238 lines
6.2 KiB
Python
238 lines
6.2 KiB
Python
"""Utilities for the neural network modules
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"""
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# Author: Issam H. Laradji <issam.laradji@gmail.com>
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# License: BSD 3 clause
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import numpy as np
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from scipy.special import expit as logistic_sigmoid
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from scipy.special import xlogy
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def inplace_identity(X):
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"""Simply leave the input array unchanged.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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Data, where `n_samples` is the number of samples
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and `n_features` is the number of features.
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"""
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# Nothing to do
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def inplace_logistic(X):
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"""Compute the logistic function inplace.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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The input data.
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"""
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logistic_sigmoid(X, out=X)
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def inplace_tanh(X):
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"""Compute the hyperbolic tan function inplace.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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The input data.
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"""
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np.tanh(X, out=X)
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def inplace_relu(X):
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"""Compute the rectified linear unit function inplace.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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The input data.
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"""
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np.maximum(X, 0, out=X)
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def inplace_softmax(X):
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"""Compute the K-way softmax function inplace.
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Parameters
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----------
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X : {array-like, sparse matrix}, shape (n_samples, n_features)
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The input data.
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"""
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tmp = X - X.max(axis=1)[:, np.newaxis]
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np.exp(tmp, out=X)
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X /= X.sum(axis=1)[:, np.newaxis]
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ACTIVATIONS = {
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"identity": inplace_identity,
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"tanh": inplace_tanh,
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"logistic": inplace_logistic,
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"relu": inplace_relu,
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"softmax": inplace_softmax,
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}
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def inplace_identity_derivative(Z, delta):
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"""Apply the derivative of the identity function: do nothing.
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Parameters
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----------
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Z : {array-like, sparse matrix}, shape (n_samples, n_features)
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The data which was output from the identity activation function during
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the forward pass.
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delta : {array-like}, shape (n_samples, n_features)
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The backpropagated error signal to be modified inplace.
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"""
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# Nothing to do
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def inplace_logistic_derivative(Z, delta):
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"""Apply the derivative of the logistic sigmoid function.
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It exploits the fact that the derivative is a simple function of the output
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value from logistic function.
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Parameters
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----------
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Z : {array-like, sparse matrix}, shape (n_samples, n_features)
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The data which was output from the logistic activation function during
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the forward pass.
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delta : {array-like}, shape (n_samples, n_features)
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The backpropagated error signal to be modified inplace.
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"""
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delta *= Z
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delta *= 1 - Z
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def inplace_tanh_derivative(Z, delta):
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"""Apply the derivative of the hyperbolic tanh function.
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It exploits the fact that the derivative is a simple function of the output
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value from hyperbolic tangent.
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Parameters
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----------
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Z : {array-like, sparse matrix}, shape (n_samples, n_features)
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The data which was output from the hyperbolic tangent activation
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function during the forward pass.
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delta : {array-like}, shape (n_samples, n_features)
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The backpropagated error signal to be modified inplace.
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"""
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delta *= 1 - Z**2
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def inplace_relu_derivative(Z, delta):
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"""Apply the derivative of the relu function.
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It exploits the fact that the derivative is a simple function of the output
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value from rectified linear units activation function.
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Parameters
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----------
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Z : {array-like, sparse matrix}, shape (n_samples, n_features)
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The data which was output from the rectified linear units activation
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function during the forward pass.
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delta : {array-like}, shape (n_samples, n_features)
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The backpropagated error signal to be modified inplace.
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"""
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delta[Z == 0] = 0
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DERIVATIVES = {
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"identity": inplace_identity_derivative,
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"tanh": inplace_tanh_derivative,
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"logistic": inplace_logistic_derivative,
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"relu": inplace_relu_derivative,
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}
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def squared_loss(y_true, y_pred):
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"""Compute the squared loss for regression.
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Parameters
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----------
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y_true : array-like or label indicator matrix
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Ground truth (correct) values.
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y_pred : array-like or label indicator matrix
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Predicted values, as returned by a regression estimator.
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Returns
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-------
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loss : float
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The degree to which the samples are correctly predicted.
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"""
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return ((y_true - y_pred) ** 2).mean() / 2
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def log_loss(y_true, y_prob):
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"""Compute Logistic loss for classification.
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Parameters
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----------
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y_true : array-like or label indicator matrix
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Ground truth (correct) labels.
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y_prob : array-like of float, shape = (n_samples, n_classes)
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Predicted probabilities, as returned by a classifier's
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predict_proba method.
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Returns
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-------
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loss : float
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The degree to which the samples are correctly predicted.
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"""
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eps = np.finfo(y_prob.dtype).eps
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y_prob = np.clip(y_prob, eps, 1 - eps)
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if y_prob.shape[1] == 1:
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y_prob = np.append(1 - y_prob, y_prob, axis=1)
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if y_true.shape[1] == 1:
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y_true = np.append(1 - y_true, y_true, axis=1)
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return -xlogy(y_true, y_prob).sum() / y_prob.shape[0]
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def binary_log_loss(y_true, y_prob):
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"""Compute binary logistic loss for classification.
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This is identical to log_loss in binary classification case,
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but is kept for its use in multilabel case.
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Parameters
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----------
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y_true : array-like or label indicator matrix
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Ground truth (correct) labels.
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y_prob : array-like of float, shape = (n_samples, 1)
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Predicted probabilities, as returned by a classifier's
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predict_proba method.
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Returns
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-------
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loss : float
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The degree to which the samples are correctly predicted.
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"""
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eps = np.finfo(y_prob.dtype).eps
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y_prob = np.clip(y_prob, eps, 1 - eps)
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return (
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-(xlogy(y_true, y_prob).sum() + xlogy(1 - y_true, 1 - y_prob).sum())
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/ y_prob.shape[0]
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)
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LOSS_FUNCTIONS = {
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"squared_error": squared_loss,
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"log_loss": log_loss,
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"binary_log_loss": binary_log_loss,
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}
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