用向量计算bp神经网络

# author: Roy.G

import numpy as np
import dataset
import plot_utils as pu

def sigmoid(x):
    return 1/(1+np.exp(-x))

X,Y = dataset.get_beans(100)
print(X[1,:])
W=np.array([0.1,0.1])
B=np.array([0.1])
def forward_propgation (X):
    Z=X.dot(W.T)+B
    A=sigmoid(Z)
    return A

for i in range(1000):
        for i in range(100):
            Xi=X[i,:]
            y = Y[i]

            a=forward_propgation(Xi)
            e=(y-a)**2

            de_da=-2*(y-a)
            da_dz=a*(1-a)
            dz_dw=Xi
            dz_db=1

            de_dw=de_da*da_dz*dz_dw
            de_db=de_da*da_dz*dz_db

            alpha=0.05
            W=W-alpha*de_dw

            b = B - alpha * de_db
# pre=forward_propgation(xs1,xs2)
pu.show_scatter_surface(X,Y,forward_propgation)