I have a Torch Model which is trained on a large scale dataset (Places Dataset) and it's authors uploaded it on github, i am working on a similar project and i want to make use of it and use it's trained weights instead of use the large dataset to train it and save time and efforts, it is possible ? how can i know the only the trained filters weights? i don't want to copy the code, i only want to make use of it and save time and efforts.
NOTE: I use Tensoflow in my implementation.
Related
I would like to train a MLP(Multi Layer Perceptron) with MNIST dataset. I use a validation set so I can save the weights of the best model. Then I want to load these weights back into the same architecture and use them to initialize and train with another dataset. I would like to know if this is possible with Tensorflow 1.x or 2.x. Right now I am trying to write a custom function to do it but it is getting complicated. I am using tf 1.x.
I suggest you take a look at tensorflow's documentation, here a link of a tutorial to save your weights and load them afterwards:
https://www.tensorflow.org/tutorials/keras/save_and_load
I am using TF-Agents for a custom reinforcement learning problem, where I train a DQN (constructed using DqnAgents from the TF-Agents framework) on some features from my custom environment, and separately use a keras convolutional model to extract these features from images. Now I want to combine these two models into a single model and use transfer learning, where I want to initialize the weights of the first part of the network (images-to-features) as well as the second part which would have been the DQN layers in the previous case.
I am trying to build this combined model using keras.layers and compiling it with the Tf-Agents tf.networks.sequential class to bring it to the necessary form required when passing it to the DqnAgent() class. (Let's call this statement (a)).
I am able to initialize the image feature extractor network's layers with the weights since I saved it as a .h5 file and am able to obtain numpy arrays of the same. So I am able to do the transfer learning for this part.
The problem is with the DQN layers, where I saved the policy from the previous example using the prescribed Tensorflow Saved Model Format (pb) which gives me a folder containing model attributes. However, I am unable to view/extract the weights of my DQN in this way, and the recommended tf.saved_model.load('policy_directory') is not really transparent with respect to what data I can see regarding the policy. If I have to follow the transfer learning as I do in statement (a), I need to extract the weights of my DQN and assign them to the new network. The documentation seems to be quite sparse for this case where transfer learning needs to be applied.
Can anyone help me in this, by explaining how I can extract weights from the Saved Model method (from the pb file)? Or is there a better way to go about this problem?
I have trained an image classification model using pytorch.
Now, I want to move it from research to production pipeline.
I am thinking of using TensorFlow extended. I have a very noob doubt that will I'll be able to use my PyTorch trained model in the TensorFlow extended pipeline(I can convert the trained model to ONNX and then to Tensorflow compatible format).
I don't want to rewrite and retrain the training part to TensorFlow as it'll be a great overhead.
Is it possible or Is there any better way to productionize the PyTorch trained models?
You should be able to convert your PyTorch image classification model to Tensorflow format using ONNX, as long as you are using standard layers. I would recommend doing the conversion and then look at both model summaries to make sure they are relatively similar. Also, do some tests to make sure your converted model handles any particular edge cases you have. Once you have confirmed that the converted model works, save your model as a TF SavedModel format and then you should be able to use it in Tensorflow Extended (TFX).
For more info on the conversion process, see this tutorial: https://learnopencv.com/pytorch-to-tensorflow-model-conversion/
You could considering using the torchX library. I haven't use it yet, but it seems to make it easier to deploy models by creating and running model pipelines. I don't think it has the same data validation functionality that Tensorflow Extended has, but maybe that will be added in the future.
Are there ways to save a model after training and sharing just the model with others? Like a regular script? Since the network is a collection of float matrices, is it possible to just extract these trained weights and run it on new data to make predictions, instead of requiring the users to install these frameworks too? I am new to these frameworks and will make any clarifications as needed.
PyTorch: As explained in this post, you can save a model's parameters as a dictionary, or load a dictionary to set your model's parameters.
You can also save/load a PyTorch model as an object.
Both procedures require the user to have at least one tensor computation framework installed, e.g. for efficient matrix multiplication.
I've trained a seq2seq model for machine translation (DE-EN). And I have saved the trained model checkpoint. Now, I'd like to fine-tune this model checkpoint to some specific domain data samples which have not been seen in previous training phase. Is there a way to achieve this in tensorflow? Like modifying the embedding matrix somehow.
I couldn't find any relevant papers or works addressing this issue.
Also, I'm aware of the fact that the vocabulary files needs to be updated according to new sentence pairs. But, then do we have to again start training from scratch? Isn't there an easy way to dynamically update the vocabulary files and embedding matrix according to the new samples and continue training from the latest checkpoint?