Tensorflow Debug:FailedPreconditionError: Attempting to use uninitialized value accuracy/count

问题：

epochs:0/80
---------------------------------------------------------------------------
FailedPreconditionError                   Traceback (most recent call last)
~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1322     try:
-> 1323       return fn(*args)
   1324     except errors.OpError as e:

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(session, feed_dict, fetch_list, target_list, options, run_metadata)
   1301                                    feed_dict, fetch_list, target_list,
-> 1302                                    status, run_metadata)
   1303 

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    472             compat.as_text(c_api.TF_Message(self.status.status)),
--> 473             c_api.TF_GetCode(self.status.status))
    474     # Delete the underlying status object from memory otherwise it stays alive

FailedPreconditionError: Attempting to use uninitialized value accuracy/count
     [[Node: accuracy/count/read = Identity[T=DT_FLOAT, _class=["loc:@accuracy/count"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](accuracy/count)]]

During handling of the above exception, another exception occurred:

FailedPreconditionError                   Traceback (most recent call last)
<ipython-input-5-21dc8cf1ab18> in <module>()
     12             _,_loss = sess.run((train,loss),feed_dict={x:batch_x,y:batch_y})
     13             i+=batch_size
---> 14         _loss,_acc = sess.run((loss,acc),feed_dict={x:train_images_array,y:train_labels_array})
     15         print('loss:%s , acc:%s'%(_loss,_acc))

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
    887     try:
    888       result = self._run(None, fetches, feed_dict, options_ptr,
--> 889                          run_metadata_ptr)
    890       if run_metadata:
    891         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1118     if final_fetches or final_targets or (handle and feed_dict_tensor):
   1119       results = self._do_run(handle, final_targets, final_fetches,
-> 1120                              feed_dict_tensor, options, run_metadata)
   1121     else:
   1122       results = []

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
   1315     if handle is None:
   1316       return self._do_call(_run_fn, self._session, feeds, fetches, targets,
-> 1317                            options, run_metadata)
   1318     else:
   1319       return self._do_call(_prun_fn, self._session, handle, feeds, fetches)

~/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
   1334         except KeyError:
   1335           pass
-> 1336       raise type(e)(node_def, op, message)
   1337 
   1338   def _extend_graph(self):

FailedPreconditionError: Attempting to use uninitialized value accuracy/count
     [[Node: accuracy/count/read = Identity[T=DT_FLOAT, _class=["loc:@accuracy/count"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](accuracy/count)]]

Caused by op 'accuracy/count/read', defined at:
  File "/home/gpu9/anaconda3/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/gpu9/anaconda3/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
    app.launch_new_instance()
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance
    app.start()
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 478, in start
    self.io_loop.start()
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start
    super(ZMQIOLoop, self).start()
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start
    handler_func(fd_obj, events)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
    self._handle_recv()
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
    self._run_callback(callback, msg)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
    callback(*args, **kwargs)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
    return fn(*args, **kwargs)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
    return self.dispatch_shell(stream, msg)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell
    handler(stream, idents, msg)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
    user_expressions, allow_stdin)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute
    res = shell.run_cell(code, store_history=store_history, silent=silent)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 537, in run_cell
    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2728, in run_cell
    interactivity=interactivity, compiler=compiler, result=result)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2850, in run_ast_nodes
    if self.run_code(code, result):
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-4-77f1d929d838>", line 24, in <module>
    acc,acc_op = tf.metrics.accuracy(labels=y,predictions=outlabel)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 410, in accuracy
    updates_collections, name or 'accuracy')
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 331, in mean
    count = _create_local('count', shape=[])
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/metrics_impl.py", line 196, in _create_local
    validate_shape=validate_shape)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1927, in variable
    caching_device=caching_device, name=name, dtype=dtype)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 213, in __init__
    constraint=constraint)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/variables.py", line 356, in _init_from_args
    self._snapshot = array_ops.identity(self._variable, name="read")
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/array_ops.py", line 125, in identity
    return gen_array_ops.identity(input, name=name)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/gen_array_ops.py", line 2071, in identity
    "Identity", input=input, name=name)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 2956, in create_op
    op_def=op_def)
  File "/home/gpu9/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1470, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

FailedPreconditionError (see above for traceback): Attempting to use uninitialized value accuracy/count
     [[Node: accuracy/count/read = Identity[T=DT_FLOAT, _class=["loc:@accuracy/count"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](accuracy/count)]]

问题代码：

x = tf.placeholder(tf.float32,shape=[None,784])
y = tf.placeholder(tf.int32,shape=[None,1])

with tf.variable_scope("fc1"):
    weights1 = tf.get_variable('weight',shape=[784,128],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases1 = tf.get_variable('biases',shape=[128,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out1 = tf.add(tf.matmul(x,weights1),biases1)
    out1 = tf.nn.relu(out1)
    
with tf.variable_scope("fc2"):
    weights2 = tf.get_variable('weight',shape=[128,64],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases2 = tf.get_variable('biases',shape=[64,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out2 = tf.add(tf.matmul(out1,weights2),biases2)
    out2 = tf.nn.relu(out2)
    
with tf.variable_scope("fc3"):
    weights3 = tf.get_variable('weight',shape=[64,10],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases3 = tf.get_variable('biases',shape=[10,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out3 = tf.add(tf.matmul(out2,weights3),biases3)
    out3 = tf.nn.softmax(out3)
    
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=out3)
outlabel = tf.argmax(out3,axis=1)
acc,acc_op = tf.metrics.accuracy(labels=y,predictions=outlabel)
optimizer = tf.train.AdamOptimizer(learning_rate=0.0002)
train = optimizer.minimize(loss)
batch_size = 128
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(80):
        i = int(0)
        lenth = len(train_images_array)
        print('epochs:%d/80'%i)
        while i<lenth:
            r = i+batch_size
            if r>=lenth: r=lenth-1
            batch_x = train_images_array[i:r]
            batch_y = train_labels_array[i:r]
            _,_loss = sess.run((train,loss),feed_dict={x:batch_x,y:batch_y})
            i+=batch_size
        _loss,_acc = sess.run((loss,acc),feed_dict={x:train_images_array,y:train_labels_array})
        print('loss:%s , acc:%s'%(_loss,_acc))

问题主要在tf.metrics.accuracy的使用。

后来查阅文档https://tensorflow.google.cn/api_docs/python/tf/metrics/accuracy发现，tf.metrics.accuracy会产生两个局部变量 count 和 total。

经过大神指点https://stackoverflow.com/questions/46409626/how-to-properly-use-tf-metrics-accuracy，发现需要加入sess.run(tf.local_variables_initializer())

更新代码为：

x = tf.placeholder(tf.float32,shape=[None,784])
y = tf.placeholder(tf.int32,shape=[None,1])

with tf.variable_scope("fc1"):
    weights1 = tf.get_variable('weight',shape=[784,128],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases1 = tf.get_variable('biases',shape=[128,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out1 = tf.add(tf.matmul(x,weights1),biases1)
    out1 = tf.nn.relu(out1)
    
with tf.variable_scope("fc2"):
    weights2 = tf.get_variable('weight',shape=[128,64],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases2 = tf.get_variable('biases',shape=[64,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out2 = tf.add(tf.matmul(out1,weights2),biases2)
    out2 = tf.nn.relu(out2)
    
with tf.variable_scope("fc3"):
    weights3 = tf.get_variable('weight',shape=[64,10],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    biases3 = tf.get_variable('biases',shape=[10,],dtype=tf.float32,initializer=tf.glorot_uniform_initializer())
    out3 = tf.add(tf.matmul(out2,weights3),biases3)
    out3 = tf.nn.softmax(out3)
    
loss = tf.losses.sparse_softmax_cross_entropy(labels=y,logits=out3)
outlabel = tf.argmax(out3,axis=1)
acc,acc_op = tf.metrics.accuracy(labels=y,predictions=outlabel)
optimizer = tf.train.AdamOptimizer(learning_rate=0.0002)
train = optimizer.minimize(loss)
batch_size = 128
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    sess.run(tf.local_variables_initializer()) #tf.metrics.accuracy会产生两个局部变量
    for i in range(80):
        l = int(0)
        lenth = len(train_images_array)
        print('epochs:%d/80'%i)
        while l<lenth:
            r = l+batch_size
            if r>=lenth: r=lenth-1
            batch_x = train_images_array[l:r]
            batch_y = train_labels_array[l:r]
            _,_loss = sess.run((train,loss),feed_dict={x:batch_x,y:batch_y})
            l+=batch_size
        _loss,_acc_op = sess.run((loss,acc_op),feed_dict={x:train_images_array,y:train_labels_array})
        _acc = sess.run((acc),feed_dict={x:train_images_array,y:train_labels_array})
        print('loss:%s , acc:%s'%(_loss,_acc))

 

解决问题。