def build_network(input_size,hidden_size,constraint_adj=False):
P = Parameters()
X = T.bmatrix('X')
P.W_input_hidden = U.initial_weights(input_size,hidden_size)
P.b_hidden = U.initial_weights(hidden_size)
P.b_output = U.initial_weights(input_size)
hidden_lin = T.dot(X,P.W_input_hidden)+P.b_hidden
hidden = T.nnet.sigmoid(hidden_lin)
output = T.nnet.softmax(T.dot(hidden,P.W_input_hidden.T) + P.b_output)
parameters = P.values()
cost = build_error(X,output,P)
if constraint_adj:pass
#cost = cost + adjacency_constraint(hidden_lin)
return X,output,cost,P
def build_network(input_size, hidden_size, constraint_adj=False):
P = Parameters()
X = T.bmatrix('X')
P.W_input_hidden = U.initial_weights(input_size, hidden_size)
P.b_hidden = U.initial_weights(hidden_size)
P.b_output = U.initial_weights(input_size)
hidden_lin = T.dot(X, P.W_input_hidden) + P.b_hidden
hidden = T.nnet.sigmoid(hidden_lin)
output = T.nnet.softmax(T.dot(hidden, P.W_input_hidden.T) + P.b_output)
parameters = P.values()
cost = build_error(X, output, P)
if constraint_adj: pass
#cost = cost + adjacency_constraint(hidden_lin)
return X, output, cost, P
