diff --git a/experiments/cifar/cifar_complementary.yaml b/experiments/cifar/cifar_complementary.yaml new file mode 100644 index 0000000..7bc8a9a --- /dev/null +++ b/experiments/cifar/cifar_complementary.yaml @@ -0,0 +1,114 @@ +--- +__object__: src.explib.base.ExperimentCollection +name: fashion_ablation +experiments: + - &exp_rad_logN + __object__: src.explib.hyperopt.HyperoptExperiment + name: cfair_full_radial_logN + skip: true + device: cuda + skip: true + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 1 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.explib.datasets.Cifar10Split + params: + space_to_depth_factor: 4 + dataloc: /home/faried/Projects/USFlows/data/cifar10 + + epochs: &epochs 200000 + patience: &patience 1 + batch_size: &batch_size + __eval__: tune.choice([32]) + optim_cfg: &optim + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-3 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([10]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 48 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [48, 8, 8] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: pyro.distributions.Normal + loc: + __eval__: torch.zeros([48, 8, 8]).to("cuda") + scale: + __eval__: torch.ones([48, 8, 8]).to("cuda") + - &exp_normal + __overwrites__: *exp_rad_logN + name: fashion_full_laplace + skip: false + trial_config: + optim_cfg: + params: + lr: + __eval__: 1e-5 + model_cfg: + params: + lu_transform: 0 + affine_conjugation: false + base_distribution: + __object__: src.usflows.distributions.RadialDistribution + device: cuda + p: + __eval__: float("1") + loc: + __eval__: torch.zeros([48, 8, 8]).to("cuda") + norm_distribution: + __exact__: + __object__: src.usflows.distributions.LogNormal + loc: + __eval__: torch.ones([1]).to("cuda") * 6 + scale: + __eval__: torch.ones([1]).to("cuda") * .35 + device: cuda + diff --git a/experiments/fashion/fashion_complementary.yaml b/experiments/fashion/fashion_complementary.yaml new file mode 100644 index 0000000..3c9faf9 --- /dev/null +++ b/experiments/fashion/fashion_complementary.yaml @@ -0,0 +1,114 @@ +--- +__object__: src.explib.base.ExperimentCollection +name: fashion_ablation +experiments: + - &exp_rad_logN + __object__: src.explib.hyperopt.HyperoptExperiment + name: cfair_full_radial_logN + skip: true + device: cuda + skip: true + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 1 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.explib.datasets.FashionMnistSplit + params: + space_to_depth_factor: 4 + dataloc: /home/faried/Projects/USFlows/data/fashion + + epochs: &epochs 200000 + patience: &patience 1 + batch_size: &batch_size + __eval__: tune.choice([32]) + optim_cfg: &optim + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-3 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([10]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 48 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [48, 8, 8] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: pyro.distributions.Normal + loc: + __eval__: torch.zeros([48, 8, 8]).to("cuda") + scale: + __eval__: torch.ones([48, 8, 8]).to("cuda") + + - &exp_normal + __overwrites__: *exp_rad_logN + name: fashion_full_laplace + skip: false + trial_config: + optim_cfg: + params: + lr: + __eval__: 1e-5 + model_cfg: + params: + lu_transform: 0 + affine_conjugation: false + base_distribution: + __object__: src.usflows.distributions.RadialDistribution + device: cuda + p: + __eval__: float("1") + loc: + __eval__: torch.zeros([48, 8, 8]).to("cuda") + norm_distribution: + __object__: src.usflows.distributions.LogNormal + loc: + __eval__: torch.ones([1]).to("cuda") * 6 + scale: + __eval__: torch.ones([1]).to("cuda") * .35 + device: cuda + diff --git a/experiments/fashion/fashionclasses_macow_complementary.yaml b/experiments/fashion/fashionclasses_macow_complementary.yaml new file mode 100644 index 0000000..617be19 --- /dev/null +++ b/experiments/fashion/fashionclasses_macow_complementary.yaml @@ -0,0 +1,149 @@ +--- +__object__: src.usflows.explib.base.ExperimentCollection +name: fashion_ablation_macow +experiments: + - &exp_rad_logN + __object__: src.usflows.explib.hyperopt.HyperoptExperiment + name: cfair_full_normal + skip: false + device: cuda + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 1 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.usflow.explib.datasets.FashionMnistSplit + params: + space_to_depth_factor: 4 + dataloc: /home/faried/Projects/USFlows/data/fashion + label: 0 + epochs: &epochs 200000 + patience: &patience 1 + batch_size: &batch_size + __eval__: tune.choice([32]) + optim_cfg: &optim + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-3 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([10]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 16 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [16, 7, 7] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: src.usflows.distributions.RadialDistribution + device: cuda + p: + __eval__: float("1") + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + norm_distribution: + __object__: src.usflows.distributions.GammaMM + concentration: + __eval__: torch.rand([20]).to("cuda") * 75 + rate: + __eval__: torch.rand([20]).to("cuda") + mixture_weights: + __eval__: torch.ones([20]).to("cuda") / 20 + device: cuda + - __overwrites__: *exp_rad_logN + name: fashion1_radial_logN + trial_config: + dataset: + params: + label: 1 + - __overwrites__: *exp_rad_logN + name: fashion2_radial_logN + trial_config: + dataset: + params: + label: 2 + - __overwrites__: *exp_rad_logN + name: fashion3_radial_logN + trial_config: + dataset: + params: + label: 3 + - __overwrites__: *exp_rad_logN + name: fashion4_radial_logN + trial_config: + dataset: + params: + label: 4 + - __overwrites__: *exp_rad_logN + name: fashion5_radial_logN + trial_config: + dataset: + params: + label: 5 + - __overwrites__: *exp_rad_logN + name: fashion6_radial_logN + trial_config: + dataset: + params: + label: 6 + - __overwrites__: *exp_rad_logN + name: fashion7_radial_logN + trial_config: + dataset: + params: + label: 7 + - __overwrites__: *exp_rad_logN + name: fashion8_radial_logN + trial_config: + dataset: + params: + label: 8 + - __overwrites__: *exp_rad_logN + name: fashion9_radial_logN + trial_config: + dataset: + params: + label: 9 + \ No newline at end of file diff --git a/experiments/fashion/fashionclasses_veriflow_complementary.yaml b/experiments/fashion/fashionclasses_veriflow_complementary.yaml new file mode 100644 index 0000000..482a871 --- /dev/null +++ b/experiments/fashion/fashionclasses_veriflow_complementary.yaml @@ -0,0 +1,140 @@ +--- +__object__: src.usflows.explib.base.ExperimentCollection +name: fashion_ablation_veriflow +experiments: + - &exp_rad_logN + __object__: src.usflows.explib.hyperopt.HyperoptExperiment + name: cfair_full_radial_logN + skip: false + device: cuda + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 0 + cpus_per_trial: &cpus_per_trial 1 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.usflows.explib.datasets.FashionMnistSplit + params: + space_to_depth_factor: 4 + dataloc: /home/faried/Projects/USFlows/data/fashion + label: 0 + epochs: &epochs 200000 + patience: &patience 1 + batch_size: &batch_size + __eval__: tune.choice([32]) + optim_cfg: &optim + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-3 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([10]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 16 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [16, 7, 7] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + + base_distribution: + __object__: pyro.distributions.Normal + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + scale: + __eval__: torch.ones([1]).to("cuda") + - __overwrites__: *exp_rad_logN + name: fashion1_radial_logN + trial_config: + dataset: + params: + label: 1 + - __overwrites__: *exp_rad_logN + name: fashion2_radial_logN + trial_config: + dataset: + params: + label: 2 + - __overwrites__: *exp_rad_logN + name: fashion3_radial_logN + trial_config: + dataset: + params: + label: 3 + - __overwrites__: *exp_rad_logN + name: fashion4_radial_logN + trial_config: + dataset: + params: + label: 4 + - __overwrites__: *exp_rad_logN + name: fashion5_radial_logN + trial_config: + dataset: + params: + label: 5 + - __overwrites__: *exp_rad_logN + name: fashion6_radial_logN + trial_config: + dataset: + params: + label: 6 + - __overwrites__: *exp_rad_logN + name: fashion7_radial_logN + trial_config: + dataset: + params: + label: 7 + - __overwrites__: *exp_rad_logN + name: fashion8_radial_logN + trial_config: + dataset: + params: + label: 8 + - __overwrites__: *exp_rad_logN + name: fashion9_radial_logN + trial_config: + dataset: + params: + label: 9 + \ No newline at end of file diff --git a/experiments/mnist/mnist_complementary.yaml b/experiments/mnist/mnist_complementary.yaml new file mode 100644 index 0000000..7f9e569 --- /dev/null +++ b/experiments/mnist/mnist_complementary.yaml @@ -0,0 +1,111 @@ +--- +__object__: src.explib.base.ExperimentCollection +name: mnist_ablation +experiments: + - &exp_rad_logN + __object__: src.explib.hyperopt.HyperoptExperiment + name: mnist_full_radial_logN + device: cuda + skip: False + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 16 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.explib.datasets.MnistSplit + params: + dataloc: /home/faried/Projects/USFlows/data/mnist + space_to_depth_factor: 4 + device: cuda + epochs: 200000 + patience: 5 + batch_size: + __eval__: tune.choice([32]) + optim_cfg: + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-3 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([15]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 16 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [16, 7, 7] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: pyro.distributions.Normal + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + scale: + __eval__: torch.ones([16, 7, 7]).to("cuda") + - &exp_normal + __overwrites__: *exp_rad_logN + name: mnist_full_MACow + skip: false + trial_config: + optim_cfg: + params: + lr: + __eval__: 1e-5 + model_cfg: + params: + lu_transform: 0 + affine_conjugation: false + base_distribution: + __object__: src.usflows.distributions.RadialDistribution + device: cuda + p: + __eval__: float("1") + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + norm_distribution: + __object__: src.usflows.distributions.LogNormal + loc: + __eval__: torch.ones([1]).to("cuda") * 6 + scale: + __eval__: torch.ones([1]).to("cuda") * .35 + device: cuda diff --git a/experiments/mnist/mnist_digits.yaml b/experiments/mnist/mnist_digits.yaml index ea6e996..c8511b9 100644 --- a/experiments/mnist/mnist_digits.yaml +++ b/experiments/mnist/mnist_digits.yaml @@ -85,11 +85,13 @@ experiments: loc: __eval__: torch.zeros([16, 7, 7]).to("cpu") norm_distribution: - __object__: src.usflows.distributions.LogNormal - loc: - __eval__: torch.ones([1]).to("cpu") * 6 - scale: - __eval__: torch.ones([1]).to("cpu") * .35 + __object__: src.usflows.distributions.GammaMM + concentration: + __eval__: torch.rand([20]).to("cpu") * 75 + rate: + __eval__: torch.rand([20]).to("cpu") + mixture_weights: + __eval__: torch.ones([20]).to("cpu") / 20 device: cpu - __overwrites__: *exp_rad_logN diff --git a/experiments/mnist/mnist_digits_complementary.yaml b/experiments/mnist/mnist_digits_complementary.yaml new file mode 100644 index 0000000..63acf9b --- /dev/null +++ b/experiments/mnist/mnist_digits_complementary.yaml @@ -0,0 +1,148 @@ +--- +__object__: src.explib.base.ExperimentCollection +name: mnist_gigits_logN +experiments: + - &exp_rad_logN + __object__: src.explib.hyperopt.HyperoptExperiment + name: mnist0 + device: cuda + skip: false + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 16 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.explib.datasets.MnistSplit + params: + dataloc: /home/faried/Projects/USFlows/data/mnist + space_to_depth_factor: 4 + device: cuda + digit: 0 + epochs: 200000 + patience: 5 + batch_size: + __eval__: tune.choice([32]) + optim_cfg: + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-4 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([5]) + lu_transform: 1 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 16 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [16, 7, 7] + affine_conjugation: true + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: pyro.distributions.Normal + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + scale: + __eval__: torch.ones([16, 7, 7]).to("cuda") + - + __overwrites__: *exp_rad_logN + name: mnist1 + trial_config: + dataset: + params: + digit: 1 + - + __overwrites__: *exp_rad_logN + name: mnist2 + trial_config: + dataset: + params: + digit: 2 + - + __overwrites__: *exp_rad_logN + name: mnist3 + trial_config: + dataset: + params: + digit: 3 + - + __overwrites__: *exp_rad_logN + name: mnist4 + trial_config: + dataset: + params: + digit: 4 + - + __overwrites__: *exp_rad_logN + name: mnist5 + trial_config: + dataset: + params: + digit: 5 + - + __overwrites__: *exp_rad_logN + name: mnist6 + trial_config: + dataset: + params: + digit: 6 + - + __overwrites__: *exp_rad_logN + name: mnist7 + trial_config: + dataset: + params: + digit: 7 + - + __overwrites__: *exp_rad_logN + name: mnist8 + trial_config: + dataset: + params: + digit: 8 + - + __overwrites__: *exp_rad_logN + name: mnist9 + trial_config: + dataset: + params: + digit: 9 diff --git a/experiments/mnist/mnist_digits_macow_complementary.yaml b/experiments/mnist/mnist_digits_macow_complementary.yaml new file mode 100644 index 0000000..aa15da4 --- /dev/null +++ b/experiments/mnist/mnist_digits_macow_complementary.yaml @@ -0,0 +1,158 @@ +--- +__object__: src.explib.base.ExperimentCollection +name: mnist_gigits_logN +experiments: + - &exp_rad_logN + __object__: src.explib.hyperopt.HyperoptExperiment + name: mnist0 + device: cuda + skip: false + scheduler: &scheduler + __object__: ray.tune.schedulers.ASHAScheduler + max_t: 1000000 + grace_period: 1000000 + reduction_factor: 2 + num_hyperopt_samples: &num_hyperopt_samples 1 + gpus_per_trial: &gpus_per_trial 1 + cpus_per_trial: &cpus_per_trial 16 + tuner_params: &tuner_params + metric: val_loss + mode: min + trial_config: + logging: + images: false + "image_shape": [28, 28] + dataset: &dataset + class: + __class__: src.explib.datasets.MnistSplit + params: + dataloc: /home/faried/Projects/USFlows/data/mnist + space_to_depth_factor: 4 + device: cuda + digit: 0 + epochs: 200000 + patience: 5 + batch_size: + __eval__: tune.choice([32]) + optim_cfg: + optimizer: + __class__: src.usflows.sophia.SophiaG + params: + lr: + __eval__: 1e-4 + weight_decay: 0.0 + + model_cfg: + type: + __class__: src.usflows.flows.USFlow + params: + soft_training: + __eval__: tune.choice([False]) + training_noise_prior: + __object__: pyro.distributions.Uniform + low: + __eval__: 1e-20 + high: 0.01 + prior_scale: 1.0 + coupling_blocks: + __eval__: tune.choice([5]) + lu_transform: 0 + householder: 0 + conditioner_cls: + __class__: src.usflows.networks.ConvNet2D + conditioner_args: + c_in: 16 + c_hidden: + __eval__: tune.choice([32]) + num_layers: + __eval__: tune.choice([3]) + padding: same + kernel_size: 3 + rescale_hidden: 1 + normalize_layers: + __eval__: tune.choice([True]) + gating: + __eval__: tune.choice([True]) + in_dims: [16, 7, 7] + affine_conjugation: false + nonlinearity: + __eval__: tune.choice([torch.nn.ReLU()]) + base_distribution: + __object__: src.usflows.distributions.RadialDistribution + device: cuda + p: + __eval__: float("1") + loc: + __eval__: torch.zeros([16, 7, 7]).to("cuda") + norm_distribution: + __object__: src.usflows.distributions.GammaMM + concentration: + __eval__: torch.rand([20]).to("cuda") * 75 + rate: + __eval__: torch.rand([20]).to("cuda") + mixture_weights: + __eval__: torch.ones([20]).to("cuda") / 20 + device: cuda + - + __overwrites__: *exp_rad_logN + name: mnist1 + trial_config: + dataset: + params: + digit: 1 + - + __overwrites__: *exp_rad_logN + name: mnist2 + trial_config: + dataset: + params: + digit: 2 + - + __overwrites__: *exp_rad_logN + name: mnist3 + trial_config: + dataset: + params: + digit: 3 + - + __overwrites__: *exp_rad_logN + name: mnist4 + trial_config: + dataset: + params: + digit: 4 + - + __overwrites__: *exp_rad_logN + name: mnist5 + trial_config: + dataset: + params: + digit: 5 + - + __overwrites__: *exp_rad_logN + name: mnist6 + trial_config: + dataset: + params: + digit: 6 + - + __overwrites__: *exp_rad_logN + name: mnist7 + trial_config: + dataset: + params: + digit: 7 + - + __overwrites__: *exp_rad_logN + name: mnist8 + trial_config: + dataset: + params: + digit: 8 + - + __overwrites__: *exp_rad_logN + name: mnist9 + trial_config: + dataset: + params: + digit: 9