Tensorflow错误：ValueError：不支持任何值

这是我的测试代码 . 但它无法运行 . 终端总是给我这个错误：

回溯（最近一次调用最后一次）：文件“desktop / test.py”，第28行，在loss = tf.reduce_mean（tf.reduce_sum（tf.square（ys-prediction），reduction_indices = [1]））文件“/用户/ sumeixu / anaconda3 / lib / python3.6 / site-packages / tensorflow / python / ops / math_ops.py“，第898行，在binary_op_wrapper中y = ops.convert_to_tensor（y，dtype = x.dtype.base_dtype，name = “y”）文件“/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py”，第932行，在convert_to_tensor中as_ref = False）文件“/ Users / sumeixu / anaconda3 / lib / python3.6 / site-packages / tensorflow / python / framework / ops.py“，第1022行，in internal_convert_to_tensor ret = conversion_func（value，dtype = dtype，name = name，as_ref = as_ref）File”/ Users /sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py“，第233行，在_constant_tensor_conversion_function中返回常量（v，dtype = dtype，name = name）文件”/ Users / sumeixu /anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py“，第21行2，在常量值中，dtype = dtype，shape = shape，verify_shape = verify_shape））文件“/Users/sumeixu/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py”，行401，在make_tensor_proto中引发ValueError（“不支持值 . ”）ValueError：不支持任何值 .

请帮忙 . Blew是我的代码 . 非常感谢！

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_Plus_b = tf.matmul(inputs,Weights)+biases

    if activation_function is None:
        outputs=Wx_Plus_b
    else:
            outputs=activation_function(Wx_Plus_b)
            return outputs

x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)
y_data=np.square(x_data)-0.5+noise

xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])

l1 = add_layer(xs,1,10,activation_function=tf.nn.relu)

prediction=add_layer(l1,10,1,activation_function=None)

loss =tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))

train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})

    if i%50==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))