Skip to content
Permalink
Branch: master
Find file Copy path
Find file Copy path
1323 lines (1061 sloc) 51.4 KB
"""Multi-layer Perceptron
"""
# Authors: Issam H. Laradji <issam.laradji@gmail.com>
# Andreas Mueller
# Jiyuan Qian
# License: BSD 3 clause
import numpy as np
from abc import ABCMeta, abstractmethod
from scipy.optimize import fmin_l_bfgs_b
import warnings
from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
from ..base import is_classifier
from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
from ._stochastic_optimizers import SGDOptimizer, AdamOptimizer
from ..model_selection import train_test_split
from ..preprocessing import LabelBinarizer
from ..utils import gen_batches, check_random_state
from ..utils import shuffle
from ..utils import check_array, check_X_y, column_or_1d
from ..exceptions import ConvergenceWarning
from ..utils.extmath import safe_sparse_dot
from ..utils.validation import check_is_fitted
from ..utils.multiclass import _check_partial_fit_first_call, unique_labels
from ..utils.multiclass import type_of_target
_STOCHASTIC_SOLVERS = ['sgd', 'adam']
def _pack(coefs_, intercepts_):
"""Pack the parameters into a single vector."""
return np.hstack([l.ravel() for l in coefs_ + intercepts_])
class BaseMultilayerPerceptron(BaseEstimator, metaclass=ABCMeta):
"""Base class for MLP classification and regression.
Warning: This class should not be used directly.
Use derived classes instead.
.. versionadded:: 0.18
"""
@abstractmethod
def __init__(self, hidden_layer_sizes, activation, solver,
alpha, batch_size, learning_rate, learning_rate_init, power_t,
max_iter, loss, shuffle, random_state, tol, verbose,
warm_start, momentum, nesterovs_momentum, early_stopping,
validation_fraction, beta_1, beta_2, epsilon,
n_iter_no_change):
self.activation = activation
self.solver = solver
self.alpha = alpha
self.batch_size = batch_size
self.learning_rate = learning_rate
self.learning_rate_init = learning_rate_init
self.power_t = power_t
self.max_iter = max_iter
self.loss = loss
self.hidden_layer_sizes = hidden_layer_sizes
self.shuffle = shuffle
self.random_state = random_state
self.tol = tol
self.verbose = verbose
self.warm_start = warm_start
self.momentum = momentum
self.nesterovs_momentum = nesterovs_momentum
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.n_iter_no_change = n_iter_no_change
def _unpack(self, packed_parameters):
"""Extract the coefficients and intercepts from packed_parameters."""
for i in range(self.n_layers_ - 1):
start, end, shape = self._coef_indptr[i]
self.coefs_[i] = np.reshape(packed_parameters[start:end], shape)
start, end = self._intercept_indptr[i]
self.intercepts_[i] = packed_parameters[start:end]
def _forward_pass(self, activations):
"""Perform a forward pass on the network by computing the values
of the neurons in the hidden layers and the output layer.
Parameters
----------
activations : list, length = n_layers - 1
The ith element of the list holds the values of the ith layer.
"""
hidden_activation = ACTIVATIONS[self.activation]
# Iterate over the hidden layers
for i in range(self.n_layers_ - 1):
activations[i + 1] = safe_sparse_dot(activations[i],
self.coefs_[i])
activations[i + 1] += self.intercepts_[i]
# For the hidden layers
if (i + 1) != (self.n_layers_ - 1):
activations[i + 1] = hidden_activation(activations[i + 1])
# For the last layer
output_activation = ACTIVATIONS[self.out_activation_]
activations[i + 1] = output_activation(activations[i + 1])
return activations
def _compute_loss_grad(self, layer, n_samples, activations, deltas,
coef_grads, intercept_grads):
"""Compute the gradient of loss with respect to coefs and intercept for
specified layer.
This function does backpropagation for the specified one layer.
"""
coef_grads[layer] = safe_sparse_dot(activations[layer].T,
deltas[layer])
coef_grads[layer] += (self.alpha * self.coefs_[layer])
coef_grads[layer] /= n_samples
intercept_grads[layer] = np.mean(deltas[layer], 0)
return coef_grads, intercept_grads
def _loss_grad_lbfgs(self, packed_coef_inter, X, y, activations, deltas,
coef_grads, intercept_grads):
"""Compute the MLP loss function and its corresponding derivatives
with respect to the different parameters given in the initialization.
