I need the graph file(.pb file) of pretrained openimages dataset. After downloading the model there are only 3 files
labelmap.txt
model.ckpt
model.ckpt.meta
Where can I find the .pb file?
The model.ckpt.meta contains both the graph and the weights. you have to import it using import_metagraph functionality.
Then you should be able to export the graph defn in pb format, if you really need it.
However I was unable to load the ckpt.meta, due to a google-specific op defined in the model. Please let me know if you can figure that out.
Related
Is this SavedModel just for Tensorflow front-end applications or it can be use to reload model in keras format. I created it using tf.saved_model.save and now I don't know what to make of it.
Following the guide above I was able to load a SavedModel directory, and it seemingly no use, not trainable nor use to predict input like model.predict, and that the only thing I have since I lost the h5 file in my files **cough trashbin **cough.
Note: I noticed this guide tell me to use tf.keras.models.load_model('inceptionv3')
and it return this
error
You have saved the model using tf.saved_model.save and so the correct way to load it back is tf.saved_model.load('inceptionv3'). This is also suggested in your error image.
After loading model, you can try doing prediction as follows:
model = tf.saved_model.load('inceptionv3')
out = model(inputs)
I'm kinda new to TensorFlow and Keras, so please excuse any accidental stupidity, but I have an issue. I've been trying to load in models from the TensorFlow Detection Zoo, but haven't had much success.
I can't figure out how to read these saved_model folders (they contain a saved_model.pb file, and an assets and variables folder), so that they're accepted by Keras. Nor can I figure out a way to convert these models so that they may be loaded in. I've tried converting the SavedModel to ONNX, and then convert the ONNX-model to Keras, but that didn't work. Trying to load the original model as a saved_model, and then trying to to save this loaded model in another format gave me no success either.
Since you are new to Tensorflow (and I guess deep learning) I would suggest you stick with the API because the detection zoo models best interface with the object detection API. If you have already downloaded the model, you just need to export it using the exporter_main_v2.py script. This article explains it very well link.
I have downloaded the .weights and .cfg file for YOLOv3 from darknet (link: https://pjreddie.com/darknet/yolo/) I want to create a model and assign the weights from these files, and I want to save the model with the assigned weights to a .h5 file so that I can load the .h5 model into Keras by using keras.models.load_model().
Please help.
You should check the instructions given in this repository. This is basically the keras implementation of YOLOv3 (Tensorflow backend).
Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
As you have already downloaded the weights and configuration file, you can skip the first step. Download the convert.py script from repository and simply run the above command.
Note: Above command assumes that yolov3.cfg, yolov3.weights and model_data(folder) are present at the same path as convert.py.
For people getting error from this try changing the layers part in 'convert.py'
Not sure if it was version problem but changing the way converter.py file was loading 'keras.layers' solved all errors for me
I want to convert a Tensorflow model with the following structure to a .mlmodel file for use in an iOS app:
cub_image_experiment/
logdir/
val_summaries/
test_summaries/
finetune/
val_summaries/
cmds.txt
config_train.yaml
config_test.yaml
I'm following this tutorial: https://github.com/visipedia/tf_classification/wiki/CUB-200-Image-Classification
However, I'm having trouble understanding the structure of the project. Which files are important and how do I convert all the separate config files and everything into a single .mlmodel file so that I can use in my application?
I've looked online and all I could find was how to convert .caffemodel to .mlmodel or .pb file to .mlmodel. These are all single files, however my project has multiple files. I found a tutorial on how to convert a tf model into a single .pb file, however, that model's structure was different and it did not contain any yaml files. My project is not focused on creating a model at the moment, but merely integrating a model into an iOS app. I found this model interesting for an app idea and wanted to know if it can be integrated. If there are any tutorials out there that might help me in this sort of problem please let me know.
None of that stuff is used by the Core ML model. The yaml files etc are used only to train the TF model.
All you need to provide is a frozen graph (a .pb file) and then convert it to an mlmodel using tfcoreml.
It looks like your project doesn't have a frozen graph but checkpoints. There is a TF utility that you can use to convert the checkpoint to a frozen graph, see https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py
I want to use Tensorboard with a non tensorflow app. I can see how to make the graph using GraphDef and associated classes but not sure how to write it out so that Tensorboard will read it. This means that I have the graph in a serialized form and it's not the python graph class from tensorflow.
For seeing graph in tensorboard, you need weight, tensor name and structure of the graph.
I dont understand your question totally, but if you are able to create graph.pb file then , it will be simple to see that thing in tensorboard you have to run this file here.
actually here we are creating dummy graph structure using
graph_def = graph_pb2.GraphDef()
and then giving our .pb file to set all these weight and name
graph_def.ParseFromString('pb file directory to read'.read())
import_graph_def(graph_def)
let me know more detail so that i can help you better way.