实际通过tf.loadLayersModel拿到的mobilenet的模型和课程中不一样要怎么处理呢？后续出现报错

下面是调用

const mobilenet = await tf.loadLayersModel(MOBILENET_URL)

    mobilenet.summary()

打印的数据如下

_________________________________________________________________

Layer (type)                 Output shape              Param #   

=================================================================

input_1 (InputLayer)         [null,null,null,3]        0

_________________________________________________________________

conv1 (Conv2D)               [null,null,null,32]       864

_________________________________________________________________

conv1_bn (BatchNormalization [null,null,null,32]       128

_________________________________________________________________

conv1_relu (ReLU)            [null,null,null,32]       0

_________________________________________________________________

conv_dw_1 (DepthwiseConv2D)  [null,null,null,32]       288

_________________________________________________________________

conv_dw_1_bn (BatchNormaliza [null,null,null,32]       128

_________________________________________________________________

conv_dw_1_relu (ReLU)        [null,null,null,32]       0

_________________________________________________________________

conv_pw_1 (Conv2D)           [null,null,null,64]       2048

_________________________________________________________________

conv_pw_1_bn (BatchNormaliza [null,null,null,64]       256

_________________________________________________________________

conv_pw_1_relu (ReLU)        [null,null,null,64]       0

_________________________________________________________________

conv_pad_2 (ZeroPadding2D)   [null,null,null,64]       0

_________________________________________________________________

conv_dw_2 (DepthwiseConv2D)  [null,null,null,64]       576

_________________________________________________________________

conv_dw_2_bn (BatchNormaliza [null,null,null,64]       256

_________________________________________________________________

conv_dw_2_relu (ReLU)        [null,null,null,64]       0

_________________________________________________________________

conv_pw_2 (Conv2D)           [null,null,null,128]      8192

_________________________________________________________________

conv_pw_2_bn (BatchNormaliza [null,null,null,128]      512

_________________________________________________________________

conv_pw_2_relu (ReLU)        [null,null,null,128]      0

_________________________________________________________________

conv_dw_3 (DepthwiseConv2D)  [null,null,null,128]      1152

_________________________________________________________________

conv_dw_3_bn (BatchNormaliza [null,null,null,128]      512

_________________________________________________________________

conv_dw_3_relu (ReLU)        [null,null,null,128]      0

_________________________________________________________________

conv_pw_3 (Conv2D)           [null,null,null,128]      16384

_________________________________________________________________

conv_pw_3_bn (BatchNormaliza [null,null,null,128]      512

_________________________________________________________________

conv_pw_3_relu (ReLU)        [null,null,null,128]      0

_________________________________________________________________

conv_pad_4 (ZeroPadding2D)   [null,null,null,128]      0

_________________________________________________________________

conv_dw_4 (DepthwiseConv2D)  [null,null,null,128]      1152

_________________________________________________________________

conv_dw_4_bn (BatchNormaliza [null,null,null,128]      512

_________________________________________________________________

conv_dw_4_relu (ReLU)        [null,null,null,128]      0

_________________________________________________________________

conv_pw_4 (Conv2D)           [null,null,null,256]      32768

_________________________________________________________________

conv_pw_4_bn (BatchNormaliza [null,null,null,256]      1024

_________________________________________________________________

conv_pw_4_relu (ReLU)        [null,null,null,256]      0

_________________________________________________________________

conv_dw_5 (DepthwiseConv2D)  [null,null,null,256]      2304

_________________________________________________________________

conv_dw_5_bn (BatchNormaliza [null,null,null,256]      1024

_________________________________________________________________

conv_dw_5_relu (ReLU)        [null,null,null,256]      0

_________________________________________________________________

conv_pw_5 (Conv2D)           [null,null,null,256]      65536

_________________________________________________________________

conv_pw_5_bn (BatchNormaliza [null,null,null,256]      1024

_________________________________________________________________

conv_pw_5_relu (ReLU)        [null,null,null,256]      0

_________________________________________________________________

conv_pad_6 (ZeroPadding2D)   [null,null,null,256]      0

_________________________________________________________________

conv_dw_6 (DepthwiseConv2D)  [null,null,null,256]      2304

_________________________________________________________________

conv_dw_6_bn (BatchNormaliza [null,null,null,256]      1024

_________________________________________________________________

conv_dw_6_relu (ReLU)        [null,null,null,256]      0

_________________________________________________________________

conv_pw_6 (Conv2D)           [null,null,null,512]      131072

_________________________________________________________________

conv_pw_6_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_pw_6_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_dw_7 (DepthwiseConv2D)  [null,null,null,512]      4608

