from gptt_embed.projectors import FeatureTransformer, LinearProjector, Identity
from gptt_embed.gpc_runner import GPCRunner
with tf.Graph().as_default():
data_dir = "data/"
n_inputs = 10
mu_ranks = 10
D = 14
d = 10
projector = LinearProjector(D=D, d=d)
#projector = Identity(D=D)
C = 2
cov = SE_multidim(C, 0.7, 0.2, 0.1, projector)
lr = 5e-3
decay = (10, 0.2)
n_epoch = 20
batch_size = 200
data_type = 'numpy'
log_dir = 'log'
save_dir = None#'models/gpnn_100_100_4.ckpt'
model_dir = None#save_dir
load_model = False#True
runner=GPCRunner(data_dir, n_inputs, mu_ranks, cov,
lr=lr, decay=decay, n_epoch=n_epoch, batch_size=batch_size,
data_type=data_type, log_dir=log_dir, save_dir=save_dir,
model_dir=model_dir, load_model=load_model, batch_test=False)
runner.run_experiment()
C = 10
lr = 5e-3
decay = (30, 0.2)
n_epoch = 300
batch_size = 200
data_type = 'numpy'
log_dir = 'log'
save_dir = None #'models/gpnn_100_100_2.ckpt'
model_dir = None #save_dir
load_model = False #True
projector = NN(H1=64, H2=128, H3=512, H4=128, d=8)
cov = SE_multidim(C, 0.7, 0.2, 0.1, projector)
runner = GPCRunner(data_dir,
n_inputs,
mu_ranks,
cov,
lr=lr,
decay=decay,
n_epoch=n_epoch,
batch_size=batch_size,
preprocess_op=tr_preprocess_op,
te_preprocess_op=te_preprocess_op,
data_type=data_type,
log_dir=log_dir,
save_dir=save_dir,
model_dir=model_dir,
load_model=load_model)
runner.run_experiment()