ValueError：没有为任何变量提供梯度，检查你的图表是否支持梯度

我是 tensorflow 的初学者，我从网上尝试了一些玩具示例：

import io
import tensorflow as tf
import math

def _runModel(data):
        hidden1 = tf.nn.relu(tf.matmul(data, weights) + biases) 
        print(weights.eval())
        hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)  
        print(weights.eval())
        logits = tf.matmul(hidden2, weights) + biases   
        print(weights.eval())
        return logits

sess = tf.InteractiveSession()
weights = tf.Variable( tf.truncated_normal([5,5], stddev=1.0 / math.sqrt(float(5))), name='weights')
biases = tf.Variable(tf.zeros([5]), name='biases')
data_placeholder = tf.placeholder(tf.float32, shape= [1,5]) 
labels_placeholder = tf.placeholder(tf.int32, shape=[1])
sess.run(tf.global_variables_initializer())
data_placeholder = [[0.0, 1.0, 2.0, 3.0, 4.0]]
labels_placeholder = [1]

inference=_runModel(data_placeholder)
logits = inference.eval()
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.to_int64(labels_placeholder), logits=logits, name='xentropy')
loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')

optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)

_, c = sess.run([optimizer, loss])

但是当我运行它时，出现以下错误：

Traceback (most recent call last):   File "test_tensorflow.py", line 43, in <module>
    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)   File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 421, in minimize
    ([str(v) for _, v in grads_and_vars], loss)) ValueError: No gradients provided for any variable, check your graph for ops that do not support gradients, between variables ["<tf.Variable 'weights:0' shape=(5, 5) dtype=float32_ref>", "<tf.Variable 'biases:0' shape=(5,) dtype=float32_ref>"] and loss Tensor("xentropy_mean:0", shape=(), dtype=float32).

如果您有任何想法可以帮助我...