深度学习和神经网络_深度学习之神经网络基础（前馈神经网络）

import numpy as np
import sys
import os
 
class NeuralNetMLP(object):
    """ Feedforward neural network / Multi-layer perceptron classifier.
    Parameters
    ------------
    n_hidden : int (default: 30)
        Number of hidden units.
    l2 : float (default: 0.)
        Lambda value for L2-regularization.
        No regularization if l2=0. (default)
    epochs : int (default: 100)
        Number of passes over the training set.
    eta : float (default: 0.001)
        Learning rate.
    shuffle : bool (default: True)
        Shuffles training data every epoch if True to prevent circles.
    minibatch_size : int (default: 1)
        Number of training samples per minibatch.
    seed : int (default: None)
        Random seed for initalizing weights and shuffling.
    Attributes
    -----------
    eval_ : dict
      Dictionary collecting the cost, training accuracy,
      and validation accuracy for each epoch during training.
    """
    def __init__(self, n_hidden=30,
                 l2=0., epochs=100, eta=0.001,
                 shuffle=True, minibatch_size=1, seed=None):
 
        self.random = np.random.RandomState(seed)
        self.n_hidden = n_hidden
        self.l2 = l2
        self.epochs = epochs
        self.eta = eta
        self.shuffle = shuffle
        self.minibatch_size = minibatch_size
 
    def _onehot(self, y, n_classes):
        """Encode labels into one-hot representation
        Parameters
        ------------
        y : array, shape = [n_samples]
            Target values.
        Returns
        -----------
        onehot : array, shape = (n_samples, n_labels)
        """
        onehot = np.zeros((n_classes, y.shape[0]))
        for idx, val in enumerate(y.astype(int)):
            onehot[val, idx] = 1.
        return onehot.T
 
    def _sigmoid(self, z):
        """Compute logistic function (sigmoid)"""
        return 1. / (1. + np.exp(-np.clip(z, -250, 250)))
 
    def _forward(self, X):
        """Compute forward propagation step"""
 
        # step 1: net input of hidden layer
        # [n_samples, n_features] dot [n_features, n_hidden]
        # -> [n_samples, n_hidden]
        z_h = np.dot(X, self.w_h) + self.b_h
 
        # step 2: activation of hidden layer
        a_h = self._sigmoid(z_h)
 
        # step 3: net input of output layer
        # [n_samples, n_hidden] dot [n_hidden, n_classlabels]
        # -> [n_samples, n_classlabels]
 
        z_out = np.dot(a_h, self.w_out) + self.b_out
 
        # step 4: activation output layer
        a_out = self._sigmoid(z_out)
 
        return z_h, a_h, z_out, a_out
 
    def _compute_cost(self, y_enc, output):
        """Compute cost function.
        Parameters
        ----------
        y_enc : array, shape = (n_samples, n_labels)
            one-hot encoded class labels.
        output : array, shape = [n_samples, n_output_units]
            Activation of the output layer (forward propagation)
        Returns
        ---------
        cost : float
            Regularized cost
        """
        L2_term = (self.l2 *
                   (np.sum(self.w_h ** 2.) +
                    np.sum(self.w_out ** 2.)))
 
        term1 = -y_enc * (np.log(output))
        term2 = (1. - y_enc) * np.log(1. - output)
        cost = np.sum(term1 - term2) + L2_term
        
        # If you are applying this cost function to other
        # datasets where activation
        # values maybe become more extreme (closer to zero or 1)
        # you may encounter "ZeroDivisionError"s due to numerical
        # instabilities in Python & NumPy for the current implementation.
        # I.e., the code tries to evaluate log(0), which is undefined.
        # To address this issue, you could add a small constant to the
        # activation values that are passed to the log function.
        #
        # For example:
        #
        # term1 = -y_enc * (np.log(output + 1e-5))
        # term2 = (1. - y_enc) * np.log(1. - output + 1e-5)
        
        return cost
 
    def predict(self, X):
        """Predict class labels
        Parameters
        -----------
        X : array, shape = [n_samples, n_features]
            Input layer with original features.
        Returns:
        ----------
        y_pred : array, shape = [n_samples]
            Predicted class labels.
        """
        z_h, a_h, z_out, a_out = self._forward(X)
        y_pred = np.argmax(z_out, axis=1)
        return y_pred
 
    def fit(self, X_train, y_train, X_valid, y_valid):
        """ Learn weights from training data.
        Parameters
        -----------
        X_train : array, shape = [n_samples, n_features]
            Input layer with original features.
        y_train : array, shape = [n_samples]
            Target class labels.
        X_valid : array, shape = [n_samples, n_features]
            Sample features for validation during training
        y_valid : array, shape = [n_samples]
            Sample labels for validation during training
        Returns:
        ----------
        self
        """
        n_output = np.unique(y_train).shape[0]  # number of class labels
        n_features = X_train.shape[1]
 
