MultiLayer Perceptron Classifier¶
Multilayer perceptron classifier
Configuration: 


Attributes: 

Inputs:  
Outputs: 

Ports:
Outputs:
model: model
Model
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’, noop 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 quasiNewton methods.
 ‘sgd’ refers to stochastic gradient descent.
 ‘adam’ refers to a stochastic gradientbased 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 two 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 two 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 stepsize 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
 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.
 warm_start
 When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
Some of the docstrings for this module have been automatically extracted from the scikitlearn library and are covered by their respective licenses.