Полиномиальная регрессия. Не удаётся подобрать коэффициенты

import tensorflow as tf
import numpy as np
from tqdm import tqdm_notebook as tqdm
from matplotlib import pyplot as plt
 
learning_rate = 0.01
training_epoches = 500
 
y_coeffs = [12, 5, 5, 245, 3, 59, -190, 99, 8]
num_coeffs = len(y_coeffs)
 
x_train = np.linspace(-16, 11, 1000)
 
y_train = 0
for i in range(num_coeffs):
    y_train += y_coeffs[i] * np.power(x_train, i)
 
y_train += np.random.randn(*x_train.shape) * 50000000.4
 
fig = plt.figure(figsize=(10, 10))
plt.scatter(x_train, y_train)
plt.show()
 
X = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
w = tf.Variable([0.0] * num_coeffs, name='weights')
 
def F(x, w):
    terms = []
    for i in range(num_coeffs):
        term = tf.multiply(w[i], tf.pow(x, i))
        terms.append(term)
        
    return tf.add_n(terms)
 
model = F(X, w)
cost = tf.pow(Y - model, 2)
 
optimizer = tf.train.AdadeltaOptimizer(learning_rate)
train_operation = optimizer.minimize(cost)
 
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
 
for epoche in tqdm(range(training_epoches), desc='epoches: '):
    for (x, y) in zip(x_train, y_train):
        sess.run(train_operation, feed_dict={X : x, Y : y})
        
w_end = sess.run(w)
sess.close()
 
fig = plt.figure(figsize=(10, 10))
plt.scatter(x_train, y_train)
 
trY2 = 0
for i in range(num_coeffs):
    trY2 += w_end[i] * np.power(x_train, i)
    
plt.plot(x_train, trY2, linewidth=5, color='green')
plt.show()
 
print(w_end)