Tensorflow (.pb) format to Keras (.h5)

Question

I am trying to convert my model in Tensorflow (.pb) format to Keras (.h5) format to view post hoc attention visualisation. I have tried below code.

file_pb = "/test.pb"
file_h5 = "/test.h5"
loaded_model = tf.keras.models.load_model(file_pb)
tf.keras.models.save_keras_model(loaded_model, file_h5)
loaded_model_from_h5 = tf.keras.models.load_model(file_h5)

Can anyone help me with this? Is this even possible?

Answer 1

In the Latest Tensorflow Version (2.2), when we Save the Model using tf.keras.models.save_model, the Model will be Saved in not just a pb file but it will be Saved in a Folder, which comprises Variables Folder and Assets Folder, in addition to the saved_model.pb file, as shown in the screenshot below:

For example, if the Model is Saved with the Name, "Model", we have to Load using the Name of the Folder, "Model", instead of saved_model.pb, as shown below:

loaded_model = tf.keras.models.load_model('Model')

instead of

loaded_model = tf.keras.models.load_model('saved_model.pb')

One more change you can do is to replace

tf.keras.models.save_keras_model

with

tf.keras.models.save_model

Complete working Code to convert a Model from Tensorflow Saved Model Format (pb) to Keras Saved Model Format (h5) is shown below:

import os
import tensorflow as tf
from tensorflow.keras.preprocessing import image

New_Model = tf.keras.models.load_model('Dogs_Vs_Cats_Model') # Loading the Tensorflow Saved Model (PB)
print(New_Model.summary())

Output of the New_Model.summary command is:

Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 513       
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________
None

Continuing the code:

# Saving the Model in H5 Format and Loading it (to check if it is same as PB Format)
tf.keras.models.save_model(New_Model, 'New_Model.h5') # Saving the Model in H5 Format

loaded_model_from_h5 = tf.keras.models.load_model('New_Model.h5') # Loading the H5 Saved Model
print(loaded_model_from_h5.summary())

Output of the command, print(loaded_model_from_h5.summary()) is shown below:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 148, 148, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 74, 74, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 72, 72, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         
_________________________________________________________________
flatten (Flatten)            (None, 6272)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               3211776   
_________________________________________________________________
dense_1 (Dense)              (None, 1)                 513       
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________

​ As can be seen from the Summary of both the Models above, both the Models are same.