我正在训练一种面部表情（愤怒与快乐）模型。最后一个密集的输出层以前是1，但是当我预测图像时，其输出始终为1，准确度为64％。所以我将其更改为2 2输出。但是现在我收到了这个错误：：
Epoch 1/15
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-54-9c7272c38dcb> in <module>()
11 epochs=epochs,
12 validation_data = val_data_gen,
---> 13 validation_steps = validation_steps,
14
15 )
10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
966 except Exception as e: # pylint:disable=broad-except
967 if hasattr(e, "ag_error_metadata"):
--> 968 raise e.ag_error_metadata.to_exception(e)
969 else:
970 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:571 train_function *
outputs = self.distribute_strategy.run(
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run **
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:533 train_step **
y, y_pred, sample_weight, regularization_losses=self.losses)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/compile_utils.py:205 __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:143 __call__
losses = self.call(y_true, y_pred)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:246 call
return self.fn(y_true, y_pred, **self._fn_kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/losses.py:1527 categorical_crossentropy
return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:4561 categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/tensor_shape.py:1117 assert_is_compatible_with
raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (None, 1) and (None, 2) are incompatible
相关代码为：
model = Sequential([
Conv2D(32,3, activation='relu', input_shape=(48,48,1)),
BatchNormalization(),
MaxPooling2D(pool_size=(3, 3)),
Flatten(),
Dense(512, activation='relu'),
Dense(2,activation='softmax')
])
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
Model: "sequential_4"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_6 (Conv2D) (None, 46, 46, 32) 320
_________________________________________________________________
batch_normalization_4 (Batch (None, 46, 46, 32) 128
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 7200) 0
_________________________________________________________________
dense_8 (Dense) (None, 512) 3686912
_________________________________________________________________
dense_9 (Dense) (None, 2) 1026
=================================================================
Total params: 3,688,386
Trainable params: 3,688,322
Non-trainable params: 64
_________________________________________________________________
epochs = 15
steps_per_epoch = train_data_gen.n//train_data_gen.batch_size
validation_steps = val_data_gen.n//val_data_gen.batch_size
history = model.fit(
x=train_data_gen,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data = val_data_gen,
validation_steps = validation_steps,
)
答案 0 :(得分：19)
因为输出标签是二进制的，所以将分类交叉熵更改为二进制交叉熵。也将Softmax更改为Sigmoid，因为Sigmoid是二进制数据的正确激活功能
答案 1 :(得分：8)
我遇到了同样的问题 我的形状是
shape of X (271, 64, 64, 3)
shape of y (271,)
shape of trainX (203, 64, 64, 3)
shape of trainY (203, 1)
shape of testX (68, 64, 64, 3)
shape of testY (68, 1)
和
loss="categorical_crossentropy"
我改成
loss="sparse_categorical_crossentropy"
它对我来说就像一个魅力
答案 2 :(得分：3)
您可以将标签从二进制值更改为分类值，然后继续相同的代码。例如，
from keras.utils import to_categorical
one_hot_label = to_cateorical(input_labels)
# change to [1, 0, 0,..., 0] --> [[0, 1], [1, 0], ..., [1, 0]]
您可以通过此链接来更好地了解Keras API。
如果要将分类交叉熵用于两个类别，请使用softmax并进行一次热编码。无论是哪种二进制分类，都可以像使用Sigmoid激活函数那样使用二进制交叉熵，如前面的答案中所述。
model = Sequential([
Conv2D(32,3, activation='relu', input_shape=(48,48,1)),
BatchNormalization(),
MaxPooling2D(pool_size=(3, 3)),
Flatten(),
Dense(512, activation='relu'),
Dense(2,activation='softmax') # activation change
])
model.compile(optimizer='adam',
loss='categorical_crossentropy', # Loss
metrics=['accuracy'])
model = Sequential([
Conv2D(32,3, activation='relu', input_shape=(48,48,1)),
BatchNormalization(),
MaxPooling2D(pool_size=(3, 3)),
Flatten(),
Dense(512, activation='relu'),
Dense(1,activation='sigmoid') #activation change
])
model.compile(optimizer='adam',
loss='binary_crossentropy', # Loss
metrics=['accuracy'])
答案 3 :(得分：3)
如果您的数据集已加载image_dataset_from_directory，请使用label_mode='categorial'
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
path,
label_mode='categorial'
)
或加载flow_from_directory，flow_from_dataframe，然后使用class_mode='categorical'
train_ds = ImageDataGenerator.flow_from_directory(
path,
class_mode='categorical'
)
答案 4 :(得分：1)
即使遇到同样的问题，我也用class_mode='categorical'方法中的class_mode='binary'而不是flow_from_directory来代替对我有用的
答案 5 :(得分：1)
正如@Akash 指出的那样，应该将您的标签转换为单热编码，如下所示：
y = keras.utils.to_categorical(y, num_classes=num_classes_in_your_case)
答案 6 :(得分：0)
我自己遇到了这个问题，就我而言，问题出在模型的声明中。我试图使用 VGG16 进行迁移学习，但我用错误的层代替了输出。我没有使用我创建的预测层，而是使用了另一个层。因此，如果您在遇到此错误时放错了任何图层，请查看您的模型。