vae.md

Variational Autoencoder

You can try the Variational Autoencoder Example running on Mnist.

import os

import theano
import theano.tensor as T
import numpy as np

from mozi.datasets.mnist import Mnist
from mozi.model import Sequential
from mozi.layers.vae import VariationalAutoencoder
from mozi.log import Log
from mozi.train_object import TrainObject
from mozi.cost import SGVB_bin
from mozi.learning_method import SGD

# 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()