我正在尝试使用xtrain=(1221,50,50,1)运行模型在model.fit时显示此错误
[ValueError：检查输入时出错：预期input_1具有5个维，但数组的形状为（1221，50，50，1）] >>
使用这些功能的即时消息：
model.compile(loss=categorical_crossentropy,optimizer=Adadelta(lr=0.8), metrics=['acc']) model.fit(x=ZZ, y=yyy, batch_size=128, epochs=1, validation_split=0.2)当我将尺寸增加到（1221,50,50,1,1）时使用expand_dims即时通讯收到此错误：
[ValueError：检查输入时出错：预期输入_1具有形状（16、50、50、1），但具有形状为（1221、50、50、1、1）的数组]
Idk即时通讯出现问题
这是我的模特
input_layer = Input((16, 50, 50, 1))
## convolutional layers
conv_layer1 = Conv3D(filters=8, kernel_size=(3, 3, 3), activation='relu')(input_layer)
conv_layer2 = Conv3D(filters=16, kernel_size=(3, 3, 3), activation='relu')(conv_layer1)
## add max pooling to obtain the most imformatic features
pooling_layer1 = MaxPool3D(pool_size=(2, 2, 2))(conv_layer2)
conv_layer3 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu')(pooling_layer1)
conv_layer4 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu')(conv_layer3)
pooling_layer2 = MaxPool3D(pool_size=(2, 2, 2))(conv_layer4)
## perform batch normalization on the convolution outputs before feeding it to MLP architecture
pooling_layer2 = BatchNormalization()(pooling_layer2)
flatten_layer = Flatten()(pooling_layer2)
## create an MLP architecture with dense layers : 4096 -> 512 -> 10
## add dropouts to avoid overfitting / perform regularization
dense_layer1 = Dense(units=2048, activation='relu')(flatten_layer)
dense_layer1 = Dropout(0.4)(dense_layer1)
dense_layer2 = Dense(units=512, activation='relu')(dense_layer1)
dense_layer2 = Dropout(0.4)(dense_layer2)
output_layer = Dense(units=6, activation='softmax')(dense_layer2) #Use 5 instead of 1
## define the model with input layer and output layer
model = Model(inputs=input_layer, outputs=output_layer)```
我正在尝试在模型生成时使用xtrain =（1221,50,50,1）运行模型。显示其此错误ValueError：检查输入时出错：预期input_1具有5个维度，但数组包含。 ..
input_layer = Input((16, 50, 50, 1))
问题出在xtrain中，它包含1221个形状为（50,50,1）的图像，但是Conv3D期望使用形状为(16,50,50,1)的图像。