally run the code train_object.setup() train_object.run()
def train(): # build dataset data = Mnist(batch_size=64, train_valid_test_ratio=[5,1,1]) # for autoencoder, the output will be equal to input data.set_train(X=data.get_train().X, y=data.get_train().X) data.set_valid(X=data.get_valid().X, y=data.get_valid().X) # build model model = Sequential() # build encoder model.add(Gaussian(input_var=T.matrix())) encode_layer1 = Linear(prev_dim=28*28, this_dim=200) model.add(encode_layer1) model.add(RELU()) encode_layer2 = Linear(prev_dim=200, this_dim=50) model.add(encode_layer2) model.add(Tanh()) # build decoder decode_layer1 = Linear(prev_dim=50, this_dim=200, W=encode_layer2.W.T) model.add(decode_layer1) model.add(RELU()) decode_layer2 = Linear(prev_dim=200, this_dim=28*28, W=encode_layer1.W.T) model.add(decode_layer2) model.add(Sigmoid()) # build learning method learning_method = AdaGrad(learning_rate=0.01, momentum=0.9, lr_decay_factor=0.9, decay_batch=10000) # put everything into the train object train_object = TrainObject(model = model, log = None, dataset = data, train_cost = entropy, valid_cost = entropy, learning_method = learning_method, stop_criteria = {'max_epoch' : 10, 'epoch_look_back' : 5, 'percent_decrease' : 0.01} ) # finally run the code train_object.setup() train_object.run()