问tensorflow2.0逻辑模型每次输出nan权重和偏差

EN

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

features = stock_copy2.iloc[:,:-1]
target = stock_copy2.iloc[:,-1]

x_train, x_test, y_train, y_test = \
train_test_split(features, target, test_size = 0.3, random_state = 1)

tf.random.set_seed(2020)
W = tf.Variable(tf.random.normal([91, 1], mean=0.0))
b = tf.Variable(tf.random.normal([1], mean=0.0))

# Learning Rate
learning_rate = 0.01

# Hypothesis and Prediction Function
def predict(X):
    z = tf.matmul(X, W) + b
    hypothesis = 1 / (1 + tf.exp(-z))
    return hypothesis

# Training
for i in range(2000+1):

    with tf.GradientTape() as tape:

        hypothesis = predict(x_train)
        cost = tf.reduce_mean(-tf.reduce_sum(y_train*tf.math.log(hypothesis) + (1-y_train)*tf.math.log(1-hypothesis)))        
        W_grad, b_grad = tape.gradient(cost, [W, b])

        W.assign_sub(learning_rate * W_grad)
        b.assign_sub(learning_rate * b_grad)

    if i % 400 == 0:
        print(">>> #%s \n Weights: \n%s \n Bias: \n%s \n cost: %s\n" % (i, W.numpy(), b.numpy(), cost.numpy()))

Weights: 
[[nan]
 [nan]
 [nan]
 [nan]
 [nan]
 [nan] ....