TF Slim: Fine Tune mobilenet v2 on custom dataset

I am trying to fine-tune Mobilenet_v2_1.4_224 model on my custom dataset for Image Classification task. I am following this tutorial TensorFlow-Slim image classification library. I have already created the .tfrecord train and validation files. When I try to fine tune from an existing checkpoint I get the following error:

InvalidArgumentError (see above for traceback): Assign requires shapes of both tensors to match. lhs shape= [1,1,24,144] rhs shape= [1,1,32,192] [[Node: save/Assign_149 = Assign[T=DT_FLOAT, _class=["loc:@MobilenetV2/expanded_conv_2/expand/weights"], use_locking=true, validate_shape=true, _device="/job:localhost/replica:0/task:0/device:CPU:0"](MobilenetV2/expanded_conv_2/expand/weights, save/RestoreV2:149)]]

The fine-tuning script that I have used is:

DATASET_DIR=G:\Dataset

TRAIN_DIR=G:\Dataset\emotion-models\mobilenet_v2

CHECKPOINT_PATH=C:\Users\lenovo\Desktop\mobilenet_v2\mobilenet_v2_1.4_224.ckpt

python train_image_classifier.py \
--train_dir=${TRAIN_DIR} \
--dataset_dir=${DATASET_DIR} \
--dataset_name=emotion \
--dataset_split_name=train \
--model_name=mobilenet_v2 \
--train_image_size=224 \
--clone_on_cpu=True \
--checkpoint_path=${CHECKPOINT_PATH} \
--checkpoint_exclude_scopes=MobilenetV2/Logits \
--trainable_scopes=MobilenetV2/Logits

I suspect that the error is due to the last 2 arguments "checkpoint_exclude_scopes" or "trainable_scopes".

I know that these 2 arguments are being used for transfer learning by removing the last 2 layers and creating our own softmax layer for custom dataset classficiation. But I'm not sure if I'm passing the right values for them.

Solution

To retrain the model, you must fine tune for your custom number of classes

MobilenetV2/Predictions and MobilenetV2/predics

--checkpoint_exclude_scopes=MobilenetV2/Logits,MobilenetV2/Predictions,MobilenetV2/predics \
--trainable_scopes=MobilenetV2/Logits,MobilenetV2/Predictions,MobilenetV2/predics \

In mobilenet_v2.py, depth_multiplier=1 for both mobilenet and mobilenet_base, you should change that to 1.4

@slim.add_arg_scope 
def mobilenet_base(input_tensor, depth_multiplier=1.4, **kwargs): 
"""Creates base of the mobilenet (no pooling and no logits) .""" 
return mobilenet(input_tensor,
                 depth_multiplier=depth_multiplier,
                 base_only=True, **kwargs)

@slim.add_arg_scope 
def mobilenet(input_tensor,
                  num_classes=1001,
                  depth_multiplier=1.4,
                  scope='MobilenetV2',
                  conv_defs=None,
                  finegrain_classification_mode=False,
                  min_depth=None,
                  divisible_by=None,
                  **kwargs):