torch-light | Basic nns like Logistic | Machine Learning library
kandi X-RAY | torch-light Summary
kandi X-RAY | torch-light Summary
This repository includes basics and advanced examples for deep learning by using Pytorch. Basics which are basic nns like Logistic, CNN, RNN, LSTM are implemented with few lines of code, advanced examples are implemented by complex model. It is better finish Official Pytorch Tutorial before this.
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Top functions reviewed by kandi - BETA
- Convert a tree into a conll file
- Join a list of words
- Iterate over the strings in a sequence
- Prints debug information to stderr
- Train DarkNet
- Calculate the average accuracy of the detection
- Calculate the margin loss
- Computes the loss of reconstruction loss
- Compute the loss of the loss function
- Load a corpus from a file
- Run the model
- Performs a single step
- Predict the given prediction
- Return the list of all mention spans for the given word
- Calculate the average accuracy accuracy
- Predict next prediction
- Perform the forward computation
- Train actor - critic
- Join a list of words together
- Perform a single step
- Save the trained model
- Create embedding
- Pre - train critic
- Go to play
- Compute the forward layer
- Preprocess training dataset
- Forward a story
torch-light Key Features
torch-light Examples and Code Snippets
Community Discussions
Trending Discussions on torch-light
QUESTION
On Google Colaboratory, I have tried all 3 runtimes: CPU, GPU, TPU. All give the same error.
Cells:
...ANSWER
Answered 2021-Aug-19 at 14:08Searching online; there semes to be many causes for this same problem.
In my case, setting Accelerator to None in Google Colaboratory solved this.
QUESTION
I have installed pytorch version 1.10.0 alongside torchtext, torchvision and torchaudio using conda. My PyTorch is cpu-only, and I have experimented with both conda install pytorch-mutex -c pytorch and conda install pytorch cpuonly -c pytorch to install the cpuonly version, both yielding the same eror that I will describe in the following lines.
I have also installed pytorch-lightning in conda, alongside jsonargparse[summaries via pip in the environment.
I have written this code to see whether LightningCLI works or not.
ANSWER
Answered 2021-Nov-24 at 22:00So in order to fix the problem, I had to change my environment.yaml in order to force pytorch to install from the pytorch channel.
So this is my environment.yaml now:
QUESTION
Doing things on Google Colab.
- transformers: 4.10.2
- pytorch-lightning: 1.2.7
ANSWER
Answered 2021-Sep-20 at 13:25The Trainer needs to call its .fit() in order to set up a lot of things and then only you can do .test() or other methods.
You are right about putting a .fit() just before .test() but the fit call needs to a valid one. You have to feed a dataloader/datamodule to it. But since you don't want to do a training/validation in this fit call, just pass limit_[train/val]_batches=0 while Trainer construction.
QUESTION
I would like to to multiply following two tensors x (of shape (BS, N, C)) and y (of shape (BS,1,C)) in the following way:
...ANSWER
Answered 2021-Sep-10 at 14:08Is there anything wrong with x*y? As you can see in the code below, it yields exactly the same output as your function:
QUESTION
Unable to use Automatic Logging (self.log) when calling training_step() on Pytorch Lightning, what am I missing? Here is a minimal example:
ANSWER
Answered 2021-Aug-25 at 17:45This is NOT the correct usage of LightningModule class. You can't call a hook (namely .training_step()) manually and expect everything to work fine.
You need to setup a Trainer as suggested by PyTorch Lightning at the very start of its tutorial - it is a requirement. The functions (or hooks) that you define in a LightningModule merely tells Lightning "what to do" in a specific situation (in this case, at each training step). It is the Trainer that actually "orchestrates" the training by instantiating the necessary environment (including Logging functionality) and feeding it into the Lightning Module whenever needed.
So, do it the way Lightning suggests and it will work.
QUESTION
I want to make a dataset using NumPy and then want to train and test a simple model like 'linear, or logistic`.
I am trying to learn Pytorch Lightning. I have found a tutorial that we can use the NumPy dataset and can use uniform distribution here. As a newcomer, I am not getting the full idea, how can I do that!
My code is given below
...ANSWER
Answered 2021-May-07 at 16:25This code will return you label as y and a,b as 2 features of 500 random examples merged into X.
QUESTION
I am trying to tokenize some numerical strings using a WordLevel/BPE tokenizer, create a data collator and eventually use it in a PyTorch DataLoader to train a new model from scratch.
However, I am getting an error
AttributeError: 'ByteLevelBPETokenizer' object has no attribute 'pad_token_id'
when running the following code
...ANSWER
Answered 2021-Mar-27 at 16:25The error tells you that the tokenizer needs an attribute called pad_token_id. You can either wrap the ByteLevelBPETokenizer into a class with such an attribute (... and met other missing attributes down the road) or use the wrapper class from the transformers library:
QUESTION
I trained a vanilla vae which I modified from this repository. When I try and use the trained model I am unable to load the weights using load_from_checkpoint. It seems there is a mismatch between my checkpoint object and my lightningModule object.
I have setup an experiment (VAEXperiment) using pytorch-lightning LightningModule. I try to load the weights into the network with:
ANSWER
Answered 2020-Aug-04 at 12:45Posting the answer from comments:
QUESTION
In Pytorch-Lightning you usually never have to specify cuda or gpu. But when I want to create a gaussian sampled Tensor using torch.normal I get
ANSWER
Answered 2020-Aug-30 at 18:36The recommended way is to do lights = torch.normal(0, 1, size=[100, 3], device=self.device) if this is inside lightning class.
You could also do: lights = torch.normal(0, 1, size=[100, 3]).type_as(tensor), where tensor is some tensor which is on cuda.
QUESTION
I am training a variational autoencoder, using pytorch-lightning. My pytorch-lightning code works with a Weights and Biases logger. I am trying to do a parameter sweep using a W&B parameter sweep.
The hyperparameter search procedure is based on what I followed from this repo.
The runs initialise correctly, but when my training script is run with the first set of hyperparameters, i get the following error:
...ANSWER
Answered 2020-Aug-20 at 05:18Do you launch python in your shell by typing python or python3?
Your script could be calling python 2 instead of python 3.
If this is the case, you can explicitly tell wandb to use python 3. See this section of documentation, in particular "Running Sweeps with Python 3".
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Install torch-light
You can use torch-light like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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