nolearn用于多标签分类

Question

我尝试使用从nolearn包导入的DBN函数，这是我的代码：

from nolearn.dbn import DBN
import numpy as np
from sklearn import cross_validation

fileName = 'data.csv'
fileName_1 = 'label.csv'

data = np.genfromtxt(fileName, dtype=float, delimiter = ',')
label = np.genfromtxt(fileName_1, dtype=int, delimiter = ',')

clf = DBN(
    [data, 300, 10],
    learn_rates=0.3,
    learn_rate_decays=0.9,
    epochs=10,
    verbose=1,
    )

clf.fit(data,label)
score = cross_validation.cross_val_score(clf, data, label,scoring='f1', cv=10)
print score

由于我的数据的形状为（1231，229），标签的形状为（1231,13），因此标签集看起来像（[0 0 1 0 1 0 0 0 0 0 0 1 1 0]
…，[。 ..]），当我运行代码时，出现以下错误消息：输入形状错误（1231,13）。我想知道这里可能会发生两个问题：

1. DBN不支持多标签分类
2. 我的标签不适合在DBN适合功能中使用。

Answer 1

正如Francisco Vargas所提，nolearn.dbn已弃用，您应该改nolearn.lasagne而使用（如果可以的话）。

如果要在千层面中进行多标签分类，则应将regression参数设置为True，以定义验证分数和自定义损失。

这是一个例子：

import numpy as np
import theano.tensor as T
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
from nolearn.lasagne import BatchIterator
from lasagne import nonlinearities

# custom loss: multi label cross entropy
def multilabel_objective(predictions, targets):
    epsilon = np.float32(1.0e-6)
    one = np.float32(1.0)
    pred = T.clip(predictions, epsilon, one - epsilon)
    return -T.sum(targets * T.log(pred) + (one - targets) * T.log(one - pred), axis=1)


net = NeuralNet(
    # customize "layers" to represent the architecture you want
    # here I took a dummy architecture
    layers=[(layers.InputLayer, {"name": 'input', 'shape': (None, 1, 229, 1)}),

            (layers.DenseLayer, {"name": 'hidden1', 'num_units': 20}),
            (layers.DenseLayer, {"name": 'output', 'nonlinearity': nonlinearities.sigmoid, 'num_units': 13})], #because you have 13 outputs

    # optimization method:
    update=nesterov_momentum,
    update_learning_rate=5*10**(-3),
    update_momentum=0.9,

    max_epochs=500,  # we want to train this many epochs
    verbose=1,

    #Here are the important parameters for multi labels
    regression=True,

    objective_loss_function=multilabel_objective,
    custom_score=("validation score", lambda x, y: np.mean(np.abs(x - y)))

    )

net.fit(X_train, labels_train)