model.save in keras vgg16 just save weights without model architecture - tensorflow

Question

I created my model and saved by using model.save .
I then loaded it by using tf.keras.modles.load_model which loads the weights and architecture.
The vgg16 model just save the weights without model architecture
The message error is:
(ValueError: You are trying to load a weight file containing 15 layers into a model with 0 layers.)
Also, what is the difference between
model.save and tf.keras.saved_model.save

Answer 1

This seems to be an issue in older versions of Tensorflow or Keras. There are related issues in Github and Stackoverflow as well.
But this error is fixed in the latest stable version of Tensorflow, 2.1.
Mentioned below is the simple working code example:
from tensorflow.keras.applications import VGG16
import tensorflow as tf
model = VGG16(include_top = False, weights = 'imagenet', input_shape = (224,224,3))
model.save('model.h5')
loaded_model1 = tf.keras.models.load_model('model.h5')
model.save('model')
loaded_model2 = tf.keras.models.load_model('model')
Mentioned below is the output when executed the commands, loaded_model1 and loaded_model2.
Model: "vgg16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 150, 150, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 150, 150, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 150, 150, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 75, 75, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 75, 75, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 75, 75, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 37, 37, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 37, 37, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 18, 18, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 9, 9, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
If you are facing issues even after upgrading your Tensorflow Version to 2.1, please share the complete code as mentioned above. We can investigate it further.