test_grid_local = ConfigGrid_TopoAE(
learning_rate=[1 / 1000],
batch_size=[1024],
n_epochs=[2],
weight_decay=[0],
early_stopping=[5],
rec_loss_weight=[1],
top_loss_weight=[1],
toposig_kwargs=[dict(match_edges='symmetric')],
model_class=[Autoencoder_MLP_topoae],
model_kwargs={
'input_dim': [101],
'latent_dim': [2],
'size_hidden_layers': [[32, 32]]
},
dataset=[Spheres()],
sampling_kwargs={'n_samples': [512]},
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=[''],
method_args=[None],
experiment_dir=
'/home/simonberg/PycharmProjects/MT-VAEs-TDA/output/test_simulator/TopoAE_testing_final_3',
seed=1,
device='cpu',
num_threads=2,
verbose=False)
ConfigGrid_TopoAE(
learning_rate=[1 / 10, 1 / 100, 1 / 1000],
batch_size=[64, 128, 256],
n_epochs=[1000],
weight_decay=[1e-6],
early_stopping=[32],
rec_loss_weight=[1],
top_loss_weight=[i for i in np.logspace(-5, 3, num=9, base=2.0)],
toposig_kwargs=[dict(match_edges='symmetric')],
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=[None],
experiment_dir='/cluster/scratch/schsimo/output/mnist_topoae_1_deepae4',
seed=seed,
device='cpu',
num_threads=1,
verbose=False) for seed in [838, 579, 1988]
spheres_euler_seed6_parallel_shuffled = [ConfigGrid_TopoAE(
learning_rate=[1/1000],
batch_size=random.sample([int(i) for i in np.logspace(3,12,num=10,base = 2.0)], 10),
n_epochs=[100],
weight_decay=[0],
early_stopping=[10],
rec_loss_weight=[1],
top_loss_weight=[j],
toposig_kwargs = [dict(match_edges = 'symmetric')],
model_class=[Autoencoder_MLP_topoae],
model_kwargs={
'input_dim' : [101],
'latent_dim' : [2],
'size_hidden_layers': [[32, 32]]
},
dataset=[Spheres()],
sampling_kwargs={
'n_samples': [512]
},
eval=[ConfigEval(
active = True,
evaluate_on = 'test',
save_eval_latent = True,
save_train_latent = True,
online_visualization = False,
k_min = 5,
k_max = 80,
k_step = 25,
)],
uid = [''],
experiment_dir='/cluster/home/schsimo/MT/output/TopoAE/Spheres/seed6',
seed = 6,
device = 'cpu',
num_threads=1,
verbose = False
) for j in [i for i in np.logspace(-4,8,num=13,base = 2.0)]]
ConfigGrid_TopoAE(
learning_rate=[1 / 1000],
batch_size=random.sample(
[int(i) for i in np.logspace(3, 9, num=7, base=2.0)], 7),
n_epochs=[1000],
weight_decay=[0],
early_stopping=[10],
rec_loss_weight=[1],
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(
unity_xytrans_1 = ConfigGrid_TopoAE(
learning_rate=[1 / 1000, 1 / 100, 1 / 10],
batch_size=[200, 400],
n_epochs=[5000],
weight_decay=[0],
early_stopping=[250],
rec_loss_weight=[1],
top_loss_weight=[1, 2, 4],
toposig_kwargs=[dict(match_edges='symmetric')],
model_class=[ConvAE_Unity480320],
model_kwargs=[dict()],
dataset=[Unity_XYTransOpenAI(version='xy_trans_l_newpers')],
sampling_kwargs=[dict(root_path='/cluster/scratch/schsimo')],
eval=[
ConfigEval(active=True,
evaluate_on='test',
eval_manifold=False,
save_eval_latent=True,
save_train_latent=True,
online_visualization=False,
k_min=5,
k_max=10,
k_step=5,
quant_eval=False)
],
uid=[''],
method_args=[dict(val_size=0)],
experiment_dir='/cluster/scratch/schsimo/output/TopoAE_xytrans1',
seed=1,
device='cpu',
num_threads=1,
verbose=False)
mnist_test_loc = ConfigGrid_TopoAE(
learning_rate=[1 / 1000],
batch_size=[
64,
],
n_epochs=[1],
weight_decay=[0],
early_stopping=[10],
rec_loss_weight=[1],
top_loss_weight=[1],
toposig_kwargs=[dict(match_edges='symmetric')],
model_class=[ConvAE_MNIST],
model_kwargs=[dict()],
dataset=[MNIST_offline()],
sampling_kwargs=[dict()],
eval=[
ConfigEval(
active=True,
evaluate_on='test',
save_eval_latent=True,
save_train_latent=False,
online_visualization=False,
k_min=10,
k_max=30,
k_step=5,
)
],
uid=[''],
method_args=[None],
experiment_dir=
'/Users/simons/PycharmProjects/MT-VAEs-TDA/output/TopoAE/MNIST/test',
seed=838,
device='cpu',
num_threads=1,
verbose=True)
TopoAE_sample_config = ConfigGrid_TopoAE(
learning_rate=[1 / 10, 1 / 100, 1 / 1000],
batch_size=[64],
n_epochs=[1000],
weight_decay=[1e-6],
early_stopping=[50],
rec_loss_weight=[1],
top_loss_weight=[1024],
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',
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=[None],
experiment_dir='output/sample',
seed=1,
device='cpu',
num_threads=1,
verbose=False)
ConfigGrid_TopoAE(
learning_rate=[1 / 10, 1 / 100, 1 / 1000],
batch_size=random.sample(
[int(i) for i in np.logspace(3, 9, num=7, base=2.0)], 7),
n_epochs=[1000],
weight_decay=[1e-6],
early_stopping=[32],
rec_loss_weight=[1],
top_loss_weight=[int(i) for i in np.logspace(9, 13, num=5, base=2.0)],
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=5,
k_max=20,
k_step=5,
)
],
uid=[''],
method_args=[None],
experiment_dir=
'/cluster/scratch/schsimo/output/TopoAE_swissroll_symmetric',
seed=seed,
device='cpu',
num_threads=1,
verbose=False)