I have a Tensorflow Lite model (.tflite file), which is already trained.
I need to use it in an API python view that receives recorded .wav files for speech recognition, and returns the equivalent text to the recorded file that was sent.
Any advices ou tutos on how I could use the trained model in order to treat the recorded instructions?
Thanks.
Refer to the TFLite Inference Guide for more details.
Specifically, for python refer to this
Related
So I have trained a posenet model for classifying different poses on the teachable machine learning website (link for the website). I want to use this trained model in a flutter app, and for that I need to convert the model into tflite format. I checked many online blogs which said that the website has an option of converting it to that format, but they have removed that feature. Thus, I wanted to know how can I convert this teachable machine model into tflite format?
I downloaded a pose model of my own from that site, and the zip appears to be a Tensorflow.JS model.
Now that we know the unzipped file is just a TF.js model, refer to a tutorial like this to convert the TFJS model back into a keras SavedModel, which can be then saved into a tflite model.
There are many ways to save a model and its weights. It is confusing when there are so many ways and not any source where we can read and compare their properties.
Some of the formats I know are:
1. YAML File - Structure only
2. JSON File - Structure only
3. H5 Complete Model - Keras
4. H5 Weights only - Keras
5. ProtoBuf - Deployment using TensorFlow serving
6. Pickle - Scikit-learn
7. Joblib - Scikit-learn - replacement for Pickle, for objects containing large data.
Discussion:
Unlike scikit-learn, Keras does not recommend you save models using pickle. Instead, models are saved as an HDF5 file. The HDF5 file contains everything you need to not only load the model to make predictions (i.e., architecture and trained parameters) but also to restart training (i.e., loss and optimizer settings and the current state).
What are other formats to save the model for Scikit-learn, Keras, Tensorflow, and Mxnet? Also what info I am missing about each of the above-discussed formats?
There are also formats like onnx which basically supports most of the frameworks and helps in removing the confusion of using different formats for different frameworks.
There exists also TFJS format, which enables you to use the model on web or node.js environments. Additionally, you will need TF Lite format to make inference on mobile and edge devices. Most recently, TF Lite for Microcontrollers exports the model as a byte array in C header file.
Your question on formats for saving a model has multiple possible answers, based on why you want to save your model:
Save your model to resume training it later
Save your model to load it for inference later
These scenarios give you a couple of options:
You could save your model using the library-specific saving functions; if you want to resume training, make sure that you have saved all the information you need to really be able to resume training. Formats here will vary by library, and indeed are not aimed at being formats that you would inspect or read in any way - they are just files. If you are looking for a library that wraps all of these save functions behind a common API, you should check out the modelstore Python library.
You can also want to use a common format like ONNX; there are converters from Keras to ONNX and scikit-learn to ONNX available; but it is uncommon to use this format to later resume training. The benefit here is that they are all saved to a common format, which may streamline the process of loading them later.
I built a CNN model for image classification using the Keras library. However training takes many hours. Once I trained my model, how can I use it without training once more? I mean after I trained my model, I want to use it many times.
Because I will use my model in android studio.
Any help is appreciated
Thank YOU...
EDIT
When I wrote this question, I did not know the save model and load.model, in the answers you see the appropriate usage of them.
You can easily save your model after the training process by using:
model.save('my_model.h5')
you can later load that model by using:
model = load_model('my_model.h5')
for more details have a look at the documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
I have downloaded a pre-trained model on ImageNet of Inception v3 from http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz (found this link while following one of the tutorials on codelabs).
This zip file contains .pb file that I can directly import as GraphDef in TensorFlow.
I would like to know if there are similar .pb files for other architectures as well, such as ResNet, VGG16, MobileNet, etc. If yes, could you provide the link of those?
Thanks in advance.
Kind Regards,
Ajay
You can find many pretrained models here: https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
And the corresponding codes are here:
https://github.com/tensorflow/models/tree/master/research/slim/nets
Here are addition sources to download pretrained models:
TensorFlow Hub: https://tfhub.dev/
ModelZoo: https://modelzoo.co/
Though slightly different, Keras provides an interface to access pretrained models:
https://keras.io/api/applications/
I'm looking to run a basic fully-connected neural network for the MNIST dataset with the C++ API v1.2 from Tensorflow. I have trained the model and exported it using tf.train.Saver() in Python. This gave me a checkpoint file, a data file, an index file and a meta file.
I know that the data file contains the saved variables while the meta file contains the graph from using Tensorboard on a previous project.
However, I am not sure what is the recommended way to load those files
and run the trained model in a C++ environment in v1.2, since all the
tutorials and questions I've found are for older versions which differ
substantially.
I've found that tensorflow::ops::Restore should be the method to do such a thing, but I know that inference in Tensorflow isn't well supported, as such I am not certain what parameters should I give it in order to receive the trained model that I can just put into a session->Run() and receive an accuracy statement when fed test data.