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。