Cannot load input model

[Lam__Carson]

python3 ~/intel/computer_vision_sdk_2018.5.445/deployment_tools/model_optimizer/mo_tf.py --input_model /media/carson/New\ Volume/vision/keras/saved_models/NasMobi.pb --input_checkpoint /media/carson/New\ Volume/vision/keras/saved_models/NasMobi.ckpt --scale_values [255,255,255] --input_shape [1,3,224,224] --output_dir /media/carson/New\ Volume/vision/NCS/bin_xml/FP16 --data_type FP16 --model_name NasMobi
Model Optimizer arguments:
Common parameters:
    - Path to the Input Model:     /media/carson/New Volume/vision/keras/saved_models/NasMobi.pb
    - Path for generated IR:     /media/carson/New Volume/vision/NCS/bin_xml/FP16
    - IR output name:     NasMobi
    - Log level:     ERROR
    - Batch:     Not specified, inherited from the model
    - Input layers:     Not specified, inherited from the model
    - Output layers:     Not specified, inherited from the model
    - Input shapes:     [1,3,224,224]
    - Mean values:     Not specified
    - Scale values:     [255,255,255]
    - Scale factor:     Not specified
    - Precision of IR:     FP16
    - Enable fusing:     True
    - Enable grouped convolutions fusing:     True
    - Move mean values to preprocess section:     False
    - Reverse input channels:     False
TensorFlow specific parameters:
    - Input model in text protobuf format:     False
    - Offload unsupported operations:     False
    - Path to model dump for TensorBoard:     None
    - List of shared libraries with TensorFlow custom layers implementation:     None
    - Update the configuration file with input/output node names:     None
    - Use configuration file used to generate the model with Object Detection API:     None
    - Operations to offload:     None
    - Patterns to offload:     None
    - Use the config file:     None
Model Optimizer version:     1.5.12.49d067a0
[ FRAMEWORK ERROR ]  Cannot load input model: Input 0 of node replica_0/model/stem_bn1/cond/ReadVariableOp/Switch was passed float from stem_bn1/gamma:0 incompatible with expected resource.

python3 ~/intel/computer_vision_sdk_2018.5.445/deployment_tools/model_optimizer/mo_tf.py --input_model /media/carson/New\ Volume/vision/keras/saved_models/test.pb --input_checkpoint /media/carson/New\ Volume/vision/keras/saved_models/test.ckpt --scale_values [255,255,255] --input_shape [1,3,256,256] --output_dir /media/carson/New\ Volume/vision/NCS/bin_xml/FP16 --data_type FP16 --model_name test
Model Optimizer arguments:
Common parameters:
    - Path to the Input Model:     /media/carson/New Volume/vision/keras/saved_models/test.pb
    - Path for generated IR:     /media/carson/New Volume/vision/NCS/bin_xml/FP16
    - IR output name:     test
    - Log level:     ERROR
    - Batch:     Not specified, inherited from the model
    - Input layers:     Not specified, inherited from the model
    - Output layers:     Not specified, inherited from the model
    - Input shapes:     [1,3,256,256]
    - Mean values:     Not specified
    - Scale values:     [255,255,255]
    - Scale factor:     Not specified
    - Precision of IR:     FP16
    - Enable fusing:     True
    - Enable grouped convolutions fusing:     True
    - Move mean values to preprocess section:     False
    - Reverse input channels:     False
TensorFlow specific parameters:
    - Input model in text protobuf format:     False
    - Offload unsupported operations:     False
    - Path to model dump for TensorBoard:     None
    - List of shared libraries with TensorFlow custom layers implementation:     None
    - Update the configuration file with input/output node names:     None
    - Use configuration file used to generate the model with Object Detection API:     None
    - Operations to offload:     None
    - Patterns to offload:     None
    - Use the config file:     None
Model Optimizer version:     1.5.12.49d067a0
[ FRAMEWORK ERROR ]  Cannot load input model: Input 0 of node batch_normalization_188/cond/ReadVariableOp/Switch was passed float from batch_normalization_188/beta:0 incompatible with expected resource.
 

[Hyodo__Katsuya]

@Lam, Carson Will not it work with the following specifications? --input_shape=[1,224,224,3]