Question

在此代码中将tensorboard作为回调包含在内会输出错误：-

window_sizes=[3,5]
conv_layers=[1,2]
dense_layers=[1]
for ws in window_sizes:
    for cl in conv_layers:
        for dl in dense_layers:
            name="{}-conv_layers-{}-window_size-{}-dense_layers-{}".format(cl,ws,dl,int(time.time()))
            #keras.backend.clear_session()
            model=Sequential()
            model.add(Conv2D(64,(ws,ws),input_shape=X.shape[1:]))   
            model.add(Activation('relu'))
            model.add(MaxPooling2D(pool_size=(3,3)))
            for i in range(cl-1):
                model.add(Conv2D(64,(ws,ws)))
                model.add(Activation('relu'))
                model.add(MaxPooling2D(pool_size=(3,3)))

            model.add(Flatten())
            for i in range(dl):
                model.add(Dense(64))
                model.add(Activation("relu"))
                model.add(Dropout(0.2))
            model.add(Dense(1))
            model.add(Activation("sigmoid"))
            tensorboard = TensorBoard(log_dir="logs/{}".format(name))
            model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])
            model.fit(X,y,batch_size=32,epochs=3,validation_split=0.2,callbacks=[tensorboard])

但是，从回调列表中删除tensorboard修复了该错误。我尝试使用回调列表中包含的tensorboard解决该错误，但似乎没有任何解决方法。这是错误：-

1-conv_layers-3-window_size-1-dense_layers-1546041626
Train on 19302 samples, validate on 4826 samples
Epoch 1/3
---------------------------------------------------------------------------
FailedPreconditionError                   Traceback (most recent call last)
<ipython-input-10-f65d5bcffb55> in <module>()
     25             tensorboard = TensorBoard(log_dir="logs/{}".format(name))
     26             model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])
---> 27             model.fit(X,y,batch_size=32,epochs=3,validation_split=0.2,callbacks=[tensorboard])
     28 #1-conv_layers-3-window_size-1-dense_layers-1546011295----98.59
     29 #1-conv_layers-3-window_size-2-dense_layers-1546011765----98.15

~\Anaconda3\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
   1037                                         initial_epoch=initial_epoch,
   1038                                         steps_per_epoch=steps_per_epoch,
-> 1039                                         validation_steps=validation_steps)
   1040 
   1041     def evaluate(self, x=None, y=None,

~\Anaconda3\lib\site-packages\keras\engine\training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
    197                     ins_batch[i] = ins_batch[i].toarray()
    198 
--> 199                 outs = f(ins_batch)
    200                 outs = to_list(outs)
    201                 for l, o in zip(out_labels, outs):

~\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in __call__(self, inputs)
   2713                 return self._legacy_call(inputs)
   2714 
-> 2715             return self._call(inputs)
   2716         else:
   2717             if py_any(is_tensor(x) for x in inputs):

~\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py in _call(self, inputs)
   2673             fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
   2674         else:
-> 2675             fetched = self._callable_fn(*array_vals)
   2676         return fetched[:len(self.outputs)]
   2677 

~\Anaconda3\lib\site-packages\tensorflow\python\client\session.py in __call__(self, *args, **kwargs)
   1380           ret = tf_session.TF_SessionRunCallable(
   1381               self._session._session, self._handle, args, status,
-> 1382               run_metadata_ptr)
   1383         if run_metadata:
   1384           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\Anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    517             None, None,
    518             compat.as_text(c_api.TF_Message(self.status.status)),
--> 519             c_api.TF_GetCode(self.status.status))
    520     # Delete the underlying status object from memory otherwise it stays alive
    521     # as there is a reference to status from this from the traceback due to

FailedPreconditionError: Attempting to use uninitialized value training_10/Adam/Variable_9
     [[Node: training_10/Adam/Variable_9/read = Identity[T=DT_FLOAT, _class=["loc:@training_10/Adam/Assign_10"], _device="/job:localhost/replica:0/task:0/device:GPU:0"](training_10/Adam/Variable_9)]]
     [[Node: metrics_10/acc/Mean_1/_1111 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_670_metrics_10/acc/Mean_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]

有人建议在创建模型之前添加keras.backend.clear_session（），但这还是无济于事。

Answer 1

使用此

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten,Conv2D, MaxPooling2D,Activation

代替

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten,Conv2D, MaxPooling2D,Activation

为我修复了

将TensorBoard用作Keras模型拟合中的回调会导致FailedPreconditionError

1 个答案: