# Construct RBM
# -----------------------------------------------------------------------------
sampler = PersistentContrastiveDivergence(k=k,
hidden_activations=True,
continuous_output=True)
optimizer = Adam(learning_rate)
rbm = RBM(n_visible=vis,
n_hidden=hidd,
sampler=sampler,
optimizer=optimizer,
device=device,
vbias=vbias,
verbose=verbose)
pre_trained = os.path.isfile(model_dir)
if pre_trained:
rbm.load_state_dict(torch.load(model_dir))
# -----------------------------------------------------------------------------
# Training
# -----------------------------------------------------------------------------
if not pre_trained:
validation = data[:10000]
for _ in range(epochs):
train_loader = torch.utils.data.DataLoader(data,
batch_size=batch_size,
shuffle=True)
rbm.train(train_loader)
# A good measure of well-fitting is the free energy difference
# between some known and unknown instances. It is related to the
# log-likelihood difference, but it does not depend on the
# partition function. It should be around 0, and if it grows, it
