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DOC Update attribute docstrings in _multilayer_perceptron.py #19595

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Apr 9, 2021
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DOC Update attribute docstrings in _multilayer_perceptron.py #19595

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3973245
Update _multilayer_perceptron.py
Shooter23 Mar 2, 2021
98d3dae
Merge remote-tracking branch 'upstream/main' into pr/19595
thomasjpfan Apr 8, 2021
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14 changes: 5 additions & 9 deletions sklearn/neural_network/_multilayer_perceptron.py
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Original file line number Diff line number Diff line change
Expand Up @@ -884,7 +884,7 @@ class MLPClassifier(ClassifierMixin, BaseMultilayerPerceptron):
layer i + 1.

n_iter_ : int
The number of iterations the solver has ran.
The number of iterations the solver has run.

n_layers_ : int
Number of layers.
Expand Down Expand Up @@ -1292,10 +1292,13 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
The minimum loss reached by the solver throughout fitting.

loss_curve_ : list of shape (`n_iter_`,)
Loss value evaluated at the end of each training step.
The ith element in the list represents the loss at the ith iteration.

t_ : int
The number of training samples seen by the solver during fitting.
Mathematically equals `n_iters * X.shape[0]`, it means
`time_step` and it is used by optimizer's learning rate scheduler.

coefs_ : list of shape (n_layers - 1,)
The ith element in the list represents the weight matrix corresponding
Expand All @@ -1306,7 +1309,7 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
layer i + 1.

n_iter_ : int
The number of iterations the solver has ran.
The number of iterations the solver has run.

n_layers_ : int
Number of layers.
Expand All @@ -1317,13 +1320,6 @@ class MLPRegressor(RegressorMixin, BaseMultilayerPerceptron):
out_activation_ : str
Name of the output activation function.

loss_curve_ : list of shape (n_iters,)
Loss value evaluated at the end of each training step.

t_ : int
Mathematically equals `n_iters * X.shape[0]`, it means
`time_step` and it is used by optimizer's learning rate scheduler.

Examples
--------
>>> from sklearn.neural_network import MLPRegressor
Expand Down