Returned gradients are packed in a single vector so it can be used
in lbfgs
Parameters
----------
packed_coef_inter : array-like
A vector comprising the flattened coefficients and intercepts.
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
activations : list, length = n_layers - 1
The ith element of the list holds the values of the ith layer.
deltas : list, length = n_layers - 1
The ith element of the list holds the difference between the
activations of the i + 1 layer and the backpropagated error.
More specifically, deltas are gradients of loss with respect to z
in each layer, where z = wx + b is the value of a particular layer
before passing through the activation function
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.
intercept_grads : list, length = n_layers - 1
The ith element contains the amount of change used to update the
intercept parameters of the ith layer in an iteration.
Returns
-------
loss : float
grad : array-like, shape (number of nodes of all layers,)
"""
self._unpack(packed_coef_inter)
loss, coef_grads, intercept_grads = self._backprop(
X, y, activations, deltas, coef_grads, intercept_grads)
self.n_iter_ += 1
grad = _pack(coef_grads, intercept_grads)
return loss, grad
def _backprop(self, X, y, activations, deltas, coef_grads,
intercept_grads):
"""Compute the MLP loss function and its corresponding derivatives
with respect to each parameter: weights and bias vectors.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,)
The target values.
activations : list, length = n_layers - 1
The ith element of the list holds the values of the ith layer.
deltas : list, length = n_layers - 1
The ith element of the list holds the difference between the
activations of the i + 1 layer and the backpropagated error.
More specifically, deltas are gradients of loss with respect to z
in each layer, where z = wx + b is the value of a particular layer
before passing through the activation function
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.
intercept_grads : list, length = n_layers - 1
The ith element contains the amount of change used to update the
intercept parameters of the ith layer in an iteration.
Returns
-------
loss : float
coef_grads : list, length = n_layers - 1
intercept_grads : list, length = n_layers - 1
"""
n_samples = X.shape[0]
# Forward propagate
activations = self._forward_pass(activations)
# Get loss
loss_func_name = self.loss
if loss_func_name == 'log_loss' and self.out_activation_ == 'logistic':
loss_func_name = 'binary_log_loss'
loss = LOSS_FUNCTIONS[loss_func_name](y, activations[-1])
# Add L2 regularization term to loss
values = np.sum(
np.array([np.dot(s.ravel(), s.ravel()) for s in self.coefs_]))
loss += (0.5 * self.alpha) * values / n_samples
# Backward propagate
last = self.n_layers_ - 2
# The calculation of delta[last] here works with following
# combinations of output activation and loss function:
# sigmoid and binary cross entropy, softmax and categorical cross
# entropy, and identity with squared loss
deltas[last] = activations[-1] - y
# Compute gradient for the last layer
coef_grads, intercept_grads = self._compute_loss_grad(
last, n_samples, activations, deltas, coef_grads, intercept_grads)
# Iterate over the hidden layers
for i in range(self.n_layers_ - 2, 0, -1):
deltas[i - 1] = safe_sparse_dot(deltas[i], self.coefs_[i].T)
inplace_derivative = DERIVATIVES[self.activation]
inplace_derivative(activations[i], deltas[i - 1])
coef_grads, intercept_grads = self._compute_loss_grad(
i - 1, n_samples, activations, deltas, coef_grads,
intercept_grads)
return loss, coef_grads, intercept_grads
def _initialize(self, y, layer_units):
# set all attributes, allocate weights etc for first call
# Initialize parameters
self.n_iter_ = 0
self.t_ = 0
self.n_outputs_ = y.shape[1]
# Compute the number of layers
self.n_layers_ = len(layer_units)
# Output for regression
if not is_classifier(self):
self.out_activation_ = 'identity'
# Output for multi class
elif self._label_binarizer.y_type_ == 'multiclass':
self.out_activation_ = 'softmax'
# Output for binary class and multi-label
else:
self.out_activation_ = 'logistic'
# Initialize coefficient and intercept layers
self.coefs_ = []
self.intercepts_ = []
for i in range(self.n_layers_ - 1):
coef_init, intercept_init = self._init_coef(layer_units[i],
layer_units[i + 1])
self.coefs_.append(coef_init)
self.intercepts_.append(intercept_init)
if self.solver in _STOCHASTIC_SOLVERS:
self.loss_curve_ = []
self._no_improvement_count = 0
if self.early_stopping:
self.validation_scores_ = []
self.best_validation_score_ = -np.inf
else:
self.best_loss_ = np.inf
def _init_coef(self, fan_in, fan_out):
# Use the initialization method recommended by
# Glorot et al.
factor = 6.
