from autoencoder import AutoEncoder
from dem_trainer import DEMTrainer
import cifar10_ae
import gibbs_sampler
import utils
import keras.backend as K
import os
import h5py
import numpy as np
np.random.seed(66699)
sess = utils.create_session()
K.set_session(sess)
dataset = Cifar10Wrapper.load_default()
ae_folder = 'prod/cifar10_ae3_relu_6/'
encoder_weights_file = os.path.join(ae_folder, 'encoder.h5')
decoder_weights_file = os.path.join(ae_folder, 'decoder.h5')
rbm_params_file = os.path.join(
ae_folder,
'ptrbm_scheme1/ptrbm_hid2000_lr0.001_pcd25/epoch_500_rbm.h5')
# encoder_weights_file = '/home/hhu/Developer/dem/prod/cifar10_ae3_relu_6/test_ae_fe_const_balance/epoch_500_encoder.h5'
# decoder_weights_file = encoder_weights_file.replace('encoder.', 'decoder.')
# rbm_params_file = encoder_weights_file.replace('encoder.', 'rbm.')
dem = DEM.load_from_param_files(dataset.x_shape, cifar10_ae.RELU_MAX,
cifar10_ae.encode, encoder_weights_file,
cifar10_ae.decode, decoder_weights_file,
rbm_params_file)
output_dir = os.path.join(parent_dir, rbm_dir)
train_config.dump_log(output_dir)
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)
def compare_dataset():
d1 = Cifar10Wrapper.load_from_h5('prod/test_relu6/encoded_cifar10.h5')
d2 = Cifar10Wrapper.load_from_h5(
'prod/cifar10_ae2_relu_6/encoded_cifar10.h5')
return d1, d2
import os
import keras
from keras.layers import Input
from keras.models import Model
from keras.utils.visualize_util import plot
from keras.callbacks import LearningRateScheduler
from keras.preprocessing.image import ImageDataGenerator
import utils
import autoencoder
from dataset_wrapper import Cifar10Wrapper
import cifar10_ae
def evalute_encoder(encode_fn, relu_max, weights_file, dataset, dataset_cls):
encoder = autoencoder.build_model(dataset.x_shape, relu_max, encode_fn,
None, weights_file)
encoded_train_xs = encoder.predict(dataset.train_xs)
encoded_test_xs = encoder.predict(dataset.test_xs)
encoded_dataset = dataset_cls(encoded_train_xs, dataset.train_ys,
encoded_test_xs, dataset.test_ys)
plot_path = weights_file + '.encoded_dataset.png'
encoded_dataset.plot_data_dist(plot_path)
if __name__ == '__main__':
evalute_encoder(cifar10_ae.encode, 6,
'prod/cifar10_new_ae768_relu6/encoder.h5',
Cifar10Wrapper.load_default(), Cifar10Wrapper)
default=None,
help='Where to store samples and models')
if __name__ == '__main__':
opt = parser.parse_args()
opt.manualSeed = 666999
print(opt)
np.random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
torch.cuda.manual_seed(opt.manualSeed)
assert opt.experiment is not None, 'specify output dir to avoid overwriting.'
if not os.path.exists(opt.experiment):
os.makedirs(opt.experiment)
print(opt, file=open(os.path.join(opt.experiment, 'configs.txt'), 'w'))
cudnn.benchmark = True
dataset = Cifar10Wrapper.load_default(opt.batch_size)
dem = DEM(opt)
sampler = Sampler(opt)
print(dem.net_f)
print(sampler.net_g)
opt.max_steps = 25
dem.train(opt, dataset, sampler)
# if opt.net_f and opt.net_g:
# dem.eval(dataset.train_xs, dataset.test_xs)
]},
}
if __name__ == '__main__':
np.random.seed(66699)
sess = utils.create_session()
K.set_session(sess)
data = 'cifar'
if data == 'cifar':
ae_folder = 'prod/cifar10_ae3_relu_%d' % cifar10_ae.RELU_MAX
# ae_folder = 'prod/cifar10_new_ae%d_relu%d' % (
# cifar10_ae.LATENT_DIM, cifar10_ae.RELU_MAX)
ae = AutoEncoder(Cifar10Wrapper.load_default(),
cifar10_ae.encode, cifar10_ae.decode,
cifar10_ae.RELU_MAX, ae_folder)
encoded_dataset = Cifar10Wrapper.load_from_h5(
os.path.join(ae_folder, 'encoded_cifar10.h5'))
elif data =='stl':
ae_folder = '/home/hengyuah/dem/prod/stl10_ae_1024_relu6'
ae = AutoEncoder(STL10Wrapper.load_default(),
stl_ae.encode, stl_ae.decode,
stl_ae.RELU_MAX, ae_folder)
encoded_dataset = STL10Wrapper.load_from_h5(
os.path.join(ae_folder, 'encoded_stl10.h5'))
ae.build_models(ae_folder) # load model
assert len(encoded_dataset.x_shape) == 1