import numpy as np
'''
純隨機資料，練習演算法編碼
模擬一個三變數雙層神經網路
_n代表層數
_v代表向量
'''
w_1_v = np.random.randn(4,3)#4x3
b_1_v = np.random.randn(4,1)#4x1
x_v = np.random.randn(3,10)#3x10
y_v = np.random.randn(1,10)#1x10
w_2_v = np.random.randn(1,4)#1x4
b_2_v = np.random.randn(1,1)#1x1
aa = 0.2#Alpha
z_1_v = np.dot(w_1_v,x_v)+b_1_v#4x10
a_1_v = (np.exp(z_1_v)-np.exp(-z_1_v))/(np.exp(z_1_v)+np.exp(-z_1_v))#4x10
z_2_v = np.dot(w_2_v,a_1_v)+b_2_v#1x10
a_2_v = 1/(1+np.exp(-z_2_v))#1x10
dz_2_v = a_2_v-y_v#1x10
dw_2_v = np.dot(dz_2_v,a_1_v.T)#1x4
db_2_v = 1/10*np.sum(dz_2_v,axis = 1,keepdims=True)#1x1
dz_1_v = np.dot(w_2_v.T,dz_2_v)*(1-(np.exp(z_1_v)-np.exp(-z_1_v))/(np.exp(z_1_v)+np.exp(-z_1_v))*(np.exp(z_1_v)-np.exp(-z_1_v))/(np.exp(z_1_v)+np.exp(-z_1_v)))#4x10
dw_1_v = np.dot(dz_1_v,x_v.T)#4x3
db_1_v = 1/10*np.sum(dz_1_v,axis = 1,keepdims=True)#4x1
w_1_v = w_1_v-aa*dw_1_v
w_2_v = w_2_v-aa*dw_2_v
b_1_v = b_1_v-aa*db_1_v
b_2_v = b_2_v-aa*db_2_v
print("w_1_v:")
print(w_1_v)
print("w_2_v:")
print(w_2_v)
print("b_1_v:")
print(b_1_v)
print("b_2_v:")
print(b_2_v)