top_loss_weight=1,
model_class=Autoencoder_MLP,
model_kwargs={
'input_dim' : 101,
'latent_dim' : 2,
'size_hidden_layers': [128, 64, 32]
},
dataset=Spheres(),
sampling_kwargs={
'n_samples': 500
},
eval=ConfigEval(
active=True,
evaluate_on='test',
save_eval_latent=True,
save_train_latent=True,
online_visualization=True,
k_min=5,
k_max=105,
k_step=25,
),
uid = '',
)
test_grid_local = ConfigGrid_COREL(
learning_rate=[1/1000],
batch_size=[64],
n_epochs=[20],
weight_decay=[10e-5],
early_stopping=[5],
rec_loss=[L1Loss()],
top_loss=[L1Loss()],
tmp = tmp[k]
tmp[kc[-1]] = kc_v
ret.append(Config_Competitors(**ret_i))
return ret
placeholder_config_competitors = Config_Competitors(
model_class=tSNE,
model_kwargs=dict(),
dataset=SwissRoll(),
sampling_kwargs={'n_samples': [2560]},
eval=[
ConfigEval(
active=True,
evaluate_on=None,
save_eval_latent=True,
save_train_latent=True,
online_visualization=False,
k_min=5,
k_max=20,
k_step=5,
)
],
uid='uid',
verbose=False,
experiment_dir='',
seed=123123,
)
batch_size=[64, 128, 256, 512],
n_epochs=[1000],
weight_decay=[1e-6],
early_stopping=[32],
model_class=[DeepAE_MNIST_4D],
model_kwargs=[dict()],
dataset=[MNIST_offline()],
sampling_kwargs=[
dict(root_path='/cluster/home/schsimo/MT/AEs-VAEs-TDA')
],
eval=[
ConfigEval(active=True,
evaluate_on='test',
eval_manifold=False,
save_eval_latent=False,
save_train_latent=False,
online_visualization=False,
quant_eval=True,
k_min=4,
k_max=16,
k_step=4)
],
uid=[''],
method_args=[dict()],
experiment_dir='/cluster/scratch/schsimo/output/mnist_ae_1_deepae4',
seed=seed,
device='cpu',
num_threads=1,
verbose=False) for seed in [838, 579, 1988]
]
mnist_1_deepae4_list = list(
model_class=[Autoencoder_MLP_topoae],
model_kwargs={
'input_dim' : [3],
'latent_dim' : [2],
'size_hidden_layers': [[32, 32]]
},
dataset=[SwissRoll()],
sampling_kwargs={
'n_samples': [2560]
},
eval=[ConfigEval(
active=True,
evaluate_on='test',
eval_manifold=True,
save_eval_latent=True,
save_train_latent=True,
online_visualization=False,
k_min=15,
k_max=45,
k_step=15,
)],
uid=[''],
method_args=[dict()],
experiment_dir='/Users/simons/PycharmProjects/MT-VAEs-TDA/output/vanillaAE/test',
seed=1,
device='cpu',
num_threads=1,
verbose=True,
)
rec_loss_weight=[1],
top_loss_weight=[i for i in np.logspace(-2, -1, num=2, base=2.0)],
match_edges=['push_active'],
k=[1, 2, 3, 4, 5, 6],
r_max=[10],
model_class=[ConvAE_MNIST],
model_kwargs=[dict()],
dataset=[MNIST_offline()],
sampling_kwargs=[dict(root_path='/cluster/home/schsimo/MT/AEs-VAEs-TDA')],
eval=[
ConfigEval(
active=False,
evaluate_on='test',
eval_manifold=False,
save_eval_latent=True,
save_train_latent=False,
online_visualization=False,
k_min=5,
k_max=45,
k_step=5,
)
],
uid=[''],
toposig_kwargs=[dict()],
method_args=dict(n_jobs=[1],
normalize=[True],
mu_push=[1, 1.125],
online_wc=[True],
wc_offline=[dict(path_to_data=wcpath_mnist_s838_1024)]),
experiment_dir='/cluster/scratch/schsimo/output/mnist1024_2',
seed=838,
top_loss_weight=[tlw],
toposig_kwargs=[dict(match_edges='symmetric')],
model_class=[Autoencoder_MLP_topoae],
model_kwargs={
'input_dim': [3],
'latent_dim': [2],
'size_hidden_layers': [[32, 32]]
},
dataset=[SwissRoll()],
sampling_kwargs={'n_samples': [2560]},
eval=[
ConfigEval(
active=True,
evaluate_on='test',
save_eval_latent=True,
save_train_latent=True,
online_visualization=False,
k_min=10,
k_max=30,
k_step=5,
)
],
uid=[''],
method_args=[None],
experiment_dir=
'/cluster/home/schsimo/MT/output/TopoAE/SwissRoll/multiseed_asymmetric',
seed=seed,
device='cpu',
num_threads=1,
verbose=False) for tlw, seed in zip(
list(np.repeat([i for i in np.logspace(6, 13, num=8, base=2.0)],
4)), [6, 34, 79, 102] * 8)
