implement of deep neural network --- python

import numpy as npimport randomdef sigmoid(z):    return 1.0/(1.0 + np.exp(-z))def sigmoid_prime(z):    return sigmoid(z)*(1.0-sigmoid(z))class Net(object):    def __init__(self,sizes):        self.layer_num = len(sizes)        self.sizes = sizes        self.bias = [ np.random.randn(y,1) for y in sizes[1:] ]        self.weights = [ np.random.randn(y,x) for x,y in zip(sizes[:-1],sizes[1:]) ]        def feedward(self,a):        a = np.array([a]).transpose()        print a        for b,w in zip(self.bias, self.weights):            a = sigmoid( np.dot(w,a) + b )            print w.shape, a.shape        return a    def SDG(self, training_data, epochs, mini_batch_size, eta):        n = len(training_data)        for j in xrange(epochs):            random.shuffle(training_data)            mini_batchs = [ training_data[k:k+mini_batch_size]                          for k in xrange(0,n,mini_batch_size) ]            for mini_batch in mini_batchs:                self.update_mini_batch(mini_batch, eta)            if j%100 ==0:                print 'epoch{0} complete..'.format(j)        def update_mini_batch(self, mini_batch, eta):                nabla_b = [ np.zeros(b.shape) for b in self.bias ]        nabla_w = [ np.zeros(w.shape) for w in self.weights ]                for x,y in mini_batch:            delta_b, delta_w = self.backprop(x,y)            nabla_b = [ nb+dnb for nb, dnb in zip(nabla_b, delta_b) ]            nabla_w = [ nw+bnw for nw, bnw in zip(nabla_w, delta_w) ]                    self.weights = [ w-(eta/len(mini_batch))*nw for w,nw in zip(self.weights, nabla_w) ]        self.bias = [ b-(eta/len(mini_batch))*nb for b,nb in zip(self.bias, nabla_b) ]    def backprop(self,x,y):        nabla_b = [ np.zeros(b.shape) for b in self.bias ]        nabla_w = [ np.zeros(w.shape) for w in self.weights ]                # feedward        activation = np.array([x]).transpose()                #print activation        activations = [activation]        zs = []        for b,w in zip(self.bias, self.weights):            z = np.dot(w, activation)+b            zs.append(z)            activation = sigmoid(z)            activations.append(activation)        # backward        delta = self.cost_derivate(activations[-1],y) * sigmoid_prime(zs[-1])        nabla_b[-1] = delta        nabla_w[-1] = np.dot(delta, activations[-2].transpose())        for l in xrange(2, self.layer_num):            z = zs[-l]            sp = sigmoid_prime(z)            delta = np.dot( self.weights[-l+1].transpose(), delta) * sp            nabla_b[-l] = delta            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())        return (nabla_b, nabla_w)        def cost_derivate(self, output_activations, y):        return (output_activations-y)xx = Net([2,3,3,1])traindata = [([1,1],3),([1,0],2),([0,0],0), ([0,1],1),([1,1],3),([1,0],2),([0,0],0), ([0,1],1)]xx.SDG(traindata, 100, 4, 0.5)

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http://neuralnetworksanddeeplearning.com/