我在尝试运行这个简单的程序来计算梯度，但是我得到了None错误：

import tensorflow as tfimport numpy as npbatch_size = 5dim = 3hidden_units = 8sess = tf.Session()with sess.as_default():    x = tf.placeholder(dtype=tf.float32, shape=[None, dim], name="x")    y = tf.placeholder(dtype=tf.int32, shape=[None], name="y")    w = tf.Variable(initial_value=tf.random_normal(shape=[dim, hidden_units]), name="w")    b = tf.Variable(initial_value=tf.zeros(shape=[hidden_units]), name="b")    logits = tf.nn.tanh(tf.matmul(x, w) + b)    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, y,name="xentropy")    # 定义模型结束    # 开始训练    optimizer = tf.train.GradientDescentOptimizer(1e-5)    grads_and_vars = optimizer.compute_gradients(cross_entropy, tf.trainable_variables())    # 生成数据    data = np.random.randn(batch_size, dim)    labels = np.random.randint(0, 10, size=batch_size)    sess.run(tf.initialize_all_variables())    gradients_and_vars = sess.run(grads_and_vars, feed_dict={x:data, y:labels})    for g, v in gradients_and_vars:        if g is not None:            print "****************这是变量*************"            print "变量的形状:", v.shape            print v            print "****************这是梯度*************"            print "梯度的形状:", g.shape            print gsess.close()

错误：

---------------------------------------------------------------------------TypeError                                 Traceback (most recent call last)<ipython-input-14-8096b2e21e06> in <module>()     29      30     sess.run(tf.initialize_all_variables())---> 31     outnet = sess.run(grads_and_vars, feed_dict={x:data, y:labels})     32 #     print(gradients_and_vars)     33 #         if g is not None://anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)    893     try:    894       result = self._run(None, fetches, feed_dict, options_ptr,--> 895                          run_metadata_ptr)    896       if run_metadata:    897         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)//anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)   1107     # Create a fetch handler to take care of the structure of fetches.   1108     fetch_handler = _FetchHandler(-> 1109         self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)   1110    1111     # Run request and get response.//anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, graph, fetches, feeds, feed_handles)    411     """    412     with graph.as_default():--> 413       self._fetch_mapper = _FetchMapper.for_fetch(fetches)    414     self._fetches = []    415     self._targets = []//anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)    231     elif isinstance(fetch, (list, tuple)):    232       # NOTE(touts): This is also the code path for namedtuples.--> 233       return _ListFetchMapper(fetch)    234     elif isinstance(fetch, dict):    235       return _DictFetchMapper(fetch)//anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, fetches)    338     """    339     self._fetch_type = type(fetches)--> 340     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]    341     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)    342 //anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in <listcomp>(.0)    338     """    339     self._fetch_type = type(fetches)--> 340     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]    341     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)    342 //anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)    231     elif isinstance(fetch, (list, tuple)):    232       # NOTE(touts): This is also the code path for namedtuples.--> 233       return _ListFetchMapper(fetch)    234     elif isinstance(fetch, dict):    235       return _DictFetchMapper(fetch)//anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in __init__(self, fetches)    338     """    339     self._fetch_type = type(fetches)--> 340     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]    341     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)    342 //anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in <listcomp>(.0)    338     """    339     self._fetch_type = type(fetches)--> 340     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]    341     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)    342 //anaconda/lib/python3.5/site-packages/tensorflow/python/client/session.py in for_fetch(fetch)    228     if fetch is None:    229       raise TypeError('Fetch argument %r has invalid type %r' %--> 230                       (fetch, type(fetch)))    231     elif isinstance(fetch, (list, tuple)):    232       # NOTE(touts): This is also the code path for namedtuples.TypeError: Fetch参数None的类型无效

为什么会出现这个错误？是版本问题吗？

回答：

Gradients 如果图中它们之间没有明确的连接，则会返回None。在你的代码中，似乎所有声明的变量都有连接，所以可能是从其他图中加载变量的情况。你可以使用：

 print([v.name for v in tf.all_variables()])

并检查只有预期的变量是这个图的一部分。

尝试像这样的代码：

sess.run(tf.initialize_all_variables())gradients_and_vars = sess.run([variable for grad,variable in grads_and_vars], feed_dict={x:data, y:labels})print(gradients_and_vars)