I run into this issue when I was interested in freezing graphs for using them in mobile devices. Freezing a Graph means combining the structure of a model with its weights, so first we need to save those two parts to later combine them.
Saving the structure
g = sess.graph
# In my case, I use the default Graph
gdef = g.as_graph_def()
# Parameters: 1) graph, 2) directory where we want to save the pb file,
# 3) name of the file, 4) text format (True) or binary format.
tf.train.write_graph(gdef,".","graph.pb",False)
Note: if we save our graph as text, setting the boolean value to True, we can have a look at it with any text editor and see how it looks like. Since it uses Protocol Buffers it is easily readable.
Saving the weights
We also call this the checkpoints. We have to get a Saver object and use it after the network is trained so it will contained the weights obtained after the optimization.
saver = tf.train.Saver()
# ....
# After the model is trained
saver.save(sess, 'tmp/my-weights')
Freezing the Graph
Now it’s time to combine both files. We can see the commands in the original tutorial in github.
Since I didn’t want to mess up with my current tensorflow library, I downloaded tensorflow again in a separate folder
After I installed Bazel (following their website instructions) I tried to build the freeze_graph (make sure you are in the right path. If you download again tensorflow from github note that it has a “WORKSPACE” file. You should be there to run Bazel) by doing:
It takes a while to build it. In MacOS Sierra I didn’t have any problem, but in Ubuntu 16.04 I did, and after searching I found on github how to solve it.
After this, in the same folder, we just need to execute the command provided in the tutorial:
--input_graph=/tmp/mobilenet_v1_224.pb \
--input_checkpoint=/tmp/checkpoints/mobilenet-10202.ckpt \
--input_binary=true --output_graph=/tmp/frozen_mobilenet_v1_224.pb \
--output_node_names=MobileNet/Predictions/Reshape_1
input_graph: location of the structure of the graph (first part of the tutorial, pb file)
input_checkpoint: weights stored using the Saver (second part of the tutorial)
input_binary=true: remember to save the graph in binary format. They recommend that this value has to be true, so do not use text format generating the pb file.
output_graph: location of the new frozen graph
output_node_names: name of the output node. You can check this using Tensorboard, but assuming you are naming all the tensors, this should be easy to figure out. You can also check the name of all the operations, or check the pb file (text mode), but probably the most intuitive way is using Tensorboard.
After executing this, we will have our frozen graph.
Bonus: How to use the frozen Graph in Python
Here I found a very easy to follow code that explains itself how to read a frozen Graph to use it.
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we import the graph_def into a new Graph and returns it
with tf.Graph().as_default() as graph:
# The name var will prefix every op/nodes in your graph
# Since we load everything in a new graph, this is not needed
tf.import_graph_def(graph_def, name="prefix")
return graph
