如何在SciKitLearn中为MLPRegressor确定隐藏层大小? [英] How is the hidden layer size determined for MLPRegressor in SciKitLearn?

问题描述

可以说我正在使用以下代码创建神经网络:

Lets say I'm creating a neural net using the following code:

from sklearn.neural_network import MLPRegressor

model = MLPRegressor(
  hidden_layer_sizes=(100,),
  activation='identity'
)
model.fit(X_train, y_train)

对于 hidden_​​layer_sizes ，我只需将其设置为默认值即可.但是，我不太了解它是如何工作的.我的定义中的隐藏层数是多少?是100吗?

For the hidden_layer_sizes, I simply set it to the default. However, I don't really understand how it works. What is the number of hidden layers in my definition? Is it 100?

推荐答案

来自文档:

hidden_​​layer_sizes:元组，长度= n_layers-2，默认值(100，)

hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)

第ith个元素代表第i个隐藏层中的神经元数量.

The ith element represents the number of neurons in the ith hidden layer.

它是 length = n_layers-2 ，因为您隐藏的层数是 n_layers 的总层数减去输入层的1减去输入层的1输出层.

It is length = n_layers - 2, because the number of your hidden layers is the total number of layers n_layers minus 1 for your input layer, minus 1 for your output layer.

在(默认)(100，)的情况下，这意味着一个隐藏的层，其中包含100个单位(神经元).

In your (default) case of (100,), it means one hidden layer of 100 units (neurons).

对于3个分别为100、50和25个单位的隐藏层，它将是

For 3 hidden layers of, say, 100, 50, and 25 units respectively, it would be

hidden_layer_sizes = (100, 50, 25)

请参阅文档中的示例(用于 MLPClassifier ，但逻辑相同).

See the example in the docs (it is for MLPClassifier, but the logic is identical).

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