tensorflow预测的y太大

Question

我在python3上用张量流实现了“线性回归” 当我运行代码时，predicted_y太大。

我不知道是什么问题

请告诉我。

以下是有问题的代码

import tensorflow as tf
import numpy as np

class Model(object):
 def __init__(self):
        self.w = tf.Variable(0.2)
        self.b = tf.Variable(0.1)
        self.var_list = []
        self.var_list.append(self.w)
        self.var_list.append(self.b)
 def __call__(self, x):
        return self.w*x+self.b

 def loss(self, predicted_y, target_y):
    return tf.reduce_mean((predicted_y-target_y)**2)

 def grad(self, x, target_y):
    with tf.GradientTape() as tape:
        tape.watch(self.var_list)
        loss_value = self.loss(self(x), target_y)
    return tape.gradient(loss_value, [self.w, self.b])

 def optimizer(self):
    opt = tf.keras.optimizers.SGD(learning_rate=0.01)
    return opt

height = np.array([[120, 140, 160, 170, 180]])
weight = np.array([[40,     50, 60, 65, 75]])

model = Model()
model(height)
opt = model.optimizer()

for _ in range(10):
    print(model(height))
    grads = model.grad(height, weight)  
    opt.apply_gradients(zip(grads, [model.w, model.b]))