本文整理匯總了Python中mlp.MLP.kl_divergence方法的典型用法代碼示例。如果您正苦於以下問題：Python MLP.kl_divergence方法的具體用法？Python MLP.kl_divergence怎麽用？Python MLP.kl_divergence使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類mlp.MLP的用法示例。
在下文中一共展示了MLP.kl_divergence方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: build_network
# 需要導入模塊: from mlp import MLP [as 別名]
# 或者: from mlp.MLP import kl_divergence [as 別名]
def build_network(args, wordEmbeddings, L1_reg=0.00, L2_reg=1e-4):
print("Building model ...")
rng = np.random.RandomState(1234)
base_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(base_dir, 'data')
sick_dir = os.path.join(data_dir, 'sick')
rel_vocab_path = os.path.join(sick_dir, 'rel_vocab.txt')
rels = defaultdict(int)
with open(rel_vocab_path, 'r') as f:
for tok in f:
rels[tok.rstrip('\n')] += 1
rep_model = depTreeLSTMModel(args.lstmDim)
rep_model.initialParams(wordEmbeddings, rng=rng)
rnn_optimizer = RNN_Optimization(rep_model, alpha=args.step, optimizer=args.optimizer)
x = T.fmatrix('x') # n * d, the data is presented as one sentence output
y = T.fmatrix('y') # n * d, the target distribution\
classifier = MLP(rng=rng,input=x, n_in=2*args.lstmDim, n_hidden=args.hiddenDim,n_out=5)
cost = T.mean(classifier.kl_divergence(y)) + 0.5*L2_reg * classifier.L2_sqr
gparams = [ T.grad(cost, param) for param in classifier.params]
hw = classifier.params[0]
hb = classifier.params[1]
delta_x = theano.function([x,y], T.dot(hw, T.grad(cost, hb)), allow_input_downcast=True)
if args.optimizer == "sgd":
update_sdg = [
(param, param - args.step * gparam)
for param, gparam in zip(classifier.params, gparams)
]
update_params_theano = theano.function(inputs=[x,y], outputs=cost,
updates=update_sdg, allow_input_downcast=True)
elif args.optimizer == "adagrad":
grad_updates_adagrad = sgd_updates_adagrad(classifier.params, cost)
update_params_theano = theano.function(inputs=[x,y], outputs=cost,
updates=grad_updates_adagrad, allow_input_downcast=True)
elif args.optimizer == "adadelta":
grad_updates_adadelta = sgd_updates_adadelta(classifier.params, cost)
update_params_theano = theano.function(inputs=[x,y], outputs=cost,
updates=grad_updates_adadelta, allow_input_downcast=True)
elif args.optimizer == "adam":
grad_updates_adam = sgd_updates_adam(classifier.params, cost)
update_params_theano = theano.function(inputs=[x,y], outputs=cost,
updates=grad_updates_adam, allow_input_downcast=True)
else:
raise "Set optimizer"
cost_and_prob = theano.function([x, y], [cost, classifier.output], allow_input_downcast=True)
return rep_model, rnn_optimizer, update_params_theano, delta_x, cost_and_prob