张量流类型错误：获取参数 none 具有无效的类型<类"NoneType>

Question

我正在做CS231N分配2并遇到此问题。

我正在使用TensorFlow-GPU 1.5.0

代码如下

# define our input (e.g. the data that changes every batch)
# The first dim is None, and gets sets automatically based on batch size fed in
X = tf.placeholder(tf.float32, [None, 32, 32, 3])
y = tf.placeholder(tf.int64, [None])
is_training = tf.placeholder(tf.bool)
# define model
def complex_model(X,y,is_training):
    pass
y_out = complex_model(X,y,is_training)
# Now we're going to feed a random batch into the model 
# and make sure the output is the right size
x = np.random.randn(64, 32, 32,3)
with tf.Session() as sess:
    with tf.device("/cpu:0"): #"/cpu:0" or "/gpu:0"
    tf.global_variables_initializer().run()
    ans = sess.run(y_out,feed_dict={X:x,is_training:True})
    %timeit sess.run(y_out,feed_dict={X:x,is_training:True})
    print(ans.shape)
    print(np.array_equal(ans.shape, np.array([64, 10])))

完成追溯

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-97f0b6c5a72e> in <module>()
      6         tf.global_variables_initializer().run()
      7 
----> 8         ans = sess.run(y_out,feed_dict={X:x,is_training:True})
      9         get_ipython().run_line_magic('timeit',     'sess.run(y_out,feed_dict={X:x,is_training:True})')
     10         print(ans.shape)
c:userskasperappdatalocalprogramspythonpython36libsite-    packagestensorflowpythonclientsession.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)
c:userskasperappdatalocalprogramspythonpython36libsite-packagestensorflowpythonclientsession.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1111     # Create a fetch handler to take care of the structure of fetches.
   1112     fetch_handler = _FetchHandler(
-> 1113         self._graph, fetches, feed_dict_tensor,     feed_handles=feed_handles)
   1114 
   1115     # Run request and get response.
c:userskasperappdatalocalprogramspythonpython36libsite-packagestensorflowpythonclientsession.py in __init__(self, graph, fetches, feeds, feed_handles)
    419     with graph.as_default():
--> 420       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    421     self._fetches = []
    422     self._targets = []
c:userskasperappdatalocalprogramspythonpython36libsite-packagestensorflowpythonclientsession.py in for_fetch(fetch)
    235     if fetch is None:
    236       raise TypeError('Fetch argument %r has invalid type %r' %
--> 237                       (fetch, type(fetch)))
    238     elif isinstance(fetch, (list, tuple)):
    239       # NOTE(touts): This is also the code path for namedtuples.
TypeError: Fetch argument None has invalid type <class 'NoneType'>

我以前在此网站上提出过类似的问题，但是这些问题似乎并没有解决我的问题。

任何帮助将不胜感激，谢谢！

Answer 1

问题是 y_out参数为 sess.run()是 None，而它必须是 tf.Tensor(或类似张量的对象，例如 tf.Variable(或 tf.Operation。

在您的示例中，y_out由以下代码定义：

# define model
def complex_model(X,y,is_training):
    pass
y_out = complex_model(X,y,is_training)

complex_model()不返回值，因此y_out = complex_model(...)将y_out设置为None。我不确定此功能是否代表您的真实代码，但是您的实际complex_model()功能也可能缺少return语句。

Answer 2

我相信 mrry 是正确的。

如果您再看笔记本分配2 -tensorflow.ipynb，您将注意到描述单元格，如下：

训练特定模型

在本节中，我们将指定一个供您构建的模型。 这里的目标不是要获得良好的表现(那将是下一个(，而是 相反，要了解张量 文档并配置自己的型号。

使用上面提供的代码作为指导，并使用以下 TensorFlow文档，指定具有以下方式的模型 建筑：

7x7 Convolutional Layer with 32 filters and stride of 1
ReLU Activation Layer
Spatial Batch Normalization Layer (trainable parameters, with scale and centering)
2x2 Max Pooling layer with a stride of 2
Affine layer with 1024 output units
ReLU Activation Layer
Affine layer from 1024 input units to 10 outputs

要求您在功能中定义模型

# define model
def complex_model(X,y,is_training):
    pass

就像他们在

中一样

def simple_model(X,y):
    # define our weights (e.g. init_two_layer_convnet)
    # setup variables
    Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 3, 32])
    bconv1 = tf.get_variable("bconv1", shape=[32])
    W1 = tf.get_variable("W1", shape=[5408, 10])
    b1 = tf.get_variable("b1", shape=[10])
    # define our graph (e.g. two_layer_convnet)
    a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1
    h1 = tf.nn.relu(a1)
    h1_flat = tf.reshape(h1,[-1,5408])
    y_out = tf.matmul(h1_flat,W1) + b1
    return y_out

希望这会有所帮助！