关于tensorflow：错误：预期conv3d_1_input具有5个维度，但数组的形状为(10，224，224，3)

Error: expected conv3d_1_input to have 5 dimensions, but got array with shape (10, 224, 224, 3)

我正在尝试在数据集上训练神经网络以进行生动的反欺骗。我在名为genuine和fake的两个文件夹中有一些视频。我已提取每个视频的10帧并将它们保存在两个具有上述名称的文件夹中，位于新目录tarining下。

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--/training/
----/genuine/ #containes 10frame*300videos=3000images
----/fake/ #containes 10frame*800videos=8000images

我第一次尝试使用Keras设计以下3D Convent，但是运行它之后，它将引发以下异常：

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from keras.preprocessing.image import ImageDataGenerator
from keras import Model, optimizers, activations, losses, regularizers, backend, Sequential
from keras.layers import Dense, MaxPooling3D, AveragePooling3D, Conv3D, Input, Flatten, BatchNormalization

BATCH_SIZE = 10
TARGET_SIZE = (224, 224)

train_datagen = ImageDataGenerator(rescale=1.0/255,
data_format='channels_last',
validation_split=0.2,
shear_range=0.0,
zoom_range=0,
horizontal_flip=False,
featurewise_center=False,
featurewise_std_normalization=False,
width_shift_range=False,
height_shift_range=False)

train_generator = train_datagen.flow_from_directory("./training/",
target_size=TARGET_SIZE,
batch_size=BATCH_SIZE,
class_mode='binary',
shuffle=False,
subset='training')

validation_generator = train_datagen.flow_from_directory("./training/",
target_size=TARGET_SIZE,
batch_size=BATCH_SIZE,
class_mode='binary',
shuffle=False,
subset='validation')

SHAPE = (10, 224, 224, 3)
model = Sequential()
model.add(Conv3D(filters=128, kernel_size=(1, 3, 3), data_format='channels_last', activation='relu', input_shape=(10, 224, 224, 3)))
model.add(MaxPooling3D(data_format='channels_last', pool_size=(1, 2, 2)))
model.add(Conv3D(filters=64, kernel_size=(2, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2)))
model.add(Conv3D(filters=32, kernel_size=(2, 3, 3), activation='relu'))
model.add(Conv3D(filters=32, kernel_size=(2, 3, 3), activation='relu'))
model.add(MaxPooling3D(pool_size=(1, 2, 2)))
model.add(Conv3D(filters=16, kernel_size=(2, 3, 3), activation='relu'))
model.add(Conv3D(filters=16, kernel_size=(2, 3, 3), activation='relu'))
model.add(AveragePooling3D())
model.add(BatchNormalization())
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.summary()
model.compile(optimizer=optimizers.adam(), loss=losses.binary_crossentropy, metrics=['accuracy'])
model.fit_generator(train_generator, steps_per_epoch=train_generator.samples/train_generator.batch_size, epochs=5, validation_data=validation_generator, validation_steps=validation_generator.samples/validation_generator.batch_size)
model.save('3d.h5')

这是错误：

1	ValueError: Error when checking input: expected conv3d_1_input to have 5 dimensions, but got array with shape (10, 224, 224, 3)

这是model.summary()的输出

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Model:"sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv3d_1 (Conv3D) (None, 10, 222, 222, 128) 3584
_________________________________________________________________
max_pooling3d_1 (MaxPooling3 (None, 10, 111, 111, 128) 0
_________________________________________________________________
conv3d_2 (Conv3D) (None, 9, 109, 109, 64) 147520
_________________________________________________________________
max_pooling3d_2 (MaxPooling3 (None, 9, 54, 54, 64) 0
_________________________________________________________________
conv3d_3 (Conv3D) (None, 8, 52, 52, 32) 36896
_________________________________________________________________
conv3d_4 (Conv3D) (None, 7, 50, 50, 32) 18464
_________________________________________________________________
max_pooling3d_3 (MaxPooling3 (None, 7, 25, 25, 32) 0
_________________________________________________________________
conv3d_5 (Conv3D) (None, 6, 23, 23, 16) 9232
_________________________________________________________________
conv3d_6 (Conv3D) (None, 5, 21, 21, 16) 4624
_________________________________________________________________
average_pooling3d_1 (Average (None, 2, 10, 10, 16) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 2, 10, 10, 16) 64
_________________________________________________________________
dense_1 (Dense) (None, 2, 10, 10, 32) 544
_________________________________________________________________
dense_2 (Dense) (None, 2, 10, 10, 1) 33
=================================================================
Total params: 220,961
Trainable params: 220,929
Non-trainable params: 32
__________________________________________________________

感谢您为解决该异常提供的帮助。顺便说一句，如果它有助于解决问题，我正在使用TensorFlow作为后端。