.. _`Multi-Layer Perceptron Regressor`:

.. _`org.sysess.sympathy.machinelearning.mlp_regressor`:

Multi-Layer Perceptron Regressor
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: neuralnetwork.svg
   :width: 48


Multi-layer perceptron regressor



**Documentation**

Multi-layer perceptron regressor

*Configuration*:


  - *max_iter*

    Maximum number of iterations. The solver iterates until convergence
    (determined by 'tol') or this number of iterations. For stochastic
    solvers ('sgd', 'adam'), note that this determines the number of epochs
    (how many times each data point will be used), not the number of
    gradient steps.


  - *hidden_layer_sizes*

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


  - *activation*

    Activation function for the hidden layer.

    - 'identity', no-op activation, useful to implement linear bottleneck,
      returns f(x) = x

    - '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)


  - *solver*

    The solver for weight optimization.

    - 'lbfgs' is an optimizer in the family of quasi-Newton methods.

    - 'sgd' refers to stochastic gradient descent.

    - 'adam' refers to a stochastic gradient-based optimizer proposed by
      Kingma, Diederik, and Jimmy Ba

    Note: The default solver '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, 'lbfgs' can converge faster and perform
    better.


  - *batch_size*

    Size of minibatches for stochastic optimizers.
    If the solver is 'lbfgs', the classifier will not use minibatch.
    When set to "auto", `batch_size=min(200, n_samples)`


  - *learning_rate*

    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 solver='sgd'.


  - *shuffle*

    Whether to shuffle samples in each iteration. Only used when
    solver='sgd' or 'adam'.


  - *early_stopping*

    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
    ``n_iter_no_change`` consecutive epochs.
    Only effective when solver='sgd' or 'adam'


  - *validation_fraction*

    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


  - *alpha*

    L2 penalty (regularization term) parameter.


  - *tol*

    Tolerance for the optimization. When the loss or score is not improving
    by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,
    unless ``learning_rate`` is set to 'adaptive', convergence is
    considered to be reached and training stops.


  - *learning_rate_init*

    The initial learning rate used. It controls the step-size
    in updating the weights. Only used when solver='sgd' or 'adam'.


  - *power_t*

    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 solver='sgd'.


  - *momentum*

    Momentum for gradient descent update.  Should be between 0 and 1. Only
    used when solver='sgd'.


  - *nesterovs_momentum*

    Whether to use Nesterov's momentum. Only used when solver='sgd' and
    momentum > 0.


  - *beta_1*

    Exponential decay rate for estimates of first moment vector in adam,
    should be in [0, 1). Only used when solver='adam'


  - *beta_2*

    Exponential decay rate for estimates of second moment vector in adam,
    should be in [0, 1). Only used when solver='adam'


  - *epsilon*

    Value for numerical stability in adam. Only used when solver='adam'


  - *random_state*

    Determines random number generation for weights and bias
    initialization, train-test split if early stopping is used, and batch
    sampling when solver='sgd' or 'adam'.
    Pass an int for reproducible results across multiple function calls.
    See random_state.


  - *warm_start*

    When set to True, reuse the solution of the previous
    call to fit as initialization, otherwise, just erase the
    previous solution. See warm_start.




*Attributes*:


  - *loss_*

    The current loss computed with the loss function.


  - *coefs_*

    The ith element in the list represents the weight matrix corresponding
    to layer i.


  - *intercepts_*

    The ith element in the list represents the bias vector corresponding to
    layer i + 1.


  - *n_iter_*

    The number of iterations the solver has ran.


  - *n_layers_*

    Number of layers.


  - *n_outputs_*

    Number of outputs.


  - *out_activation_*

    Name of the output activation function.




*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_MLPRegressor
    :noindex:

.. class:: MLPRegressor
    :noindex: