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import random
x_data = np.array({1,2,3,4,5}).reshape(5,1)
t_data = np.array({2,3,4,5,6}).reshape(5,1)
W=np.random.rand(1,1)
b=np.random.rand(1)
print("W=",W,"W.shape=",W.shape,"b=",b,",b.shape=",b.shape)
def loss_func(x,t):
y=np.dot(x,W)+b
return(np.sum((ty)**2))/(len(x))
def numerical_derivative(f,x):
delta_x=1e4
grad=np.zeros_like(x)
it=np.nditer(x,flags=['multi_index'],op_flags=['readwrite'])
while not it.finished:
idx=it.multi_index
tmp_val=x[idx]
x[idx]=float(tmp_val)+delta_x
fx1=f(x)
x[idx]=tmp_val=delta_x
x[idx]=tmp_val
it.iternext()
return grad
def error_val(x,t):
y=np.dot(x,W)+b
return(np.sum((ty)**2))/(len(x))
def predict(x):
y=np.dot(x,W)+b
return y
learning_rate=1e2
f=lambda x:loss_func(x_data, t_data)
print("initial error value=",error_val(x_data,t_data),"initial W=",W,"b=",b)
for step in range(8001):
W=learning_rate*numerical_derivative(f,W)
b=learning_rate*numerical_derivative(f,b)
if(step%400==0):
print("step=",step,"error value=", error_val(x_data,t_data),"W=",W,"b=",b)
print(predict(43))

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