CS545 fall 2016

"""
Feed-forward neural networks trained using backpropagation
based on code from http://rolisz.ro/2013/04/18/neural-networks-in-python/
"""
 
 
import numpy as np
 
def tanh(x):
    return np.tanh(x)
 
def tanh_deriv(x):
    return 1.0 - np.tanh(x)**2
 
def logistic(x):
    return 1/(1 + np.exp(-x))
 
def logistic_derivative(x):
    return logistic(x)*(1-logistic(x))
 
class NeuralNetwork:
    def __init__(self, layers, activation='tanh') :
        """
        layers: A list containing the number of units in each layer.
                Should contain at least two values
        activation: The activation function to be used. Can be
                "logistic" or "tanh"
        """
        if activation == 'logistic':
            self.activation = logistic
            self.activation_deriv = logistic_derivative
        elif activation == 'tanh':
            self.activation = tanh
            self.activation_deriv = tanh_deriv
        self.num_layers = len(layers) - 1
        self.weights = [ np.random.randn(layers[i - 1] + 1, layers[i] + 1)/10 for i in range(1, len(layers) - 1) ]
        self.weights.append(np.random.randn(layers[-2] + 1, layers[-1])/10)
 
    def forward(self, x) :
        """
        compute the activation of each layer in the network
        """
        a = [x]
        for i in range(self.num_layers) :
            a.append(self.activation(np.dot(a[i], self.weights[i])))
        return a
 
    def backward(self, y, a) :
        """
        compute the deltas for example i
        """
        deltas = [(y - a[-1]) * self.activation_deriv(a[-1])]
        for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
            deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
        deltas.reverse()
        return deltas
 
    def fit(self, X, y, learning_rate=0.2, epochs=50):
        X = np.asarray(X)
        temp = np.ones( (X.shape[0], X.shape[1]+1))
        temp[:, 0:-1] = X  # adding the bias unit to the input layer
        X = temp
        y = np.asarray(y)
 
        for k in range(epochs):
            if k%10==0 : print "***************** ", k, "epochs  ***************"            
            I = np.random.permutation(X.shape[0])
            for i in I :
                a = self.forward(X[i])
                deltas = self.backward(y[i], a)
                # update the weights using the activations and deltas:
                for i in range(len(self.weights)):
                    layer = np.atleast_2d(a[i])
                    delta = np.atleast_2d(deltas[i])
                    self.weights[i] += learning_rate * layer.T.dot(delta)
 
    def predict(self, x):
        x = np.asarray(x)
        temp = np.ones(x.shape[0]+1)
        temp[0:-1] = x
        a = temp
        for l in range(0, len(self.weights)):
            a = self.activation(np.dot(a, self.weights[l]))
        return a
 
def test_digits() :
 
    from sklearn.cross_validation import train_test_split 
    from sklearn.datasets import load_digits
    from sklearn.metrics import confusion_matrix, classification_report
    from sklearn.preprocessing import LabelBinarizer
 
    digits = load_digits()
    X = digits.data
    y = digits.target
    X /= X.max()
 
    nn = NeuralNetwork([64,100,10],'logistic')
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
    labels_train = LabelBinarizer().fit_transform(y_train)
    labels_test = LabelBinarizer().fit_transform(y_test)
 
    nn.fit(X_train,labels_train,epochs=100)
    predictions = []
    for i in range(X_test.shape[0]) :
        o = nn.predict(X_test[i])
        predictions.append(np.argmax(o))
    print confusion_matrix(y_test,predictions)
    print classification_report(y_test,predictions)
 
 
if __name__=='__main__' :
    w = np.random.uniform(-1, 1, 2)
    X = np.random.uniform(-1, 1, [20, 2])
    y = (np.sign(np.dot(X, w)) + 1) / 2
    network = NeuralNetwork([2,2,1], 'logistic')
    network.fit(X,y, epochs=100)
    for i in range(len(X)) :
        print i, y[i], network.predict(X[i])