尝试训练我的模型时出现以下错误：
(13369344,)
C
(44733312,)
Epoch 1/5
D
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InvalidArgumentError Traceback (most recent call last)
<ipython-input-45-e13f72904f51> in <module>()
1 model.fit(x=modelTrainGen(maxLen),
2 epochs=5,
---> 3 batch_size=1)
6 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/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_batch_size,
validation_freq, max_queue_size, workers, use_multiprocessing)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in __call__(self,
*args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py in _call(self, *args,
**kwds)
886 # Lifting succeeded, so variables are initialized and we can run the
887 # stateless function.
--> 888 return self._stateless_fn(*args, **kwds)
889 else:
890 _, _, _, filtered_flat_args = \
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in __call__(self, *args,
**kwargs)
2941 filtered_flat_args) = self._maybe_define_function(args, kwargs)
2942 return graph_function._call_flat(
-> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint:
disable=protected-access
2944
2945 @property
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in _call_flat(self, args,
captured_inputs, cancellation_manager)
1917 # No tape is watching; skip to running the function.
1918 return self._build_call_outputs(self._inference_function.call(
-> 1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
1921 args,
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py in call(self, ctx, args,
cancellation_manager)
558 inputs=args,
559 attrs=attrs,
--> 560 ctx=ctx)
561 else:
562 outputs = execute.execute_with_cancellation(
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name,
num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: indices[8618190,0] = -1 is not in [0, 44733312)
[[node sequential_13/embedding_13/embedding_lookup (defined at <ipython-input-45-
e13f72904f51>:3) ]]
[[Adam/Adam/update/AssignSubVariableOp/_33]]
(1) Invalid argument: indices[8618190,0] = -1 is not in [0, 44733312)
[[node sequential_13/embedding_13/embedding_lookup (defined at <ipython-input-45-
e13f72904f51>:3) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_149727]
Errors may have originated from an input operation.
Input Source operations connected to node sequential_13/embedding_13/embedding_lookup:
sequential_13/embedding_13/embedding_lookup/144654 (defined at /usr/lib/python3.7/contextlib.py:112)
Input Source operations connected to node sequential_13/embedding_13/embedding_lookup:
sequential_13/embedding_13/embedding_lookup/144654 (defined at /usr/lib/python3.7/contextlib.py:112)
Function call stack:
train_function -> train_function
C
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这是我的代码：
型号：
model = keras.Sequential()
# Add an Embedding layer expecting input vocab of size 1000, and
# output embedding dimension of size 64.
model.add(layers.Embedding(input_dim=maxLen, output_dim=2,mask_zero=True))
#model.add(layers.Masking())
# Add a LSTM layer with 128 internal units.
#model.add(layers.Input(shape=[1,None]) )
model.add( layers.LSTM(8,return_sequences=True))
model.add( layers.Dropout(0.2) )
model.add( layers.LSTM(8))
model.add( layers.Dropout(0.2) )
# Add a Dense layer with 10 units.
model.add(layers.Dense(16,activation="relu"))
model.add(layers.Dropout(0.2))
model.add(layers.Dense(2,activation="softmax"))
model.compile(loss='categorical_crossentropy',
optimizer='adam')
生成器：
def modelTrainGen(maxLen):
bmTrainDirectory = '/content/drive/MyDrive/death_black_classifier/black_metal_train/'
dmTrainDirectory = '/content/drive/MyDrive/death_black_classifier/death_metal_train/'
dmTrainFileNames = os.listdir(dmTrainDirectory)
bmTrainFileNames = os.listdir(bmTrainDirectory)
maxAudioLen = maxLen
bmTensor = tf.convert_to_tensor([[1],[0]])
dmTensor = tf.convert_to_tensor([[0],[1]])
allFileNames = []
for fileName in zip(bmTrainFileNames,dmTrainFileNames):
bmFileName = fileName[0]
dmFileName = fileName[1]
allFileNames.append((bmFileName,1))
allFileNames.append((dmFileName,0))
random.shuffle(allFileNames)
for fileNameVal in allFileNames:
fileName = fileNameVal[0]
val = fileNameVal[1]
if val == 1:
bmFileName = fileName
audio = tfio.audio.AudioIOTensor(bmTrainDirectory + bmFileName)
audio_slice = tf.reduce_max(tf.transpose(audio[0:]),0)
del audio
print(audio_slice.shape)
padded_x = tf.keras.preprocessing.sequence.pad_sequences( [audio_slice], padding="post",
dtype=float,maxlen=maxAudioLen )
del audio_slice
converted = tf.convert_to_tensor(padded_x[0])
del padded_x
print("A")
print(converted.shape)
yield ( converted,bmTensor)
print("B")
del converted
else:
dmFileName = fileName
audio = tfio.audio.AudioIOTensor(dmTrainDirectory + dmFileName)
audio_slice = tf.reduce_max(tf.transpose(audio[0:]),0)
del audio
print(audio_slice.shape)
padded_x = tf.keras.preprocessing.sequence.pad_sequences( [audio_slice], padding="post",
dtype=float,maxlen=maxAudioLen )
del audio_slice
converted = tf.convert_to_tensor(padded_x[0])
del padded_x
print("C")
print(converted.shape)
yield ( converted,dmTensor)
print("D")
del converted
这个 train_function 错误是什么意思？我看过的其他解决方案到目前为止还没有奏效。我正在尝试制作一个分类器，可以对两种不同类型的歌曲进行分类。如果它看起来不是很好，也可以随意评论架构。单元如此之少的原因是为了确保这不是 RAM 问题。