检查目标时出错:期望dense_3具有形状(256个)，但得到形状为(1 )的数组

Question

我正在Keras中训练一个类似VGG16 16的模型，试图预测具有输入映像的连续/时间到事件值(回归)，并遇到以下错误：

检查目标时出错:期望dense_3具有形状(256个)，但得到形状为(1 )的数组

这是模型的结构：

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 256, 256, 64)      1792      
_________________________________________________________________
batch_normalization_1 (Batch (None, 256, 256, 64)      256       
_________________________________________________________________
.
.
.

_________________________________________________________________
conv2d_16 (Conv2D)           (None, 16, 16, 512)       2359808   
_________________________________________________________________
batch_normalization_16 (Batc (None, 16, 16, 512)       2048      
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 8, 8, 512)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 32768)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 1000)              32769000  
_________________________________________________________________
dense_2 (Dense)              (None, 1000)              1001000   
_________________________________________________________________
dense_3 (Dense)              (None, 256)               256256    

Total params: 54,072,656
Trainable params: 54,061,648
Non-trainable params: 11,008

我试着在最后添加另一个层，只有一个神经元，它似乎是这样工作的，但我认为这不是正确的方法。我曾读到过类似的问题，但我未能找到解决办法。

下面代码构建的模型的最后几层

#Convolution Layer
#input: 64x64x128, image with 128 channels, appy 256 convolution filters
model.add(Conv2D(512, kernel_size=3, activation='relu',padding='same' ))
#the output of the layer above is 64x64x256

#Normalization layer
model.add(BatchNormalization())

#Convolution Layer
#input: 64x64x128, image with 128 channels, appy 256 convolution filters
model.add(Conv2D(512, kernel_size=3, activation='relu',padding='same' ))
#the output of the layer above is 64x64x256

#Normalization layer
model.add(BatchNormalization())

#Max-Pooling
#poolsize:(2,2), factors by which to downscale (vertical, horizontal)
model.add(MaxPooling2D(pool_size=(2,2), dim_ordering="tf"))

#Flatten layer
model.add(Flatten())

#Fully connected layer 
#number of neurons is chosen randomly
model.add(Dense(1000, activation='relu'))

#Fully connected layer 
model.add(Dense(1000, activation='relu'))


#Fully connected layer 
model.add(Dense(256, activation='softmax'))

model.summary()


#Compile model
model.compile(loss='categorical_crossentropy', optimizer='adagrad')

我也不确定在预测时间对事件值的情况下，应该使用哪种损失函数。

Answer 1

model.add(Dense(256, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adagrad')

将最后一层更改为重新激活，最后添加一个线性激活层。使用MSE损失，因为这是一个回归问题。