sklearn.neural_network.MLPClassifier¶
- class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', algorithm='adam', alpha=0.0001, batch_size=200, learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)[source]¶
Multi-layer Perceptron classifier.
This algorithm optimizes the log-loss function using l-bfgs or gradient descent.
Parameters: hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
The ith element represents the number of neurons in the ith hidden layer.
activation : {‘logistic’, ‘tanh’, ‘relu’}, default ‘relu’
Activation function for the hidden layer.
- ‘logistic’, the logistic sigmoid function, returns f(x) = 1 / (1 + exp(-x)).
- ‘tanh’, the hyperbolic tan function, returns f(x) = tanh(x).
- ‘relu’, the rectified linear unit function, returns f(x) = max(0, x)
algorithm : {‘l-bfgs’, ‘sgd’, ‘adam’}, default ‘adam’
The algorithm for weight optimization.
- ‘l-bfgs’ is an optimization algorithm in the family of quasi-Newton methods.
- ‘sgd’ refers to stochastic gradient descent.
- ‘adam’ refers to a stochastic gradient-based optimization algorithm proposed by Kingma, Diederik, and Jimmy Ba
Note: The default algorithm ‘adam’ works pretty well on relatively large datasets (with thousands of training samples or more) in terms of both training time and validation score. For small datasets, however, ‘l-bfgs’ can converge faster and perform better.
alpha : float, optional, default 0.0001
L2 penalty (regularization term) parameter.
batch_size : int, optional, default 200
Size of minibatches for stochastic optimizers. If the algorithm is ‘l-bfgs’, the classifier will not use minibatch.
learning_rate : {‘constant’, ‘invscaling’, ‘adaptive’}, default ‘constant’
Learning rate schedule for weight updates.
- -‘constant’, is a constant learning rate given by
‘learning_rate_init’.
- -‘invscaling’ gradually decreases the learning rate learning_rate_ at
each time step ‘t’ using an inverse scaling exponent of ‘power_t’. effective_learning_rate = learning_rate_init / pow(t, power_t)
- -‘adaptive’, keeps the learning rate constant to
‘learning_rate_init’ as long as training loss keeps decreasing. Each time two consecutive epochs fail to decrease training loss by at least tol, or fail to increase validation score by at least tol if ‘early_stopping’ is on, the current learning rate is divided by 5.
Only used when algorithm=’sgd’.
max_iter : int, optional, default 200
Maximum number of iterations. The algorithm iterates until convergence (determined by ‘tol’) or this number of iterations.
random_state : int or RandomState, optional, default None
State or seed for random number generator.
shuffle : bool, optional, default True
Whether to shuffle samples in each iteration. Only used when algorithm=’sgd’ or ‘adam’.
tol : float, optional, default 1e-4
Tolerance for the optimization. When the loss or score is not improving by at least tol for two consecutive iterations, unless learning_rate is set to ‘adaptive’, convergence is considered to be reached and training stops.
learning_rate_init : double, optional, default 0.001
The initial learning rate used. It controls the step-size in updating the weights. Only used when algorithm=’sgd’ or ‘adam’.
power_t : double, optional, default 0.5
The exponent for inverse scaling learning rate. It is used in updating effective learning rate when the learning_rate is set to ‘invscaling’. Only used when algorithm=’sgd’.
verbose : bool, optional, default False
Whether to print progress messages to stdout.
warm_start : bool, optional, default False
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
momentum : float, default 0.9
Momentum for gradient descent update. Should be between 0 and 1. Only used when algorithm=’sgd’.
nesterovs_momentum : boolean, default True
Whether to use Nesterov’s momentum. Only used when algorithm=’sgd’ and momentum > 0.
early_stopping : bool, default False
Whether to use early stopping to terminate training when validation score is not improving. If set to true, it will automatically set aside 10% of training data as validation and terminate training when validation score is not improving by at least tol for two consecutive epochs. Only effective when algorithm=’sgd’ or ‘adam’
validation_fraction : float, optional, default 0.1
The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True
beta_1 : float, optional, default 0.9
Exponential decay rate for estimates of first moment vector in adam, should be in [0, 1). Only used when algorithm=’adam’
beta_2 : float, optional, default 0.999
Exponential decay rate for estimates of second moment vector in adam, should be in [0, 1). Only used when algorithm=’adam’
epsilon : float, optional, default 1e-8
Value for numerical stability in adam. Only used when algorithm=’adam’
Attributes: `classes_` : array or list of array of shape (n_classes,)
Class labels for each output.
