if __name__ == '__main__':
import keras.backend as K
from autoencoder import AutoEncoder
from dataset_wrapper import MnistWrapper
import utils
K.set_session(utils.create_session())
mnist_dataset = MnistWrapper.load_default()
# ----------normal relu pretraining----------
print 'Training model with normal relu'
folder = 'test/mnist_ae_relu_inf'
ae = AutoEncoder(mnist_dataset, encode, decode, None, folder)
ae.build_models()
num_epoch = 30
lr_schedule = utils.generate_decay_lr_schedule(num_epoch, 0.1, 1)
ae.train(128, num_epoch, lr_schedule)
ae.save_models()
ae.test_models(utils.vis_mnist)
ae.log()
encoded_dataset = ae.encode(MnistWrapper)
encoded_dataset.dump_to_h5(os.path.join(folder, 'encoded_mnist.h5'))
encoded_dataset.plot_data_dist(os.path.join(folder, 'encoded_plot.png'))
# ----------truncate relu and fine-tune----------
print 'Training model with relu-%d' % RELU_MAX
new_folder = 'test/mnist_ae_relu_%d' % RELU_MAX
trainer = RBMPretrainer(sess, dataset, rbm, decoder, vis_fn, output_dir)
trainer.train(train_config, sampler, sampler_generator)
trainer.dump_log(output_dir)
return output_dir
if __name__ == '__main__':
np.random.seed(66699)
sess = utils.create_session()
K.set_session(sess)
ae_folder = 'prod/cifar10_ae2_relu_%d' % cifar10_ae.RELU_MAX
ae = AutoEncoder(Cifar10Wrapper.load_default(), cifar10_ae.encode,
cifar10_ae.decode, cifar10_ae.RELU_MAX, ae_folder)
ae.build_models(ae_folder) # load model
encoded_dataset = Cifar10Wrapper.load_from_h5(
os.path.join(ae_folder, 'encoded_cifar10.h5'))
assert len(encoded_dataset.x_shape) == 1
num_hid = 2000
output_folder = os.path.join(ae_folder, 'test_pretrain')
# weights_file = os.path.join(
# output_folder, 'ptrbm_hid2000_lr0.1_cd1', 'epoch_100_rbm.h5')
weights_file = '/home/hhu/Developer/dem/prod/cifar10_ae2_relu_6/ptrbm_scheme0/ptrbm_hid2000_lr0.1_cd1/epoch_100_rbm.h5'
rbm = RBM(None, None, weights_file)
# rbm = RBM(encoded_dataset.x_shape[0], num_hid, None)
# train_config = utils.TrainConfig(
# lr=0.1, batch_size=100, num_epoch=100, use_pcd=False, cd_k=1)
if __name__ == '__main__':
import keras.backend as K
import numpy as np
from autoencoder import AutoEncoder
from dataset_wrapper import MnistWrapper
import mnist_ae
import dem_trainer
import hmc
import utils
sess = utils.create_session()
K.set_session(sess)
dataset = MnistWrapper.load_default()
ae = AutoEncoder(dataset, mnist_ae.encode, mnist_ae.decode,
mnist_ae.RELU_MAX, 'test/mnist_dem/ae')
ae.build_models('test/mnist_dem/ae') # load weights
l1_weights = ae.encoder.layers[1].get_weights()
print 'l1 weights sum: %s, bias sum: %s' % (
l1_weights[0].sum(), l1_weights[1].sum())
train_autoencoder = False
if train_autoencoder:
num_epoch = 10
lr_schedule = utils.generate_decay_lr_schedule(num_epoch, 0.1, 1)
ae.train(128, num_epoch, lr_schedule)
ae.save_models()
ae.test_models(utils.vis_mnist)
ae.log()
num_hid = 100
