When I quantized the model by lite modules in tensorflow, I cann't check the weights values that had quantized.Is there any way to view these values in the .tflite files? or Is there any way to parse the .tflite files?
There are some neural network visualizers that can also provide an interface to inspect the file. I have been using Netron. You can click on the "weights" tab of the layer you are interested in to view the data. I haven't tried it yet, but there appears to be a floppy-disk save icon when you view weights/biases in the right side-bar.
The data format is a Google FlatBuffer, defined by the schema file here. You may prefer doing this if you want to do something with the data like output it in a different format. I found the output from parsing it myself using the schema.fbs file to match Netron's for the CNN's I passed in. You can check out the FlatBuffers documentation here.
here in first answer is guide, how to create json view of .tflite model. There you can see quantized values
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Does a tflite file contain data about the model architecture? A graph that shows what operations there where between the weights and features and biases, what kind of layers (linear or convolutional etc), size of layers, and what activation functions are there in-between the layers?
For example a graph you get with graphviz, that contains all the information, or does a tflite file only contain the final weights of the model after training?
I am working on a project with image style transfer. I wanted to do some research on an existing project, and see what parameters work better. The project I am looking at is here:
https://tfhub.dev/sayakpaul/lite-model/arbitrary-image-stylization-inceptionv3-dynamic-shapes/int8/transfer/1
I can download a tflite file, but I don't know much about these files. If they have the architecture I need, how do I read it?
TFLite flatbuffer files contain the model structure as well. For example, there are a subgraph concept in TFLite, which corresponds to the function concept in the programming language and the operator nodes also represent a graph node, which takes inputs and generates outputs. By using the Netron application, the model architecture can be visualized.
What are the step-by-step instructions for using a TFlite file and embedding it in an actual Android application? For reference, this is regression. Input will be an image, output should be a number. I have already looked at the TensorFlow documentation but they do not explain how to do it from scratch.
The following steps are required to use TFLite in Android:
include the dependency 'org.tensorflow:tensorflow-lite:+' to your build.gradle
Make sure files of type .tflite will not be compressed using the aaptOptions in your build.gradle
Make the model .tflite file available by putting it into your apps assets folder (To create one, right click res folder, click res > New > Folder > Assets Folder)
In your java class that will handle the inference, import the tflite interpreter
Load the model file as a MappedByteBuffer in your java class
Load the MappedByteBuffer into your TFLite interpreter
Convert input image to float ByteBuffer
Define output array matching the size of your output layer
Use the loaded TFLite interpreter to forward pass the input ByteBuffer through your model and write the prediction to your output array.
Let me know if something is unclear!
For Android applications, here is a quick example of using TF Lite for classification. You may be able to follow a similar structure as here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/examples/android/app/src/main/java/org/tensorflow/demo/TFLiteImageClassifier.java
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 am very new to CNTK.
I wanted to train a set of images (to detect objects like alcohol glasses/bottles) using CNTK - ResNet/Fast-R CNN.
I am trying to follow below documentation from GitHub; However, it does not appear to be a straight forward procedure. https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN
I cannot find proper documentation to generate ROI's for the images with different sizes and shapes. And how to create object labels based on the trained models? Can someone point out to a proper documentation or training link using which I can work on the cntk model? Please see the attached image in which I was able to load a sample image with default ROI's in the script. How do I properly set the size and label the object in the image ? Thanks in advance!
sample image loaded for training
Not sure what you mean by proper documentation. This is an implementation of the paper (https://arxiv.org/pdf/1504.08083.pdf). Looks like you are trying to generate ROI's. Can you look through the helper functions as documented at the site to parse what you might need:
To run the toy example, make sure that in PARAMETERS.py the datasetName is set to "grocery".
Run A1_GenerateInputROIs.py to generate the input ROIs for training and testing.
Run A2_RunCntk_py3.py to train a Fast R-CNN model using the CNTK Python API and compute test results.
The algo will work on several candidate regions and then generate outputs: one for the classes of objects and another one that generates the bounding boxes for the objects belonging to those classes. Please refer to the code for getting the details of the implementation.
Can someone point out to a proper documentation or training link using which I can work on the cntk model?
You can take a look at my repository on GitHub.
It will guide you through all the steps required to train your own model for object detection and classification with CNTK.
But in short the proper steps should look something like this:
Setup environment
Prepare data
Tag images (ground truth)
Download pretrained model and create mappings for your custom dataset
Run training
Evaluate the model on test set
The Android example that comes with Tensorflow downloads a protobuf file for InceptionV3 which contains both the graph and the values from the model. In the docs, I could only find how to serialize the graph (tf.Graph.as_graph_def) or save the variable values with a tf.train.Saver. How can you save everything to a single file, as done for that example?
I answered a similar question on this topic: Is there an example on how to generate protobuf files holding trained Tensorflow graphs?
The basic idea is to use tf.import_graph_def() to replace the variables in the original (training) graph with constants, and then write out the resulting GraphDef using tf.Graph.as_graph_def().