def dbn_train(NN,SD,opt):
lAmt=len(NN['layer'])
if 'maxStep' not in opt:
opt['maxStep']=50
### Train the 1st hidden layer(connected by input layer).
# Construct RBM from current layer of network.
rbm={}
rbm['W']=NN['layer'][2]['W']
rbm['vB']=np.zeros(1,NN['sizes'](1))
rbm['hB']=NN['layer'][2]['B']
# Training of RBM.
rbm=RBM.rbm_train(rbm,SD,opt) # input data as training data for 1st RBM
# Update RBM parameters to network.
NN['layer'][2]['W']=rbm['W']
NN['layer'][1]['B']=rbm['vB']
NN['layer'][2]['B']=rbm['hB']
# Propagate input to next layer.
X=RBM.rbm_up(rbm,SD) #stochastic propagation.
### Train other hidden layers.
for li in range(2,lAmt):
# Construct RBM from current layer of network.
rbm={}
rbm['W']=NN['layer'][li]['W']
rbm['vB']=NN['layer'][li-1]['B']
rbm['hB']=NN['layer'][li]['B']
# Training of RBM.
rbm=RBM.rbm_train(rbm,X,opt)
# Update RBM parameters to network.
NN['layer'][li]['W']=rbm['W']
NN['layer'][li-1]['B']=rbm['vB']
NN['layer'][li]['B']=rbm['hB']
# Propagate input to next layer.
X=RBM.rbm_up(rbm,X,True) #stochastic propagation.
return NN