_________________________________________________________________

conv_dw_7_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_dw_7_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_pw_7 (Conv2D)           [null,null,null,512]      262144

_________________________________________________________________

conv_pw_7_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_pw_7_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_dw_8 (DepthwiseConv2D)  [null,null,null,512]      4608

_________________________________________________________________

conv_dw_8_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_dw_8_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_pw_8 (Conv2D)           [null,null,null,512]      262144

_________________________________________________________________

conv_pw_8_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_pw_8_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_dw_9 (DepthwiseConv2D)  [null,null,null,512]      4608

_________________________________________________________________

conv_dw_9_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_dw_9_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_pw_9 (Conv2D)           [null,null,null,512]      262144

_________________________________________________________________

conv_pw_9_bn (BatchNormaliza [null,null,null,512]      2048

_________________________________________________________________

conv_pw_9_relu (ReLU)        [null,null,null,512]      0

_________________________________________________________________

conv_dw_10 (DepthwiseConv2D) [null,null,null,512]      4608

_________________________________________________________________

conv_dw_10_bn (BatchNormaliz [null,null,null,512]      2048

_________________________________________________________________

conv_dw_10_relu (ReLU)       [null,null,null,512]      0

_________________________________________________________________

conv_pw_10 (Conv2D)          [null,null,null,512]      262144

_________________________________________________________________

conv_pw_10_bn (BatchNormaliz [null,null,null,512]      2048

_________________________________________________________________

conv_pw_10_relu (ReLU)       [null,null,null,512]      0

_________________________________________________________________

conv_dw_11 (DepthwiseConv2D) [null,null,null,512]      4608

_________________________________________________________________

conv_dw_11_bn (BatchNormaliz [null,null,null,512]      2048

_________________________________________________________________

conv_dw_11_relu (ReLU)       [null,null,null,512]      0

_________________________________________________________________

conv_pw_11 (Conv2D)          [null,null,null,512]      262144

_________________________________________________________________

conv_pw_11_bn (BatchNormaliz [null,null,null,512]      2048

_________________________________________________________________

conv_pw_11_relu (ReLU)       [null,null,null,512]      0

_________________________________________________________________

conv_pad_12 (ZeroPadding2D)  [null,null,null,512]      0

_________________________________________________________________

conv_dw_12 (DepthwiseConv2D) [null,null,null,512]      4608

_________________________________________________________________

conv_dw_12_bn (BatchNormaliz [null,null,null,512]      2048

_________________________________________________________________

conv_dw_12_relu (ReLU)       [null,null,null,512]      0

_________________________________________________________________

conv_pw_12 (Conv2D)          [null,null,null,1024]     524288

_________________________________________________________________

conv_pw_12_bn (BatchNormaliz [null,null,null,1024]     4096

_________________________________________________________________

conv_pw_12_relu (ReLU)       [null,null,null,1024]     0

_________________________________________________________________

conv_dw_13 (DepthwiseConv2D) [null,null,null,1024]     9216

_________________________________________________________________

conv_dw_13_bn (BatchNormaliz [null,null,null,1024]     4096

_________________________________________________________________

conv_dw_13_relu (ReLU)       [null,null,null,1024]     0

_________________________________________________________________

conv_pw_13 (Conv2D)          [null,null,null,1024]     1048576

_________________________________________________________________

conv_pw_13_bn (BatchNormaliz [null,null,null,1024]     4096

_________________________________________________________________

conv_pw_13_relu (ReLU)       [null,null,null,1024]     0

=================================================================

Total params: 3228864

Trainable params: 3206976

Non-trainable params: 21888

拿到的模型就只有86层

const model = tf.sequential()

    for(let i = 0; i < 86; i++) {

        const layer = mobilenet.layers[i]

        layer.trainable = false

        model.add(layer)

    }

    // 连接自己的双层神经网络

    model.add(tf.layers.flatten())

调用model.add(tf.layers.flatten()) 出现下面的报错

Error: The shape of the input to "Flatten" is not fully defined (got ,,1024). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.