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)
train_config = utils.TrainConfig(lr=0.01,
batch_size=100,
num_epoch=200,
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