我尝试在Keras中创建一个卷积自动编码器网络，但是，autoencoder.fit（）函数给我一个错误，即“logits”和“labels”必须具有相同的形状。我不确定“logits”指的是什么，并且我不认为我将标签传递到了fit中？如果是这样，我不太明白在哪里...有人能看到是什么导致了这个错误吗？

data = tf.keras.utils.image_dataset_from_directory('/content/gdrive/MyDrive/Skyrmion Vision/testFiles/train/',batch_size=32,image_size=(237,237),shuffle=False)
data_iterator = data.as_numpy_iterator()

#get another batch from the iterator
batch = data_iterator.next()

len(batch) # theres the images and theres the labels -- images are represented as numpy arrays

batch[0].shape

batch[1]

#different based on what the data_iterator grabs
fig, ax = plt.subplots(ncols=4, figsize=(20,20))
for idx, img in enumerate(batch[0][:4]):
    ax[idx].imshow(img.astype(int))
    ax[idx].title.set_text(batch[1][idx])

"""## **SCALE DATA**"""
# functions available to apply to pipeline, this is using the map() function: https://www.tensorflow.org/api_docs/python/tf/data/Dataset
data = data.map(lambda x,y: (x/255, y))
scaled_iterator = data.as_numpy_iterator()
batch = scaled_iterator.next()[0]
batch[0].max()

"""## **Split Data**"""

len(data) # 127 batches, each batch with 32 images
train_size = int(len(data)*.7)+2
val_size = int(len(data)*.2)
test_size = int(len(data)*.1)
print(train_size,val_size,test_size)
train_size+val_size+test_size

# from  https://www.tensorflow.org/api_docs/python/tf/data/Dataset
train = data.take(train_size)
val = data.skip(train_size).take(val_size)
test = data.skip(train_size+val_size).take(test_size)
len(train)

"""## **DEEP MODEL**"""
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model

data_iterator = data.as_numpy_iterator()
batch = data_iterator.next()

"""**Encoding Process**"""
input_img = Input(shape=(237, 237, 3))

# Conv1 #
x = Conv2D(filters = 32, kernel_size = (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D(pool_size = (2, 2), padding='same')(x)

# Conv2 #
x = Conv2D(filters = 16, kernel_size = (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D(pool_size = (2, 2), padding='same')(x) 

# Conv 3 #
x = Conv2D(filters = 8, kernel_size = (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D(pool_size = (2, 2), padding='same')(x)

"""**Decoding Process**"""
# DeConv1
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)

# DeConv2
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)

# Deconv3
x = Conv2D(32, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

"""**Declare the Model**"""

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

# Train the model
autoencoder.fit(batch[0], batch[0],
                epochs=20,
                shuffle=False,
                #validation_data=(x_test, x_test)
               )

这就给了我一个我一直在纠结的错误：

Epoch 1/20
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-70-62d669270126> in <module>
      2 autoencoder.fit(batch[0], batch[0],
      3                 epochs=20,
----> 4                 shuffle=False,
      5                 #validation_data=(x_test, x_test)
      6                )

1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in autograph_handler(*args, **kwargs)
   1145           except Exception as e:  # pylint:disable=broad-except
   1146             if hasattr(e, "ag_error_metadata"):
-> 1147               raise e.ag_error_metadata.to_exception(e)
   1148             else:
   1149               raise

ValueError: in user code:

    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1021, in train_function  *
        return step_function(self, iterator)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1010, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 1000, in run_step  **
        outputs = model.train_step(data)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 860, in train_step
        loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py", line 919, in compute_loss
        y, y_pred, sample_weight, regularization_losses=self.losses)
    File "/usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 141, in __call__
        losses = call_fn(y_true, y_pred)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 245, in call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "/usr/local/lib/python3.7/dist-packages/keras/losses.py", line 1932, in binary_crossentropy
        backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
    File "/usr/local/lib/python3.7/dist-packages/keras/backend.py", line 5247, in binary_crossentropy
        return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)

    ValueError: `logits` and `labels` must have the same shape, received ((None, 236, 236, 1) vs (None, 237, 237, 3)).