if self.activation == 'logistic':
factor = 2.
init_bound = np.sqrt(factor / (fan_in + fan_out))
# Generate weights and bias:
coef_init = self._random_state.uniform(-init_bound, init_bound,
(fan_in, fan_out))
intercept_init = self._random_state.uniform(-init_bound, init_bound,
fan_out)
return coef_init, intercept_init
def _fit(self, X, y, incremental=False):
# Make sure self.hidden_layer_sizes is a list
hidden_layer_sizes = self.hidden_layer_sizes
if not hasattr(hidden_layer_sizes, "__iter__"):
hidden_layer_sizes = [hidden_layer_sizes]
hidden_layer_sizes = list(hidden_layer_sizes)
# Validate input parameters.
self._validate_hyperparameters()
if np.any(np.array(hidden_layer_sizes) <= 0):
raise ValueError("hidden_layer_sizes must be > 0, got %s." %
hidden_layer_sizes)
X, y = self._validate_input(X, y, incremental)
n_samples, n_features = X.shape
# Ensure y is 2D
if y.ndim == 1:
y = y.reshape((-1, 1))
self.n_outputs_ = y.shape[1]
layer_units = ([n_features] + hidden_layer_sizes +
[self.n_outputs_])
# check random state
self._random_state = check_random_state(self.random_state)
if not hasattr(self, 'coefs_') or (not self.warm_start and not
incremental):
# First time training the model
self._initialize(y, layer_units)
# lbfgs does not support mini-batches
if self.solver == 'lbfgs':
batch_size = n_samples
elif self.batch_size == 'auto':
batch_size = min(200, n_samples)
else:
if self.batch_size < 1 or self.batch_size > n_samples:
warnings.warn("Got `batch_size` less than 1 or larger than "
"sample size. It is going to be clipped")
batch_size = np.clip(self.batch_size, 1, n_samples)
# Initialize lists
activations = [X]
activations.extend(np.empty((batch_size, n_fan_out))
for n_fan_out in layer_units[1:])
deltas = [np.empty_like(a_layer) for a_layer in activations]
coef_grads = [np.empty((n_fan_in_, n_fan_out_)) for n_fan_in_,
n_fan_out_ in zip(layer_units[:-1],
layer_units[1:])]
intercept_grads = [np.empty(n_fan_out_) for n_fan_out_ in
layer_units[1:]]
# Run the Stochastic optimization solver
if self.solver in _STOCHASTIC_SOLVERS:
self._fit_stochastic(X, y, activations, deltas, coef_grads,
intercept_grads, layer_units, incremental)
# Run the LBFGS solver
elif self.solver == 'lbfgs':
self._fit_lbfgs(X, y, activations, deltas, coef_grads,
intercept_grads, layer_units)
return self
def _validate_hyperparameters(self):
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False, got %s." %
self.shuffle)
if self.max_iter <= 0:
raise ValueError("max_iter must be > 0, got %s." % self.max_iter)
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0, got %s." % self.alpha)
if (self.learning_rate in ["constant", "invscaling", "adaptive"] and
self.learning_rate_init <= 0.0):
raise ValueError("learning_rate_init must be > 0, got %s." %
self.learning_rate)
if self.momentum > 1 or self.momentum < 0:
raise ValueError("momentum must be >= 0 and <= 1, got %s" %
self.momentum)
if not isinstance(self.nesterovs_momentum, bool):
raise ValueError("nesterovs_momentum must be either True or False,"
" got %s." % self.nesterovs_momentum)
if not isinstance(self.early_stopping, bool):
raise ValueError("early_stopping must be either True or False,"
" got %s." % self.early_stopping)
if self.validation_fraction < 0 or self.validation_fraction >= 1:
raise ValueError("validation_fraction must be >= 0 and < 1, "
"got %s" % self.validation_fraction)
if self.beta_1 < 0 or self.beta_1 >= 1:
raise ValueError("beta_1 must be >= 0 and < 1, got %s" %
self.beta_1)
if self.beta_2 < 0 or self.beta_2 >= 1:
raise ValueError("beta_2 must be >= 0 and < 1, got %s" %
self.beta_2)
if self.epsilon <= 0.0:
raise ValueError("epsilon must be > 0, got %s." % self.epsilon)
if self.n_iter_no_change <= 0:
raise ValueError("n_iter_no_change must be > 0, got %s."
