def init(task, args_ranges):
"""Sets up the task and argument ranges"""
global _task_fun
global _task_args_ranges
global _num_tasks
_task_fun = task
_task_args_ranges = args_ranges
_num_tasks = num_ids_from_args(_task_args_ranges)
#n_hidden_layers = 1
#hidden_dim_mults = [16, 32, 64]
#for optimizer in ['Adam']:
##for optimizer in ['Adam', 'Adamax']:
##for optimizer in ['Adam', Adagrad']:
# algorithms.append('ae%d_%%sxR_%s' % (hidden_dim_mult, optimizer))
# algorithms.append('ae%d_%%sxR_dropout12_%s' % (hidden_dim_mult, optimizer))
# algorithms.append('ae%d_%%sxR_dropout25_%s' % (hidden_dim_mult, optimizer))
# algorithms.append('ae%d_%%sxR_batchnorm_%s' % (hidden_dim_mult, optimizer))
#fig_name_suffix = "_ae%d" % (n_hidden_layers)
args = (data_sets, input_dims, repr_dims)
#tiled = False
#tiled = (3, 2)
tiled = (num_ids_from_args((input_dims,)), num_ids_from_args((repr_dims,)))
figsize = (12.0, 9.0)
relative_to = None
#relative_to = 'pca'
if tiled:
plt.figure(figsize=(tiled[0]*figsize[0], tiled[1]*figsize[1]))
#for d, data_set in enumerate(data_sets):
# print("data = %s" % data_set)
# for i, input_dim in enumerate(input_dims):
# print(" input_dim = %s" % input_dim)
# for r, repr_dim in enumerate(repr_dims):
# print(" repr_dim = %d" % repr_dim)
# algorithms
algorithms = [
#'pca',
'ae1_64xR_dropout12_Adam',
'ae2_32xR_dropout12_Adam',
'ae3_32xR_dropout12_Adam',
]
fig_name_suffix = ""
args = (data_sets, repr_dims)
#tiled = False
#tiled = (3, 2)
tiled = (1, num_ids_from_args((repr_dims, )))
figsize = (12.0, 9.0)
relative_to = None
#relative_to = 'pca'
if tiled:
plt.figure(figsize=(tiled[0] * figsize[0], tiled[1] * figsize[1]))
for i in range(num_ids_from_args(args)):
(data_set, repr_dim) = args_from_id(i, args)
print("data = %s, repr_dim = %3d" % (data_set, repr_dim))
if tiled:
plt.subplot(tiled[1], tiled[0], i + 1)
else: