Unable to load the frozen model (.pb) in GraphDef in tensorflow version 2.x

Question

1. Created a simple dummy sequential model in tf.keras as shown below:

   model = tf.keras.Sequential()
   model.add(layers.Dense(10, input_shape=(100, 100)))
   model.add(layers.Conv1D(3, 2))
   model.add(layers.Flatten())
   model.add(layers.Dense(10, activation='softmax', name='predict_10'))
   

2. Trained the model and saved it using tf.keras.models.saved_model.

3. To get the input input and output node names used saved_model_cli.

   saved_model_cli show --dir "path/to/SavedModel" --all
   

   

4. Froze the saved model with freeze_graph.py utility.

   python freeze_graph.py --input_saved_model_dir=<path/to/SavedModel> --output_graph=<path/freeze.pb> --input_binary=True --output_node_names=StatefulPartitionedCall
   

   Model is frozen.

Now Here's the main issue:

5. To load the frozen graph I've used this guide Migrate tf1.x to tf2.x (wrap_frozen_graph)
6. Used

   with tf.io.gfile.GFile("patf/to/freeze.pb", 'rb') as f:
      graph_def = tf.compat.v1.GraphDef()
      graph_def.ParseFromString(f.read())
   load_frozen = wrap_frozen_graph(graph_def, inputs='dense_3_input:0', outputs='predict_10:0')
   

7. Output error ValueError: Input 1 of node StatefulPartitionedCall was passed float from dense_3/kernel:0 incompatible with expected resource.

I'm getting same error when converting .pb to .dlc (Qualcomm). Actually I want to run original model on Qualcomm's Hexagon DSP or GPU.