% self.n_iter_no_change)
# raise ValueError if not registered
supported_activations = ('identity', 'logistic', 'tanh', 'relu')
if self.activation not in supported_activations:
raise ValueError("The activation '%s' is not supported. Supported "
"activations are %s." % (self.activation,
supported_activations))
if self.learning_rate not in ["constant", "invscaling", "adaptive"]:
raise ValueError("learning rate %s is not supported. " %
self.learning_rate)
supported_solvers = _STOCHASTIC_SOLVERS + ["lbfgs"]
if self.solver not in supported_solvers:
raise ValueError("The solver %s is not supported. "
" Expected one of: %s" %
(self.solver, ", ".join(supported_solvers)))
def _fit_lbfgs(self, X, y, activations, deltas, coef_grads,
intercept_grads, layer_units):
# Store meta information for the parameters
self._coef_indptr = []
self._intercept_indptr = []
start = 0
# Save sizes and indices of coefficients for faster unpacking
for i in range(self.n_layers_ - 1):
n_fan_in, n_fan_out = layer_units[i], layer_units[i + 1]
end = start + (n_fan_in * n_fan_out)
self._coef_indptr.append((start, end, (n_fan_in, n_fan_out)))
start = end
# Save sizes and indices of intercepts for faster unpacking
for i in range(self.n_layers_ - 1):
end = start + layer_units[i + 1]
self._intercept_indptr.append((start, end))
start = end
# Run LBFGS
packed_coef_inter = _pack(self.coefs_,
self.intercepts_)
if self.verbose is True or self.verbose >= 1:
iprint = 1
else:
iprint = -1
optimal_parameters, self.loss_, d = fmin_l_bfgs_b(
x0=packed_coef_inter,
func=self._loss_grad_lbfgs,
maxfun=self.max_iter,
iprint=iprint,
pgtol=self.tol,
args=(X, y, activations, deltas, coef_grads, intercept_grads))
self._unpack(optimal_parameters)
def _fit_stochastic(self, X, y, activations, deltas, coef_grads,
intercept_grads, layer_units, incremental):
if not incremental or not hasattr(self, '_optimizer'):
params = self.coefs_ + self.intercepts_
if self.solver == 'sgd':
self._optimizer = SGDOptimizer(
params, self.learning_rate_init, self.learning_rate,
self.momentum, self.nesterovs_momentum, self.power_t)
elif self.solver == 'adam':
self._optimizer = AdamOptimizer(
params, self.learning_rate_init, self.beta_1, self.beta_2,
self.epsilon)
# early_stopping in partial_fit doesn't make sense
early_stopping = self.early_stopping and not incremental
if early_stopping:
# don't stratify in multilabel classification
should_stratify = is_classifier(self) and self.n_outputs_ == 1
stratify = y if should_stratify else None
X, X_val, y, y_val = train_test_split(
X, y, random_state=self._random_state,
test_size=self.validation_fraction,
stratify=stratify)
if is_classifier(self):
y_val = self._label_binarizer.inverse_transform(y_val)
else:
X_val = None
y_val = None
n_samples = X.shape[0]
if self.batch_size == 'auto':
batch_size = min(200, n_samples)
else:
batch_size = np.clip(self.batch_size, 1, n_samples)
try:
for it in range(self.max_iter):
if self.shuffle:
X, y = shuffle(X, y, random_state=self._random_state)
accumulated_loss = 0.0
for batch_slice in gen_batches(n_samples, batch_size):
activations[0] = X[batch_slice]
batch_loss, coef_grads, intercept_grads = self._backprop(
X[batch_slice], y[batch_slice], activations, deltas,
coef_grads, intercept_grads)
accumulated_loss += batch_loss * (batch_slice.stop -
batch_slice.start)
# update weights
grads = coef_grads + intercept_grads
self._optimizer.update_params(grads)
self.n_iter_ += 1
self.loss_ = accumulated_loss / X.shape[0]
self.t_ += n_samples
self.loss_curve_.append(self.loss_)
if self.verbose:
print("Iteration %d, loss = %.8f" % (self.n_iter_,
self.loss_))
# update no_improvement_count based on training loss or
# validation score according to early_stopping
self._update_no_improvement_count(early_stopping, X_val, y_val)
# for learning rate that needs to be updated at iteration end
self._optimizer.iteration_ends(self.t_)
if self._no_improvement_count > self.n_iter_no_change:
# not better than last `n_iter_no_change` iterations by tol
# stop or decrease learning rate
if early_stopping:
msg = ("Validation score did not improve more than "
"tol=%f for %d consecutive epochs." % (
self.tol, self.n_iter_no_change))
else:
msg = ("Training loss did not improve more than tol=%f"
" for %d consecutive epochs." % (
self.tol, self.n_iter_no_change))
is_stopping = self._optimizer.trigger_stopping(
msg, self.verbose)
if is_stopping:
break
else:
self._no_improvement_count = 0
if incremental:
break
if self.n_iter_ == self.max_iter:
warnings.warn(
"Stochastic Optimizer: Maximum iterations (%d) "
"reached and the optimization hasn't converged yet."
