Tensorflow: gradients do not exist for variables ['Variable:0'] when minimuzing the loss

General Discussion
,
#1

I’m a novice in python.I want to estimate about three coefficeints. I wrote down the code below. I omitted some code about some data which means two variables such as growth and age. I used tensorflow, numpy, and sklearn etc.

There is no error message but I get a warning sign. which situation causes the message, below?
how can I solve the problem?

    a=df4['growth']
    b=df4['age']
    
    y=np.array(a.values.tolist())
    X=np.array(b.values.tolist())
    
    fig = plt.figure(figsize=(10,10))
    plt.plot(X, y, "o")
    
    import tensorflow as tf
    import numpy as np
    from sklearn.metrics import r2_score
    from sklearn.metrics import mean_squared_error
    import matplotlib.pyplot as plt
    import scipy.stats as stats
    
    import random
    
    a=tf.Variable(random.random())
    b=tf.Variable(random.random())
    c=tf.Variable(random.random())
    #d=tf.Variable(random.random())
    from sklearn.metrics import classification_report
    def compute_loss():
        y_pred = a + (b * X * np.exp(-c*X))
        loss=tf.reduce_mean((y-y_pred) **2)
        return loss
    
    def accuracy():
      y_pred = a + (b * X * np.exp(-c*X))
      r2=r2_score(y, y_pred)
      return r2
    
    def accuracy2():
      y_pred = a + (b * X * np.exp(-c*X))
      rmse=mean_squared_error(y, y_pred)**0.5
      return rmse  
    optimizer = tf.keras.optimizers.SGD(lr=0.01, momentum=0.0, decay = 0.0, nesterov=False)
    
    for i in range(10000):
        optimizer.minimize(compute_loss, var_list=[a, b, c])
        if i % 10000 == 9999:
           print(i, 'a:', a.numpy(), 'b:', b.numpy(), 'c:', c.numpy(),'loss:', compute_loss().numpy(), 'r2:', accuracy(), 'rmse:', accuracy2())


WARNINIG: tensorflow: gradients do not exist for variables ['Variable:0'] when minimuzing the loss