def test_dropout_rbm_hidden_sampling():
new_dropout_rbm = dropout_rbm.DropoutRBM()
v = torch.ones(1, 128)
probs, states = new_dropout_rbm.hidden_sampling(v, scale=True)
assert probs.size(1) == 128
assert states.size(1) == 128
probs, states = new_dropout_rbm.hidden_sampling(v, scale=False)
assert probs.size(1) == 128
assert states.size(1) == 128
def test_dropout_rbm_p_setter():
new_dropout_rbm = dropout_rbm.DropoutRBM()
try:
new_dropout_rbm.p = -1
except:
new_dropout_rbm.p = 0
assert new_dropout_rbm.p == 0
try:
new_dropout_rbm.p = 'a'
except:
new_dropout_rbm.p = 0
assert new_dropout_rbm.p == 0
def test_dropout_rbm_reconstruct():
test = torchvision.datasets.KMNIST(
root='./data',
train=False,
download=True,
transform=torchvision.transforms.ToTensor())
new_dropout_rbm = dropout_rbm.DropoutRBM(n_visible=784,
n_hidden=128,
steps=1,
learning_rate=0.1,
momentum=0,
decay=0,
temperature=1,
dropout=0.5,
use_gpu=False)
e, v = new_dropout_rbm.reconstruct(test)
assert e >= 0
assert v.size(1) == 784
def test_dropout_rbm_p():
new_dropout_rbm = dropout_rbm.DropoutRBM()
assert new_dropout_rbm.p == 0.5