I have a gradient boost model saved in the .pkl format. I have to load this model in tensorflowjs. i can see that there is a way to load a keras model but I can't find a way to load a sklearn model. Is it possible to do this?
It is not possible to load sklearn model in tensorflow.js. Tensorflow.js allows to load models written in tensorflow.
Though, I haven't tried myself, but I think that you can possibly use the scikit learn wrapper to rewrite the classifier in tensorflow. The model can be saved and converted to a format that can be loaded in tensorflow.js.
Related
I am trying to port two pre-trained keras models into the IPU machine. I managed to load and run them using IPUstrategy.scope but I dont know if i am doing it the right way. I have my pre-trained models in .h5 file format.
I load them this way:
def first_model():
model = tf.keras.models.load_model("./model1.h5")
return model
After searching your ipu.keras.models.py file I couldn't find any load methods to load my pre-trained models, and this is why i used tf.keras.models.load_model().
Then i use this code to run:
cfg=ipu.utils.create_ipu_config()
cfg=ipu.utils.auto_select_ipus(cfg, 1)
ipu.utils.configure_ipu_system(cfg)
ipu.utils.move_variable_initialization_to_cpu()
strategy = ipu.ipu_strategy.IPUStrategy()
with strategy.scope():
model = first_model()
print('compile attempt\n')
model.compile("sgd", "categorical_crossentropy", metrics=["accuracy"])
print('compilation completed\n')
print('running attempt\n')
res = model.predict(input_img)[0]
print('run completed\n')
you can see the output here:link
So i have some difficulties to understand how and if the system is working properly.
Basically the model.compile wont compile my model but when i use model.predict then the system first compiles and then is running. Why is that happening? Is there another way to run pre-trained keras models on an IPU chip?
Another question I have is if its possible to load a pre-trained keras model inside an ipu.keras.model and then use model.fit/evaluate to further train and evaluate it and then save it for future use?
One last question I have is about the compilation part of the graph. Is there a way to avoid recompilation of the graph every time i use the model.predict() in a different strategy.scope()?
I use tensorflow2.1.2 wheel
Thank you for your time
To add some context, the Graphcore TensorFlow wheel includes a port of Keras for the IPU, available as tensorflow.python.ipu.keras. You can access the API documentation for IPU Keras at this link. This module contains IPU-specific optimised replacement for TensorFlow Keras classes Model and Sequential, plus more high-performance, multi-IPU classes e.g. PipelineModel and PipelineSequential.
As per your specific issue, you are right when you mention that there are no IPU-specific ways to load pre-trained Keras models at present. I would encourage you, as you appear to have access to IPUs, to reach out to Graphcore Support. When doing so, please attach your pre-trained Keras model model1.h5 and a self-contained reproducer of your code.
Switching topic to the recompilation question: using an executable cache prevents recompilation, you can set that up with environmental variable TF_POPLAR_FLAGS='--executable_cache_path=./cache'. I'd also recommend to take a look into the following resources:
this tutorial gathers several considerations around recompilation and how to avoid it when using TensorFlow2 on the IPU.
Graphcore TensorFlow documentation here explains how to use the pre-compile mode on the IPU.
I am new in Pytorch. My question is: How do I apply transfer learning to a custom dataset? I am doing image segmentation on brain tumors. I can find examples which use U-net structure but I could not find examples using weights of the pre-trained models for a U-net image segmentation?
You could obtain pre-trained models in two ways:
Model weights or complete models shared in formats such .pt or .pth:
In this case, Saving and Loading Models is a good starting point. Copying from the tutorial there, you could load a model as
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
The other way is to load the model from torchvision. A list is available models is available at Torchvision Models. U-Net is not available yet. However, it is possible to load a pre-trained model as the encoder and write a separate decoder to form a U-Net with a pre-trained encoder.
In this case, the model object returned from the function calls shown in the API are already loaded with pretrained weights when pretrained=True.
For writing a custom dataloader, PyTorch data loaders may be a useful guide.
I am using Pytorch for image classification. I am looking for CNN models pretrained on a dataset other than ImageNet, I have found a link to a ".ckpt" file. I also found tutorials on loading this file with Tenserflow, but not using pytorch.
How can I load pretrained model using Pytorch from ".ckpt" file ?
I agree with #jodag that in general, PyTorch and Tensorflow are not interoperable. There are some special cases in which you may be able to do this. For example, HuggingFace provides support for converting the transformer model from TensorFlow to PyTorch.
There is a related (though closed) question on DataScience StackExchange where the idea is to rewrite the Tensorflow model into PyTorch and then loads the weights from the checkpoint file. Note that this can get tricky at times.
Refer to this to train a GAN model for MNIST dataset, I want to save a model and restore it for further prediction. After having some understanding of Saving and Importing a Tensorflow Model I am able to save and restore some variables of inputs and outputs but for this network I am able to save the model only after some specific iterations and not able to predict some output.
Did you refer to this guide? It explains very clearly how to load and save tensorflow models in all possible formats.
If you are new to ML, I'd recommend you give Keras a try first, which is much easier to use. See https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model, pretty much you can use:
model.save('my_model.h5')
to save your model to disk.
model = load_model('my_model.h5')
to load your model and make prediction
I am using gradient boosting regressor to build a predictive model.
After all the tuning/CV, finally I get my prediction right. I am now thinking about dump the model to a file, so that my production c++ program can load it and use it.
It seem that sklearn provides model persistence through pickle, but I am wondering if there is a way to convert pickle model into txt, like what xgboost has. My production code is c++ so having pickle as media is really not handy
Is there a 'dumpModel' function in the library?
Anyone has any experience ?
Thanks