DOC Correct or remove wrong documentation
@@ -94,11 +94,6 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
----------
activations : list, length = n_layers - 1
The ith element of the list holds the values of the ith layer.
-
- with_output_activation : bool, default True
- If True, the output passes through the output activation
- function, which is either the softmax function or the
- logistic function
"""
hidden_activation = ACTIVATIONS[self.activation]
# Iterate over the hidden layers
@@ -143,7 +138,7 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
Parameters
----------
- packed_parameters : array-like
+ packed_coef_inter : array-like
A vector comprising the flattened coefficients and intercepts.
X : {array-like, sparse matrix}, shape (n_samples, n_features)
@@ -162,7 +157,7 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
in each layer, where z = wx + b is the value of a particular layer
before passing through the activation function
- coef_grad : list, length = n_layers - 1
+ coef_grads : list, length = n_layers - 1
The ith element contains the amount of change used to update the
coefficient parameters of the ith layer in an iteration.
@@ -174,7 +169,6 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
-------
loss : float
grad : array-like, shape (number of nodes of all layers,)
-
"""
self._unpack(packed_coef_inter)
loss, coef_grads, intercept_grads = self._backprop(
@@ -206,7 +200,7 @@ class BaseMultilayerPerceptron(six.with_metaclass(ABCMeta, BaseEstimator)):
in each layer, where z = wx + b is the value of a particular layer
before passing through the activation function
- coef_grad : list, length = n_layers - 1
+ coef_grads : list, length = n_layers - 1
The ith element contains the amount of change used to update the
coefficient parameters of the ith layer in an iteration.
@@ -890,7 +884,6 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
optimization." arXiv preprint arXiv:1412.6980 (2014).
-
"""
def __init__(self, hidden_layer_sizes=(100,), activation="relu",
solver='adam', alpha=0.0001,
@@ -995,7 +988,7 @@ class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
y : array-like, shape (n_samples,)
The target values.
- classes : array, shape (n_classes)
+ classes : array, shape (n_classes), default None
Classes across all calls to partial_fit.
Can be obtained via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
@@ -1271,7 +1264,6 @@ class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):
Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
optimization." arXiv preprint arXiv:1412.6980 (2014).
-
"""
def __init__(self, hidden_layer_sizes=(100,), activation="relu",
solver='adam', alpha=0.0001,