        ########################
        # Weight initialization
        ########################
 
        # weights for input -> hidden
        self.b_h = np.zeros(self.n_hidden)
        self.w_h = self.random.normal(loc=0.0, scale=0.1,
                                      size=(n_features, self.n_hidden))
 
        # weights for hidden -> output
        self.b_out = np.zeros(n_output)
        self.w_out = self.random.normal(loc=0.0, scale=0.1,
                                        size=(self.n_hidden, n_output))
 
        epoch_strlen = len(str(self.epochs))  # for progress formatting
        self.eval_ = {'cost': [], 'train_acc': [], 'valid_acc': []}
 
        y_train_enc = self._onehot(y_train, n_output)
 
        # iterate over training epochs
        for i in range(self.epochs):
 
            # iterate over minibatches
            indices = np.arange(X_train.shape[0])
 
            if self.shuffle:
                self.random.shuffle(indices)
 
            for start_idx in range(0, indices.shape[0] - self.minibatch_size +
                                   1, self.minibatch_size):
                batch_idx = indices[start_idx:start_idx + self.minibatch_size]
 
                # forward propagation
                z_h, a_h, z_out, a_out = self._forward(X_train[batch_idx])
 
                ##################
                # Backpropagation
                ##################
 
                # [n_samples, n_classlabels]
                sigma_out = a_out - y_train_enc[batch_idx]
 
                # [n_samples, n_hidden]
                sigmoid_derivative_h = a_h * (1. - a_h)
 
                # [n_samples, n_classlabels] dot [n_classlabels, n_hidden]
                # -> [n_samples, n_hidden]
                sigma_h = (np.dot(sigma_out, self.w_out.T) *
                           sigmoid_derivative_h)
 
                # [n_features, n_samples] dot [n_samples, n_hidden]
                # -> [n_features, n_hidden]
                grad_w_h = np.dot(X_train[batch_idx].T, sigma_h)
                grad_b_h = np.sum(sigma_h, axis=0)
 
                # [n_hidden, n_samples] dot [n_samples, n_classlabels]
                # -> [n_hidden, n_classlabels]
                grad_w_out = np.dot(a_h.T, sigma_out)
                grad_b_out = np.sum(sigma_out, axis=0)
 
                # Regularization and weight updates
                delta_w_h = (grad_w_h + self.l2*self.w_h)
                delta_b_h = grad_b_h # bias is not regularized
                self.w_h -= self.eta * delta_w_h
                self.b_h -= self.eta * delta_b_h
 
                delta_w_out = (grad_w_out + self.l2*self.w_out)
                delta_b_out = grad_b_out  # bias is not regularized
                self.w_out -= self.eta * delta_w_out
                self.b_out -= self.eta * delta_b_out
 
            #############
            # Evaluation
            #############
 
            # Evaluation after each epoch during training
            z_h, a_h, z_out, a_out = self._forward(X_train)
            
            cost = self._compute_cost(y_enc=y_train_enc,
                                      output=a_out)
 
            y_train_pred = self.predict(X_train)
            y_valid_pred = self.predict(X_valid)
 
            train_acc = ((np.sum(y_train == y_train_pred)).astype(np.float) /
                         X_train.shape[0])
            valid_acc = ((np.sum(y_valid == y_valid_pred)).astype(np.float) /
                         X_valid.shape[0])
 
            sys.stderr.write('r%0*d/%d | Cost: %.2f '
                             '| Train/Valid Acc.: %.2f%%/%.2f%% ' %
                             (epoch_strlen, i+1, self.epochs, cost,
                              train_acc*100, valid_acc*100))
            sys.stderr.flush()
 
            self.eval_['cost'].append(cost)
            self.eval_['train_acc'].append(train_acc)
            self.eval_['valid_acc'].append(valid_acc)
 
        return self