我对Tensorflow有点陌生，运行这个简单的CNN有点困难。对于每个类，我将图像分为单独的目录，并使用来自目录的图像数据集加载到train_数据集中。

从文档中可以看出，这将产生一个元组（图像，标签），其中图像具有形状（批大小，图像大小[0]，图像大小[1]，数量通道），标签是形状的float32张量（批大小，数量类）。当图像为rgb时，num_通道数为3

然而，当我试图用我的模型拟合时，我得到一个错误，说预测是[32,5]和标签形状[160]。在我看来，标签上的批次似乎已经“崩溃”。

以下是一些片段：

BATCH_SIZE = 32
EPOCHS = 1
IMG_SIZE=(300, 300)
SEED = 1

train_dataset = tf.keras.preprocessing.image_dataset_from_directory(
    directory='train/train_images/', label_mode='categorical', class_names=class_names, color_mode='rgb', batch_size=BATCH_SIZE, image_size=IMG_SIZE)

IMG_SHAPE = IMG_SIZE + (3,)
n_classes = len(train_dataset.class_names)

def build_model():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(input_shape=IMG_SHAPE, kernel_size=(5, 5), filters=32, activation='relu'), 
        tf.keras.layers.MaxPool2D(pool_size=(3, 3)),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(256, activation='relu'),
        tf.keras.layers.Dropout(0.25),
        tf.keras.layers.Dense(units=n_classes, activation='softmax')
    ])
    
    return model

model = build_model()

model.compile(optimizer=tf.keras.optimizers.Adam(),
              loss=tf.keras.losses.sparse_categorical_crossentropy, 
              metrics=['accuracy'])

model.fit(train_dataset, epochs = EPOCHS, batch_size = BATCH_SIZE)

错误消息：

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-19-86d96e744ef0> in <module>
----> 1 model.fit(train_dataset, epochs = EPOCHS, batch_size = BATCH_SIZE)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs)
    106   def _method_wrapper(self, *args, **kwargs):
    107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
--> 108       return method(self, *args, **kwargs)
    109 
    110     # Running inside `run_distribute_coordinator` already.

/opt/conda/lib/python3.7/site-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)
   1096                 batch_size=batch_size):
   1097               callbacks.on_train_batch_begin(step)
-> 1098               tmp_logs = train_function(iterator)
   1099               if data_handler.should_sync:
   1100                 context.async_wait()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    778       else:
    779         compiler = "nonXla"
--> 780         result = self._call(*args, **kwds)
    781 
    782       new_tracing_count = self._get_tracing_count()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    805       # In this case we have created variables on the first call, so we run the
    806       # defunned version which is guaranteed to never create variables.
--> 807       return self._stateless_fn(*args, **kwds)  # pylint: disable=not-callable
    808     elif self._stateful_fn is not None:
    809       # Release the lock early so that multiple threads can perform the call

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
   2827     with self._lock:
   2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
-> 2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
   2830 
   2831   @property

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _filtered_call(self, args, kwargs, cancellation_manager)
   1846                            resource_variable_ops.BaseResourceVariable))],
   1847         captured_inputs=self.captured_inputs,
-> 1848         cancellation_manager=cancellation_manager)
   1849 
   1850   def _call_flat(self, args, captured_inputs, cancellation_manager=None):

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
   1922       # No tape is watching; skip to running the function.
   1923       return self._build_call_outputs(self._inference_function.call(
-> 1924           ctx, args, cancellation_manager=cancellation_manager))
   1925     forward_backward = self._select_forward_and_backward_functions(
   1926         args,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
    548               inputs=args,
    549               attrs=attrs,
--> 550               ctx=ctx)
    551         else:
    552           outputs = execute.execute_with_cancellation(

/opt/conda/lib/python3.7/site-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:  logits and labels must have the same first dimension, got logits shape [32,5] and labels shape [160]
     [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-18-1904262c6a7b>:1) ]] [Op:__inference_train_function_928]

Function call stack:
train_function