machinelearning_notebook/references/nn/nn_4.py

#% matplotlib inline

import numpy as np
from sklearn import datasets, linear_model
import matplotlib.pyplot as plt


# define sigmod & its derivate function
def sigmod(X):
    return 1.0/(1+np.exp(-X))


# generate the NN model
class NN_Model:
    def __init__(self, nodes=None):
        self.epsilon = 0.01                 # learning rate
        self.n_epoch = 1000                 # iterative number
        
        if not nodes:
            self.nodes = [2, 4, 2]          # default nodes size (from input -> output)
        else:
            self.nodes = nodes
    
    def init_weight(self):
        W = []
        B = []
        
        n_layer = len(self.nodes)
        for i in range(n_layer-1):
            w = np.random.randn(self.nodes[i], self.nodes[i+1]) / np.sqrt(self.nodes[i])
            b = np.random.randn(1, self.nodes[i+1])
            
            W.append(w)
            B.append(b)
            
        self.W = W
        self.B = B
    
    def forward(self, X):
        Z = []
        x0 = X
        for i in range(len(self.nodes)-1):
            z = sigmod(np.dot(x0, self.W[i]) + self.B[i])
            x0 = z
            
            Z.append(z)
        
        self.Z = Z
        
    # back-propagation
    def backpropagation(self, X, y, n_epoch=None, epsilon=None):
        if not n_epoch: n_epoch = self.n_epoch
        if not epsilon: epsilon = self.epsilon
        
        self.X = X
        self.Y = y
        
        for i in range(n_epoch):
            # forward to calculate each node's output
            self.forward(X)
            
            # calc weights update
            W = self.W
            B = self.B
            Z = self.Z
            
            D = []
            d0 = y
            n_layer = len(self.nodes)
            for j in range(n_layer-1, 0, -1):
                jj = j - 1
                z = self.Z[jj]
                
                if j == n_layer - 1:
                    d = z*(1-z)*(d0 - z)
                else:
                    d = z*(1-z)*np.dot(d0, W[jj].T)
                    
                d0 = d
                D.insert(0, d)
            
            # update weights
            for j in range(n_layer-1, 0, -1):
                jj = j - 1
                
                if jj != 0:
                    W[jj] += epsilon * np.dot(Z[jj-1].T, D[jj])
                else:
                    W[jj] += epsilon * np.dot(X.T, D[jj])
                    
                B[jj] += epsilon * np.sum(D[jj], axis=0)
        
    def evaulate(self):
        z = self.Z[-1]
        
        # print loss, accuracy
        L = np.sum((z - self.Y)**2)
            
        y_pred = np.argmax(z)
        y_true = np.argmax(self.Y)
        acc = accuracy_score(y_true, y_pred)
        
        print("L = %f, acc = %f" % (L, acc))
        


# generate sample data
np.random.seed(0)
X, y = datasets.make_moons(200, noise=0.20)

# generate nn output target
t = np.zeros((X.shape[0], 2))
t[np.where(y==0), 0] = 1
t[np.where(y==1), 1] = 1

# plot data
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)
plt.show()


nn = NN_Model([2, 4, 2])
nn.init_weight()
nn.backpropagation(X, t)

nn.evaluate()