add valid for graph on report 2

hw1/stable.py

@@ -47,6 +47,14 @@ def extract_feature(M, features, squares, cubics):
 std = np.std(x_data, axis=0)
 x_data = (x_data - mean) / (std + 1e-20)
 
+number = 5000
+x_data = x_data[:number]
+y_data = y_data[:number]
+
+valid_num = -20
+x_data_valid = x_data[valid_num:]
+y_data_valid = y_data[valid_num:]
+
 # ydata = b + w * xdata
 b = 0.0
 w = np.zeros(length*9)
@@ -58,21 +66,23 @@ def extract_feature(M, features, squares, cubics):
 for e in range(epoch):
  # Calculate the value of the loss function
  error = y_data - b - np.dot(x_data, w) #shape: (5652,)
+ error2 = y_data_valid - b - np.dot(x_data_valid, w) #shape: (valid_num,)
 
  # Calculate gradient
  b_grad = -2*np.sum(error)*1 #shape: ()
  w_grad = -2*np.dot(error, x_data) #shape: (162,)
  b_lr = b_lr + b_grad**2
  w_lr = w_lr + w_grad**2
  loss = np.mean(np.square(error))
+ valid_loss = np.mean(np.square(error2))
 
  # Update parameters.
  b = b - lr/np.sqrt(b_lr) * b_grad
  w = w - lr/np.sqrt(w_lr) * w_grad
 
  # Print loss
  if (e+1) % 1000 == 0:
- print('epoch:{}\n Loss:{}'.format(e+1, np.sqrt(loss)))
+ print('epoch:{}\n Loss:{} valid:{}\n'.format(e+1, np.sqrt(loss), np.sqrt(valid_loss)))
 
 
 # Test