tensorflow - 警告：张量流：提前停止以不可用的度量“val_binary_accuracy”为条件

Question

张量流版本：2.0.0

显卡：英伟达 2080ti

库达：10.1

cudnn: 7.6.5

我使用 keras 作为 tensorflow.keras

我正在使用 Conv-LSTM 模型

inputs = Input(shape=(480,3))
conv1 = Conv1D(16, 8, strides =1 , padding='same', activation='relu')(inputs)
conv2 = Conv1D(32, 8, strides =1 , padding='same', activation='relu')(conv1)
conv3 = Conv1D(64, 8, strides =1 , padding='same', activation='relu')(conv2)
lstm1 = LSTM(32)(conv3)
output = Dense(1,activation='sigmoid')(lstm1)
model = Model(inputs=inputs,outputs= output)

编译并运行：

model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['binary_accuracy'])
#checkpoint = ModelCheckpoint('weight_seden.h5', monitor='binary_accuracy',save_best_only=True,mode='max')
earlystop = EarlyStopping(monitor = 'val_binary_accuracy',patience =4,mode = 'max')
history = model.fit(X_train, Y_train,batch_size=15,validation_data=(X_val,Y_val),class_weight=train_weights,epochs=50,callbacks=[earlystop])

输出：

Train on 58271 samples, validate on 10284 samples
Epoch 1/50
    1/58271 [..............................] - ETA: 37:12:15WARNING:tensorflow:Early stopping conditioned on metric `val_binary_accuracy` which is not available. Available metrics are: 

错误：

---------------------------------------------------------------------------
UnknownError                              Traceback (most recent call last)
<ipython-input-15-269f98dc1caf> in <module>
      3 earlystop = EarlyStopping(monitor = 'val_binary_accuracy',patience =4,mode = 'max')
      4 #history = model.fit(X_train, Y_train,batch_size=15,validation_data=(X_val,Y_val),class_weight=train_weights,epochs=50,callbacks=[earlystop])
----> 5 history = model.fit(X_train, Y_train,batch_size=1,validation_split=0.15,class_weight=train_weights,epochs=50,callbacks=[earlystop])

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    726         max_queue_size=max_queue_size,
    727         workers=workers,
--> 728         use_multiprocessing=use_multiprocessing)
    729 
    730   def evaluate(self,

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
    322                 mode=ModeKeys.TRAIN,
    323                 training_context=training_context,
--> 324                 total_epochs=epochs)
    325             cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
    326 

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)
    121         step=step, mode=mode, size=current_batch_size) as batch_logs:
    122       try:
--> 123         batch_outs = execution_function(iterator)
    124       except (StopIteration, errors.OutOfRangeError):
    125         # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py in execution_function(input_fn)
     84     # `numpy` translates Tensors to values in Eager mode.
     85     return nest.map_structure(_non_none_constant_value,
---> 86                               distributed_function(input_fn))
     87 
     88   return execution_function

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in __call__(self, *args, **kwds)
    455 
    456     tracing_count = self._get_tracing_count()
--> 457     result = self._call(*args, **kwds)
    458     if tracing_count == self._get_tracing_count():
    459       self._call_counter.called_without_tracing()

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in _call(self, *args, **kwds)
    518         # Lifting succeeded, so variables are initialized and we can run the
    519         # stateless function.
--> 520         return self._stateless_fn(*args, **kwds)
    521     else:
    522       canon_args, canon_kwds = \

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in __call__(self, *args, **kwargs)
   1821     """Calls a graph function specialized to the inputs."""
   1822     graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 1823     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   1824 
   1825   @property

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _filtered_call(self, args, kwargs)
   1139          if isinstance(t, (ops.Tensor,
   1140                            resource_variable_ops.BaseResourceVariable))),
-> 1141         self.captured_inputs)
   1142 
   1143   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1222     if executing_eagerly:
   1223       flat_outputs = forward_function.call(
-> 1224           ctx, args, cancellation_manager=cancellation_manager)
   1225     else:
   1226       gradient_name = self._delayed_rewrite_functions.register()

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    509               inputs=args,
    510               attrs=("executor_type", executor_type, "config_proto", config),
--> 511               ctx=ctx)
    512         else:
    513           outputs = execute.execute_with_cancellation(

~/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     65     else:
     66       message = e.message
---> 67     six.raise_from(core._status_to_exception(e.code, message), None)
     68   except TypeError as e:
     69     keras_symbolic_tensors = [

~/anaconda3/envs/tf/lib/python3.7/site-packages/six.py in raise_from(value, from_value)

UnknownError:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[node model/conv1d/conv1d (defined at /home/subhashnerella/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_distributed_function_3609]

Function call stack:
distributed_function

我发现了一个类似的问题，比如我面临的这个问题。解决方案是检查验证集是否非零。我确保我的验证集不为零。在上面的时代也可以看到。这个确切的代码几天前就可以工作了。我没有改变任何东西，但我面临这个问题。我尝试减少没有帮助的批量大小。我该如何解决？

标签： tensorflowkerasconv-neural-networklstm

Answer 1

要解决 EarlyStopping 警告，请使用：

earlystop = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=4)
# Stop the training when there is no improvement in valid. loss for 4 consecutive epochs.

但你的错误显然来自

-> use_multiprocessing=use_multiprocessing)

和

Failed to get convolution algorithm. This is probably because cuDNN failed to initialize

请让我知道它是否有帮助。