% self.max_iter, ConvergenceWarning)
except KeyboardInterrupt:
warnings.warn("Training interrupted by user.")
if early_stopping:
# restore best weights
self.coefs_ = self._best_coefs
self.intercepts_ = self._best_intercepts
def _update_no_improvement_count(self, early_stopping, X_val, y_val):
if early_stopping:
# compute validation score, use that for stopping
self.validation_scores_.append(self.score(X_val, y_val))
if self.verbose:
print("Validation score: %f" % self.validation_scores_[-1])
# update best parameters
# use validation_scores_, not loss_curve_
# let's hope no-one overloads .score with mse
last_valid_score = self.validation_scores_[-1]
if last_valid_score < (self.best_validation_score_ +
self.tol):
self._no_improvement_count += 1
else:
self._no_improvement_count = 0
if last_valid_score > self.best_validation_score_:
self.best_validation_score_ = last_valid_score
self._best_coefs = [c.copy() for c in self.coefs_]
self._best_intercepts = [i.copy()
for i in self.intercepts_]
else:
if self.loss_curve_[-1] > self.best_loss_ - self.tol:
self._no_improvement_count += 1
else:
self._no_improvement_count = 0
if self.loss_curve_[-1] < self.best_loss_:
self.best_loss_ = self.loss_curve_[-1]
def fit(self, X, y):
"""Fit the model to data matrix X and target(s) y.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
Returns
-------
self : returns a trained MLP model.
"""
return self._fit(X, y, incremental=False)
@property
def partial_fit(self):
"""Update the model with a single iteration over the given data.
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.
"""
if self.solver not in _STOCHASTIC_SOLVERS:
raise AttributeError("partial_fit is only available for stochastic"
" optimizers. %s is not stochastic."
% self.solver)
return self._partial_fit
def _partial_fit(self, X, y):
return self._fit(X, y, incremental=True)
def _predict(self, X):
"""Predict using the trained model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
y_pred : array-like, shape (n_samples,) or (n_samples, n_outputs)
The decision function of the samples for each class in the model.
"""
X = check_array(X, accept_sparse=['csr', 'csc', 'coo'])
# Make sure self.hidden_layer_sizes is a list
hidden_layer_sizes = self.hidden_layer_sizes
if not hasattr(hidden_layer_sizes, "__iter__"):
hidden_layer_sizes = [hidden_layer_sizes]
hidden_layer_sizes = list(hidden_layer_sizes)
layer_units = [X.shape[1]] + hidden_layer_sizes + \
[self.n_outputs_]
# Initialize layers
activations = [X]
for i in range(self.n_layers_ - 1):
activations.append(np.empty((X.shape[0],
layer_units[i + 1])))
# forward propagate
self._forward_pass(activations)
y_pred = activations[-1]
return y_pred
class MLPClassifier(BaseMultilayerPerceptron, ClassifierMixin):
"""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 <warm_start>`.
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).
"""
def __init__(self, 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=1e-4,
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-8, n_iter_no_change=10):
super().__init__(
hidden_layer_sizes=hidden_layer_sizes,
activation=activation, solver=solver, alpha=alpha,
batch_size=batch_size, learning_rate=learning_rate,
learning_rate_init=learning_rate_init, power_t=power_t,
max_iter=max_iter, loss='log_loss', shuffle=shuffle,
random_state=random_state, tol=tol, verbose=verbose,
warm_start=warm_start, momentum=momentum,
nesterovs_momentum=nesterovs_momentum,
early_stopping=early_stopping,
validation_fraction=validation_fraction,
beta_1=beta_1, beta_2=beta_2, epsilon=epsilon,
n_iter_no_change=n_iter_no_change)
def _validate_input(self, X, y, incremental):
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
multi_output=True)
if y.ndim == 2 and y.shape[1] == 1:
y = column_or_1d(y, warn=True)
if not incremental:
self._label_binarizer = LabelBinarizer()
self._label_binarizer.fit(y)
self.classes_ = self._label_binarizer.classes_
elif self.warm_start:
classes = unique_labels(y)
if set(classes) != set(self.classes_):
raise ValueError("warm_start can only be used where `y` has "
"the same classes as in the previous "
"call to fit. Previously got %s, `y` has %s" %
(self.classes_, classes))
else:
classes = unique_labels(y)
if len(np.setdiff1d(classes, self.classes_, assume_unique=True)):
raise ValueError("`y` has classes not in `self.classes_`."
" `self.classes_` has %s. 'y' has %s." %
(self.classes_, classes))
y = self._label_binarizer.transform(y)
return X, y
def predict(self, X):
"""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.
"""
check_is_fitted(self, "coefs_")
y_pred = self._predict(X)
if self.n_outputs_ == 1:
y_pred = y_pred.ravel()
return self._label_binarizer.inverse_transform(y_pred)
def fit(self, X, y):
"""Fit the model to data matrix X and target(s) y.
Parameters
----------
X : array-like or sparse matrix, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
Returns
-------
self : returns a trained MLP model.
"""
return self._fit(X, y, incremental=(self.warm_start and
hasattr(self, "classes_")))
@property
def partial_fit(self):
"""Update the model with a single iteration over the given data.
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), 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.
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.
"""
if self.solver not in _STOCHASTIC_SOLVERS:
raise AttributeError("partial_fit is only available for stochastic"
" optimizer. %s is not stochastic"
% self.solver)
return self._partial_fit
def _partial_fit(self, X, y, classes=None):
if _check_partial_fit_first_call(self, classes):
self._label_binarizer = LabelBinarizer()
if type_of_target(y).startswith('multilabel'):
self._label_binarizer.fit(y)
else:
self._label_binarizer.fit(classes)
super()._partial_fit(X, y)
return self
def predict_log_proba(self, X):
"""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))
"""
y_prob = self.predict_proba(X)
return np.log(y_prob, out=y_prob)
def predict_proba(self, X):
"""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_`.
"""
check_is_fitted(self, "coefs_")
y_pred = self._predict(X)
if self.n_outputs_ == 1:
y_pred = y_pred.ravel()
if y_pred.ndim == 1:
return np.vstack([1 - y_pred, y_pred]).T
else:
return y_pred
class MLPRegressor(BaseMultilayerPerceptron, RegressorMixin):
"""Multi-layer Perceptron regressor.
This model optimizes the squared-loss 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 ``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 <warm_start>`.
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.
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
----------
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
-----
MLPRegressor 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).
"""
def __init__(self, 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=1e-4,
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-8, n_iter_no_change=10):
super().__init__(
hidden_layer_sizes=hidden_layer_sizes,
activation=activation, solver=solver, alpha=alpha,
batch_size=batch_size, learning_rate=learning_rate,
learning_rate_init=learning_rate_init, power_t=power_t,
max_iter=max_iter, loss='squared_loss', shuffle=shuffle,
random_state=random_state, tol=tol, verbose=verbose,
warm_start=warm_start, momentum=momentum,
nesterovs_momentum=nesterovs_momentum,
early_stopping=early_stopping,
validation_fraction=validation_fraction,
beta_1=beta_1, beta_2=beta_2, epsilon=epsilon,
n_iter_no_change=n_iter_no_change)
def predict(self, X):
"""Predict using the multi-layer perceptron model.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The input data.
Returns
-------
y : array-like, shape (n_samples, n_outputs)
The predicted values.
"""
check_is_fitted(self, "coefs_")
y_pred = self._predict(X)
if y_pred.shape[1] == 1:
return y_pred.ravel()
return y_pred
def _validate_input(self, X, y, incremental):
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],
multi_output=True, y_numeric=True)
if y.ndim == 2 and y.shape[1] == 1:
y = column_or_1d(y, warn=True)
return X, y
You can’t perform that action at this time.