我正在尝试设计一个多类别分类神经网络。
我的train_x数据集包含23个样本，每个样本有37个特征（维度：23*37）
train_y包含每个样本的输出（维度：23*7）[7个标签/类别]。我对每个样本的输出使用了一键编码。
len(words)是特征的数量。这是我的模型设计：
model = tf.keras.models.Sequential([ tf.keras.layers.Dense(units=len(words), input_shape=[len(words)]), tf.keras.layers.Dense(8, activation="relu"), tf.keras.layers.Dense(8, activation="relu"), tf.keras.layers.Dense(len(labels), activation="softmax")])我使用了Adam优化器作为优化器，并使用了稀疏分类熵作为损失函数。
model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=['accuracy'])model.fit(train_x, train_y, epochs=100)我得到了以下追溯调用：
Epoch 1/100Traceback (most recent call last): File "main.py", line 83, in <module> model.fit(train_x, train_y, epochs=100, callbacks=[callbacks]) File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 66, in _method_wrapper return method(self, *args, **kwargs) File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\engine\training.py", line 848, in fit tmp_logs = train_function(iterator) File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 580, in __call__ result = self._call(*args, **kwds) File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\def_function.py", line 644, in _call return self._stateless_fn(*args, **kwds) File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 2420, in __call__ return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 1661, in _filtered_call return self._call_flat( File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 1745, in _call_flat return self._build_call_outputs(self._inference_function.call( File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\function.py", line 593, in call outputs = execute.execute( File "C:\Users\aaman\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,tensorflow.python.framework.errors_impl.InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [23,7] and labels shape [161] [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at main.py:83) ]] [Op:__inference_train_function_709]Function call stack:train_function我已经在各种网站上搜索了两天。但他们都对第一层展平了输入数据。所有这些都使用灰度图像或RGB图像作为输入。所有这些都要求第一层被展平。但我的输入数据已经是展平的了。
据我所知，我在第一层得到了这个追溯调用。我可能误解了units和input_shape的概念，因此错误地定义了它们。
回答：
将sparse_categorical_crossentropy更改为categorical_crossentropy。
