persistent_chain = theano.shared(np.zeros((batch_size, n_hidden), dtype=theano.config.floatX), borrow=True)
# construct the RBM class
rbm = RBM(
input=x,
validation=test_set,
n_visible=n_visible,
n_labels=n_labels,
n_hidden=n_hidden,
np_rng=rng,
theano_rng=theano_rng,
)
# get the cost and the gradient corresponding to one step of CD-15
cost, updates = rbm.get_cost_updates(lr=learning_rate, persistent=persistent_chain, k=k)
accuracy = rbm.get_cv_error()
# make a prediction for an unlablled sample.
t_unlabelled = T.tensor3("unlabelled")
label, confidence = rbm.predict(t_unlabelled)
#%%========================================================================
# Training the RBM
# ==========================================================================
# it is ok for a theano function to have no output
# the purpose of train_rbm is solely to update the RBM parameters
train_rbm = theano.function(
[index],
cost,
updates=updates,