`loss_` : float
The current loss computed with the loss function.
`label_binarizer_` : LabelBinarizer
A LabelBinarizer object trained on the training set.
`coefs_` : list, length n_layers - 1
The ith element in the list represents the weight matrix corresponding to layer i.
`intercepts_` : list, length n_layers - 1
The ith element in the list represents the bias vector corresponding to layer i + 1.
n_iter_ : int,
The number of iterations the algorithm has ran.
n_layers_ : int
Number of layers.
`n_outputs_` : int
Number of outputs.
`out_activation_` : string
Name of the output activation function.
Notes
MLPClassifier trains iteratively since at each time step the partial derivatives of the loss function with respect to the model parameters are computed to update the parameters.
It can also have a regularization term added to the loss function that shrinks model parameters to prevent overfitting.
This implementation works with data represented as dense and sparse numpy arrays of floating point values.
References
- Hinton, Geoffrey E.
- “Connectionist learning procedures.” Artificial intelligence 40.1 (1989): 185-234.
- Glorot, Xavier, and Yoshua Bengio. “Understanding the difficulty of
- training deep feedforward neural networks.” International Conference on Artificial Intelligence and Statistics. 2010.
- He, Kaiming, et al. “Delving deep into rectifiers: Surpassing human-level
- performance on imagenet classification.” arXiv preprint arXiv:1502.01852 (2015).
- Kingma, Diederik, and Jimmy Ba. “Adam: A method for stochastic
- optimization.” arXiv preprint arXiv:1412.6980 (2014).
Methods
decision_function(X) Decision function of the mlp model fit(X, y) Fit the model to data matrix X and target y. get_params([deep]) Get parameters for this estimator. predict(X) Predict using the multi-layer perceptron classifier predict_log_proba(X) Return the log of probability estimates. predict_proba(X) Probability estimates. score(X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of this estimator. - __init__(hidden_layer_sizes=(100, ), activation='relu', algorithm='adam', alpha=0.0001, batch_size=200, learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08)[source]¶
- decision_function(X)[source]¶
Decision function of the mlp model
Parameters: X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns: y : array-like, shape (n_samples,) or (n_samples, n_classes)
The values of decision function for each class in the model.
- fit(X, y)[source]¶
Fit the model to data matrix X and target y.
Parameters: X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
Returns: self : returns a trained MLP model.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
- partial_fit¶
Fit the model to data matrix X and target y.
Parameters: X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
classes : array, shape (n_classes)
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. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes.
Returns: self : returns a trained MLP model.
- predict(X)[source]¶
Predict using the multi-layer perceptron classifier
Parameters: X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns: y : array-like, shape (n_samples,) or (n_samples, n_classes)
The predicted classes.
- predict_log_proba(X)[source]¶
Return the log of probability estimates.
Parameters: X : array-like, shape (n_samples, n_features)
The input data.
Returns: log_y_prob : array-like, shape (n_samples, n_classes)
The predicted log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_. Equivalent to log(predict_proba(X))
- predict_proba(X)[source]¶
Probability estimates.
Parameters: X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns: y_prob : array-like, shape (n_samples, n_classes)
The predicted probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.
- score(X, y, sample_weight=None)[source]¶
Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
sample_weight : array-like, shape = [n_samples], optional
Sample weights.
Returns: score : float
Mean accuracy of self.predict(X) wrt. y.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
