# %% Avoid issuing memory warning due to number of plots plt.rcParams.update({'figure.max_open_warning': 0}) # %% Load Iris data iris = XYDataset.from_eeyore('iris', yndmin=1, dtype=torch.float32, yonehot=True) dataloader = DataLoader(iris, batch_size=len(iris), shuffle=True) # %% Setup MLP model hparams = mlp.Hyperparameters(dims=[4, 3, 3], activations=[torch.sigmoid, None]) model = mlp.MLP(loss=loss_functions['multiclass_classification'], hparams=hparams, dtype=torch.float32) model.prior = Normal( torch.zeros(model.num_params(), dtype=model.dtype), (3 * torch.ones(model.num_params(), dtype=model.dtype)).sqrt()) # %% Setup MALA sampler sampler = MALA(model, theta0=model.prior.sample(), dataloader=dataloader, step=0.003) # %% Run MALA sampler
# %% Import packages import numpy as np import torch from torch.distributions import Normal from eeyore.constants import loss_functions from eeyore.models import mlp from bnn_mcmc_examples.examples.mlp.pima.setting2.constants import dtype, mlp_dims, mlp_bias, mlp_activations # %% Setup MLP model hparams = mlp.Hyperparameters(dims=mlp_dims, bias=mlp_bias, activations=mlp_activations) model = mlp.MLP(loss=loss_functions['binary_classification'], hparams=hparams, dtype=dtype) prior_scale = np.sqrt(10.) model.prior = Normal( torch.zeros(model.num_params(), dtype=model.dtype), torch.full([model.num_params()], prior_scale, dtype=model.dtype))
# %% Import packages import numpy as np import torch from torch.distributions import Normal from eeyore.constants import loss_functions from eeyore.models import mlp from bnn_mcmc_examples.examples.mlp.exact_xor.constants import dtype, mlp_dims # %% Setup MLP model hparams = mlp.Hyperparameters(dims=mlp_dims) model = mlp.MLP(loss=loss_functions['binary_classification'], hparams=hparams, dtype=dtype) prior_scale = np.sqrt(10.) model.prior = Normal( torch.zeros(model.num_params(), dtype=model.dtype), torch.full([model.num_params()], prior_scale, dtype=model.dtype))