Lwta-theano
来自集智百科
目录 |
实验参数和结果
momentum 3layer lwta
参数初始值
def test_mlp(learning_rate=0.05, n_epochs=1000, momentum =0.9, dataset='../data/mnist.pkl.gz',n_col_size=2, batch_size=20, n_hidden=500):
每轮变化率
momentum = momentum + 0.0005 if learning_rate>0.01: learning_rate *= 0.95
momentum参数迭代更新机制
updates = [] for param, gparam, weight_update in zip(classifier.params, gparams, classifier.updates): upd = momentum * weight_update - (1-momentum) * learning_rate * gparam updates.append((weight_update, upd)) updates.append((param, param + upd))
实验结果
蓝色: valid_set_error,红色: test_set_error
Optimization complete. Best validation score of 1.670000 % obtained at iteration 165000, with test performance 1.620000 %
2013年11月1日,lwta-theano的效果已经达到1.4%