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How to use model architecture of pretrained models but no weights

I want to use ResNet model architecture and want to change last few layers; how can I only use model architecture from model zoo in Tensorflow?

To use a ResNet model, you can choose a select few from tensorflow.keras.applications including ResNet50, ResNet101, and ResNet152. Then, you will need to change a few of the default arguments if you want to do transfer learning. For your question, you will need to set the weights parameter equal to None. Otherwise, 'imagenet' weights are provided. Also, you need to set include_top to be False since the number of classes for your problem will likely be different from ImageNet. Finally, you will need to provide the shape of your data in input_shape. This would look something like this.
base = tf.keras.applications.ResNet50(include_top=False, weights=None, input_shape=shape)
To get a summary of the model, you can do
base.summary()
To add your own head, you can use the Functional API. You will need to add an Input layer and your own Dense layer which will correspond to your task. This could be
input = tf.keras.layers.Input(shape=shape)
base = base(input)
out = tf.keras.layers.Dense(num_classes, activation='softmax')(base)
Finally, to construct a model, you can do
model = tf.keras.models.Model(input, out)
The Model constructor takes 2 arguments. The first being the inputs to your model, and the second being the outputs. Note that calling model.summary() will show the ResNet base as a separate layer. To view all layers of the ResNet base, you can do model.layers[1].summary(), or you can modify the code on how you built your model. The second way would be
out = tf.keras.layers.Dense(num_classes, activation='softmax')(base.output)
model = tf.keras.models.Model(base.input, out)
Now you can view all layers with just model.summary().

Modify trained model architecture and continue training Keras

I want to train a model in a sequential manner. That is I want to train the model initially with a simple architecture and once it is trained, I want to add a couple of layers and continue training. Is it possible to do this in Keras? If so, how?
I tried to modify the model architecture. But until I compile, the changes are not effective. Once I compile, all the weights are re-initialized and I lose all the trained information.
All the questions in web and SO I found are either about loading a pre-trained model and continuing training or modifying the architecture of pre-trained model and then only test it. I didn't find anything related to my question. Any pointers are also highly appreciated.
PS: I'm using Keras in tensorflow 2.0 package.

Without knowing the details of your model, the following snippet might help:
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Input
# Train your initial model
def get_initial_model():
...
return model
model = get_initial_model()
model.fit(...)
model.save_weights('initial_model_weights.h5')
# Use Model API to create another model, built on your initial model
initial_model = get_initial_model()
initial_model.load_weights('initial_model_weights.h5')
nn_input = Input(...)
x = initial_model(nn_input)
x = Dense(...)(x) # This is the additional layer, connected to your initial model
nn_output = Dense(...)(x)
# Combine your model
full_model = Model(inputs=nn_input, outputs=nn_output)
# Compile and train as usual
full_model.compile(...)
full_model.fit(...)
Basically, you train your initial model, save it. And reload it again, and wrap it together with your additional layers using the Model API. If you are not familiar with Model API, you can check out the Keras documentation here (afaik the API remains the same for Tensorflow.Keras 2.0).
Note that you need to check if your initial model's final layer's output shape is compatible with the additional layers (e.g. you might want to remove the final Dense layer from your initial model if you are just doing feature extraction).

Which layer of VGG19 should I use to extract feature

Now, I want feature of image to compute their similarity. We can get feature using pre-trained VGG19 model in tensorflow easily. But VGG19 model has many layers, and I don't know which layer should I use to get feature. Which layer's output is appropriate for this problem?
# I think this how is correct to extract feature
model = tf.keras.application.VGG19(include_top=True,
weight='imagenet')
input = model.input
output = model.layers[-2].output
extract_model = tf.keras.Model(input, output)
It's my infer that the more closer to last output, the more the model output powerful feature. But some tutorials says 'use include_top=False to extract feature' (e.g Image Captioning with Attention TensorFlow)
So, I don't know which layer should I use. Please try to help me here in this thread.

The include_top=False may be used because the last 3 layers (for that specific model) are fully connected layers which are not typically good feature vectors. If the model directly outputs a feature vector, then you don't need it.
Most people use the last layer for transfer learning, but it may depend on your application. For example, Gatys et. al. show that the first few layers of VGG are sensitive to the style of the image and later layers are sensitive to the content.
I would probably try all of them in a hyperparameter search and see which gives the best performance. If by image similarity you mean the similarity of objects contained inside, I would probably start with the last layer.

How to pass output of one graph as input to another graph in tensorflow

I want to do transfer learning with INCEPTION_V4 model as feature extractor
and downloaded the code and checkpoint file from
GitHub repository of Tensorflow
Then I added my own layer for classification in 5 classes. But during model restore using tf.train.Saver, it showing error that it can not find variable values for my layer that I added.
To solve this, I created two separate graphs, one for loading pre-trained model and one for my classification layers. But I can't pass output of one graph as input to the second graph.
Can you suggest any other way to do transfer learning or solve the problem

The easy solution is to just construct the inception model (without your layers), then create the saver and use it for restore, and only then create your layers.
There are more complex solutions (you can pass the var_list parameter of tf.Saver with the list of all variables you want to restore, and initialize that to be the list of all the inception variables), but this one should be straightforward, and I do it with my transfer learning models.

Insert and train an additional layer between trained lstm layers and output layers

Here is what I wanna do:
I want to use some transfer learning techniques to deal with sequence problems:
First use dataset_1 to train a lstm model,
Second insert another lstm layer before the output layer,
and then use dataset_2 to only train the newly adding layer, and the variables of other layers are imported from the first training stage and remain unchanged
here's the problem, the existing methods all require the variable name of weight/bias when restoring the pre-trained model. And I want to use the fuction tf.contrib.rnn.MultiRNNCell(*) in my code.However, the function is a black box and unable to obtain concrete variable names. How can I realize the idea?