2018-06-12 Neural Network

class NeuralNetwork:
def init(self, layers, activation='tanh'):

    if activation == 'logistic':
        self.activation = logistic
        self.activation_deriv = logistic_derivative
    elif activation == 'tanh':
        self.activation = tanh
        self.activation_deriv = tanh_deriv
        
    self.weights = []
    for i in range(1, len(layers) - 1):
        self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i] + 1))-1)*0.25)
        self.weights.append((2*np.random.random((layers[i] + 1, layers[i + 1]))-1)*0.25)

def fit(self, X, y, learning_rate=0.2, epochs=10000):
    X = np.atleast_2d(X) #x trainning dataset y target
    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.array(y)

    for k in range(epochs):   #loop times
        i = np.random.randint(X.shape[0])
        a = [X[i]]   #trainning data

        for l in range(len(self.weights)):  #going forward network, for each layer
            a.append(self.activation(np.dot(a[l], self.weights[l])))  #Computer the node value for each layer (O_i) using activation function 
        error = y[i] - a[-1]  #Computer the error at the top layer
        deltas = [error * self.activation_deriv(a[-1])] #For output layer, Err calculation (delta is updated error)

        #Staring backprobagation
        for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
            #Compute the updated error (i,e, deltas) for each node going from top layer to input layer

            deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
        deltas.reverse()
        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.array(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