pytorch-tutorial | PyTorch Tutorial for Deep Learning Researchers | Learning library

 by   yunjey Python Version: Current License: MIT

kandi X-RAY | pytorch-tutorial Summary

kandi X-RAY | pytorch-tutorial Summary

pytorch-tutorial is a Python library typically used in Institutions, Learning, Education, Tutorial, Learning, Deep Learning, Pytorch applications. pytorch-tutorial has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However pytorch-tutorial build file is not available. You can download it from GitHub.

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

            kandi-support Support

              pytorch-tutorial has a medium active ecosystem.
              It has 26754 star(s) with 7764 fork(s). There are 624 watchers for this library.
              It had no major release in the last 6 months.
              There are 64 open issues and 113 have been closed. On average issues are closed in 46 days. There are 21 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pytorch-tutorial is current.

            kandi-Quality Quality

              pytorch-tutorial has 0 bugs and 0 code smells.

            kandi-Security Security

              pytorch-tutorial has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pytorch-tutorial code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pytorch-tutorial is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pytorch-tutorial releases are not available. You will need to build from source code and install.
              pytorch-tutorial has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              pytorch-tutorial saves you 556 person hours of effort in developing the same functionality from scratch.
              It has 1301 lines of code, 65 functions and 21 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pytorch-tutorial and discovered the below as its top functions. This is intended to give you an instant insight into pytorch-tutorial implemented functionality, and help decide if they suit your requirements.
            • Performs forward transformation
            • Encodes the given x into a binary quadrature
            • Reparameterize the model
            • Decodes the input into a SSigmoid
            • Resizes images in given directory
            • Resize an image
            • Create a new layer
            • 3x3x3 Conv2d Conv2d
            • Builds a vocabulary
            • Adds a word to the vocabulary
            • Decodes the input into a sigmoid
            • Resets gradients
            • Normalize x
            • Updates the learning rate of the optimizer
            • Adds a scalar summary
            • Write a histogram summary
            • Sample the model
            • Load an image
            • Return data loader
            • Read data from file
            • Writes image summary
            • Detach from a list of states
            Get all kandi verified functions for this library.

            pytorch-tutorial Key Features

            No Key Features are available at this moment for pytorch-tutorial.

            pytorch-tutorial Examples and Code Snippets

            Pythondot img1Lines of Code : 111dot img1License : Non-SPDX (NOASSERTION)
            copy iconCopy
            from torcheeg.datasets import DEAPDataset
            from torcheeg.datasets.constants.emotion_recognition.deap import DEAP_CHANNEL_LOCATION_DICT
            dataset = DEAPDataset(io_path=f'./tmp_out/deap',
            Pythondot img2Lines of Code : 29dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            from onnx_pytorch import code_gen
            code_gen.gen("resnet18-v2-7.onnx", "./")
            import numpy as np
            import onnx
            import onnxruntime
            import torch
            from model import Model
            model = Model()
            inp = np.random.randn(1, 3, 22  
            pytorch to onnx
            C++dot img3Lines of Code : 12dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            import torch
            import torchvision
            import torch.onnx
            # An instance of your model
            model = torchvision.models.resnet18()
            # An example input you would normally provide to your model's forward() method
            x = torch.rand(1, 3, 224, 224)
            # Export the model

            Community Discussions


            IndexError: tensors used as indices must be long, byte or bool tensors - Pytorch
            Asked 2022-Feb-19 at 14:46

            I'm using a pre-trained image captioning model from this Repository, but I'm getting this error although I changed the type to long !!

            Error :

            File "", line 213, in seq, alphas = caption_image_beam_search(encoder, decoder, args.img, word_map, args.beam_size) File "", line 111, in caption_image_beam_search seqs =[seqs[prev_word_inds].long(), next_word_inds.unsqueeze(1)], dim=1) # (s, step+1) IndexError: tensors used as indices must be long, byte or bool tensors

            Code :



            Answered 2022-Feb-19 at 14:46

            You have cast the wrong part to long:



            Pytorch LSTM - generating sentence- word by word?
            Asked 2022-Jan-02 at 19:24

            I'm trying to implement a neural network to generate sentences (image captions), and I'm using Pytorch's LSTM (nn.LSTM) for that.

            The input I want to feed in the training is from size batch_size * seq_size * embedding_size, such that seq_size is the maximal size of a sentence. For example - 64*30*512.

            After the LSTM there is one FC layer (nn.Linear). As far as I understand, this type of networks work with hidden state (h,c in this case), and predict the next word each time.

            My question is- in the training - do we have to manually feed the sentence word by word to the LSTM in the forward function, or the LSTM knows how to do it itself?

            My forward function looks like this:



            Answered 2022-Jan-02 at 19:24

            The answer is, LSTM knows how to do it on its own. You do not have to manually feed each word one by one. An intuitive way to understand is that the shape of the batch that you send, contains seq_length (batch.shape[1]), using which it decides the number of words in the sentence. The words are passed through LSTM Cell generating the hidden states and C.



            Clone the repository to use Python with Azure functions
            Asked 2021-Dec-23 at 04:50

            Trying to figure out the issue with my cmd as it is getting stucked.

            As I tried to run below commands to get the virtual env enabled..



            Answered 2021-Dec-23 at 04:50

            Thank you Luis and shaILU. Posting your suggestion as an answer to help other community members.

            If you are using git-bash, you can try running following command:



            PyTorch training with dropout and/or batch-normalization
            Asked 2020-Jul-30 at 10:36

            A model should be set in the evaluation mode for inference by calling model.eval().
            Do we need to also do this during training before getting the model outputs? Like within a training epoch if the network contains one or more dropout and/or batch-normalization layers.

            If this is not done then the output of the forward pass in the training epoch might be affected by the randomness in the dropout?

            Many example codes do not do this and something along these lines is the common approach:



            Answered 2020-Jul-30 at 10:36


            Should this instead be?



            More explanation:
            Different Modules behave differently depending on whether they are in training or evaluation/test mode.
            BatchNorm and Dropout are only two examples of such modules, basically any module that has a training phase follows this rule.
            When you do .eval(), you are signaling all modules in the model to shift operations accordingly.

            The answer is during training you should not use eval mode and yes, as long as you have not set the eval mode, the dropout will be active and act randomly in each forward passes. Similarly all other modules that have two phases, will perform accordingly. That is BN will always update the mean/var for each pass, and also if you use batch_size of 1, it will error out as it can not do BN with batch of 1

            As it was pointed out in comments, it should be noted that during training, you should not do eval() before the forward pass, as it effectively disables all modules that has different phases for train/test mode such as BN and Dropout (basically any module that has updateable/learnable parameters, or impacts network topology like dropout) will be disabled and you will not see them contributing to your network learning. So don't code like that!

            Let me explain a bit what happens during training:
            When you are in training mode, all of your modules that make up your model may have two modes, training and test mode. These modules either have learnable parameters that need to be updated during training, like BN, or affect network topology in a sense like Dropout (by disabling some features during forward pass). some modules such as ReLU() only operate in one mode and thus do not have any change when modes change.
            When you are in training mode, you feed an image, it passes trough layers until it faces a dropout and here, some features are disabled, thus theri responses to the next layer is omitted, the output goes to other layers until it reaches the end of the network and you get a prediction.

            the network may have correct or wrong predictions, which will accordingly update the weights. if the answer was right, the features/combinations of features that resulted in the correct answer will be positively affected and vice versa. So during training you do not need and should not disable dropout, as it affects the output and should be affecting it so that the model learns a better set of features.

            I hope this makes it a bit more clear for you. if you still feel you need more, say so in the comments.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install pytorch-tutorial

            You can download it from GitHub.
            You can use pytorch-tutorial 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.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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