def test_2Dexperiment():
c = Config()
c.batch_size = 200
c.n_epochs = 40
c.learning_rate = 0.001
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 200
# model-specific
c.n_coupling = 8
c.prior = 'gauss'
exp = SmileyExperiment(
c,
name='gauss',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
# sampling
samples = exp.model.sample(1000).cpu().numpy()
sns.jointplot(samples[:, 0], samples[:, 1])
plt.show()
def test_MNIST_experiment():
c = Config()
c.batch_size = 64
c.n_epochs = 50
c.learning_rate = 0.001
c.weight_decay = 5e-5
if torch.cuda.is_available():
c.use_cuda = True
else:
c.use_cuda = False
c.rnd_seed = 1
c.log_interval = 100
c.subset_size = 10
# model-specific
c.n_coupling = 8
c.n_filters = 64
exp = MNISTExperiment(
c,
name='mnist_test',
n_epochs=c.n_epochs,
seed=42,
base_dir='experiment_dir',
loggers={'visdom': ['visdom', {
"exp_name": "myenv"
}]})
exp.run()
exp.model.eval()
exp.model.to('cpu')
with torch.no_grad():
samples = exp.model.sample(16, device='cpu')
img_grid = make_grid(samples).permute((1, 2, 0))
plt.imshow(img_grid)
plt.show()
return exp.model