def run(nz, n_dense, dataset="mnist", dist_type="perturbed"):
"""
Constructs a tf.Graph and tf.Session to contain the instance of experiment
"""
with tf.Graph().as_default():
config = tf.ConfigProto(device_count={"CPU": 8},
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8)
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0"
with tf.Session(config=config) as sess:
arguments = {
# experiment def / paths
"dataset": dataset,
"data_dir": os.path.join(ROOT_DIR, "data"),
"category": -1,
"dist_type": dist_type,
"take_frac": 1,
# model def
"nz": nz,
"batch_size": 128,
"n_dense": n_dense,
"filters": (4, 8),
"stride": (1, 1),
"padding": ("same", "same"),
"kernel_size": (3, 3),
"k_samples": 5,
"sumK": 2,
"leafK": 2,
# learner def
"learning_rate": 0.01,
"validation_period": 128,
"strikes": 10
}
current_time = int(time.time())
save_dir = os.path.join(
MAIN_DIR, "sp_conviwae",
arguments["dataset"] + "_" + arguments["dist_type"],
str(arguments["category"]), str(current_time))
output = sp_conviwae.get_results(save_dir=save_dir, **arguments)
save_attributes(save_dir, arguments, output)
return output
def run(take_frac, dataset="mnist", dist_type="perturbed"):
"""
Constructs a tf.Graph and tf.Session to contain the instance of experiment
"""
if dataset == "mnist" and dist_type == "perturbed":
nnodes = (32, 32) # TODO
nz = 25
elif dataset == "mnist" and dist_type == "discrete":
nnodes = (32, 32) # TODO
nz = 25
elif dataset == "svhn" and dist_type == "perturbed":
nnodes = (32, 32) # TODO
nz = 50
elif dataset == "svhn" and dist_type == "discrete":
nnodes = (32, 32) # TODO
nz = 25
elif dataset == "cifar10" and dist_type == "perturbed":
nnodes = (32, 32) # TODO
nz = 25
elif dataset == "cifar10" and dist_type == "discrete":
nnodes = (32, 32) # TODO
nz = 25
else:
raise NotImplementedError
with tf.Graph().as_default():
config = tf.ConfigProto(device_count={"CPU": 8},
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8)
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0"
with tf.Session(config=config) as sess:
arguments = {
# experiment def / paths
"dataset": dataset,
"data_dir": os.path.join(ROOT_DIR, "data"),
"category": -1,
"dist_type": dist_type,
"take_frac": take_frac,
# model def
"nz": nz,
"batch_size": 128,
"nnodes_recog": nnodes,
"nnodes_gener": nnodes,
"k_samples": 5,
"sumK": 2,
"leafK": 2,
# learner def
"learning_rate": 0.01,
"validation_period": 128,
"strikes": 10
}
current_time = int(time.time())
save_dir = os.path.join(
MAIN_DIR, "sp_iwae",
arguments["dataset"] + "_" + arguments["dist_type"],
str(arguments["category"]), str(current_time))
output = sp_iwae.get_results(save_dir=save_dir, **arguments)
save_attributes(save_dir, arguments, output)
return output