How to save a Tensorflow model ( which doesnt contain any variable ) to port it in OpenCV

Question

I wanted to know what is the correct way to save a tensorflow model that I have trained in python so that I can import it in OpenCV using the dnn module of opencv. This is my Tensorflow graph

X = tf.placeholder(tf.float32, [None,training_set.shape[1]],name = 'X')
Y = tf.placeholder(tf.float32,[None,training_labels.shape[1]], name = 'Y')

A1 = tf.contrib.layers.fully_connected(X, num_outputs = 50, activation_fn = tf.nn.relu)
A1 = tf.nn.dropout(A1, 0.8)
A2 = tf.contrib.layers.fully_connected(A1, num_outputs = 2, activation_fn = None)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = A2, labels = Y))
global_step = tf.Variable(0, trainable=False)
start_learning_rate = 0.001
learning_rate = tf.train.exponential_decay(start_learning_rate, global_step, 100, 0.1, True )
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

As you can see it doesn't contain any variables. So my question is how should this graph be saved in Tensorflow so that it can be loaded using cv::dnn::readNetFromTensorflow. Should I save the model as .pb or .pbtxt file. And will the .pb or .pbtxt contain the graph as well as the weights or just the graph ??. How to load both the graph and the weights in OpenCV ??.

Answer 1

The code that belongs to OP posted link is posted here. URL may change, code renamed or vanished. Therefore I've posted the code to where it is referred by OP.

I guess a first question is how to save the graph at least to load it in TensorFlow again? Because you need to find a way to restore it. There is some way to do it:

-- Save

# Save a graph definition (once)
tf.train.write_graph(sess.graph.as_graph_def(), "", "graph.pb")

# Weights initialization
sess.run(tf.global_variables_initializer())

# Training
...

# Save a checkpoint (weights only, no graph definition)
saver = tf.train.Saver()
saver.save(sess, 'tmp.ckpt')

-- Freeze (merge graph definition with weights, remove training-only nodes)

python ~/tensorflow/tensorflow/python/tools/freeze_graph.py \
  --input_graph=graph.pb \
  --input_checkpoint=tmp.ckpt \
  --output_graph=frozen_graph.pb \
  --output_node_names="NameOfOutputNode"

Only after these steps you might load frozen_graph.pb contains both graph definition and weights using OpenCV.