python - Lasagne/nolearn autoencoder - 如何获得隐藏层输出？

Question

我使用 lasagne/nolearn 训练了一个自动编码器。假设网络层是 [500, 100, 100, 500]。我已经像这样训练了神经网络:

net.fit(X, X)

我想做如下的事情:

net.predict(X, layer=2)

所以我会得到我的数据的抑制表示。因此，如果我的初始数据的形状为 [10000, 500]，则结果数据将为 [10000, 100]。

我进行了搜索，但找不到该怎么做。千层面/nolearn 有可能吗？

Answer 1

看起来答案在文档中: http://lasagne.readthedocs.org/en/latest/user/layers.html#propagating-data-through-layers

以下是相关部分:

To compute the output of a network, you should instead call lasagne.layers.get_output() on it. This will traverse the network graph.

You can call this function with the layer you want to compute the output expression for:

>>> y = lasagne.layers.get_output(l_out)

In that case, a Theano expression will be returned that represents the output in function of the input variables associated with the lasagne.layers.InputLayer instance (or instances) in the network

...

You can also specify a Theano expression to use as input as a second argument to lasagne.layers.get_output():

>>> x = T.matrix('x')
>>> y = lasagne.layers.get_output(l_out, x)
>>> f = theano.function([x], y)

假设 net 是 nolearn.lasagne.NeuralNet 类型，看起来您可以使用 net.get_all_layers()。我没有在文档中看到它，但它是 here在第 592 行。