DOC Update attribute docstrings in _multilayer_perceptron.py
@@ -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.
@@ -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
@@ -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.
@@ -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
