python sklearn.neural_network.MLPClassifier() 神经网络改变模型复杂度的四种方法

• MLPClassifier() 改变模型复杂度的四种方法

1. 调整神经网络每一个隐藏层上的节点数
2. 调节神经网络隐藏层的层数
3. 调节activation的方式
4. 通过调整alpha值来改变模型正则化的程度(增大alpha会降低模型复杂度, 模型会变得更加简单)

官方doc:

Init signature: 
MLPClassifier(
    hidden_layer_sizes=(100,),
    activation='relu',
    solver='adam',
    alpha=0.0001,
    batch_size='auto',
    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,
    n_iter_no_change=10,
)
Docstring:     
Multi-layer Perceptron classifier.

This model optimizes the log-loss function using LBFGS or stochastic
gradient descent.

.. versionadded:: 0.18

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 : {'identity', 'logistic', 'tanh', 'relu'}, default 'relu'
    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 : {'lbfgs', 'sgd', 'adam'}, default 'adam'
    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.

alpha : float, optional, default 0.0001
    L2 penalty (regularization term) parameter.

batch_size : int, optional, default 'auto'
    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 : {'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 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'``.

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 solver='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 solver='sgd'.

max_iter : int, optional, default 200
    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.

shuffle : bool, optional, default True
    Whether to shuffle samples in each iteration. Only used when
    solver='sgd' or 'adam'.

random_state : int, RandomState instance or None, optional, default None
    If int, random_state is the seed used by the random number generator;
    If RandomState instance, random_state is the random number generator;
    If None, the random number generator is the RandomState instance used
    by `np.random`.

tol : float, optional, default 1e-4
    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.

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. See :term:`the Glossary `.

momentum : float, default 0.9
    Momentum for gradient descent update. Should be between 0 and 1. Only
    used when solver='sgd'.

nesterovs_momentum : boolean, default True
    Whether to use Nesterov's momentum. Only used when solver='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
    ``n_iter_no_change`` consecutive epochs. The split is stratified,
    except in a multilabel setting.
    Only effective when solver='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 solver='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 solver='adam'

epsilon : float, optional, default 1e-8
    Value for numerical stability in adam. Only used when solver='adam'

n_iter_no_change : int, optional, default 10
    Maximum number of epochs to not meet ``tol`` improvement.
    Only effective when solver='sgd' or 'adam'

    .. versionadded:: 0.20

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.

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 solver 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 numpy arrays or
sparse scipy 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).
File:           c:\users\huawei\appdata\local\programs\python\python36\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py
Type:           ABCMeta
Subclasses: