SciKitLearn 中 MLPRegressor 的隐藏层大小是如何确定的? [英] How is the hidden layer size determined for MLPRegressor in SciKitLearn?

问题描述

假设我正在使用以下代码创建一个神经网络:

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，我只是将其设置为默认值.但是，我真的不明白它是如何工作的.我的定义中隐藏层的数量是多少?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 (100,)

第 i 个元素表示第 i 个隐藏层的神经元数量.

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

它是length = n_layers - 2，因为你的隐藏层数是n_layers的总层数减去输入层的1，你的输入层减去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 (100,) 的(默认)情况下，它意味着一个 100 个单元(神经元)的隐藏层.

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

对于分别有 100、50 和 25 个单位的 3 个隐藏层，将是

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

hidden_layer_sizes = (100, 50, 25)

参见文档中的示例(它适用于MLPClassifier，但逻辑是一样的).

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

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