# Pytorch Parameter

This tutorial helps NumPy or TensorFlow users to pick up PyTorch quickly. This is beyond the scope of this particular lesson. If inputis a matrix with m rows, outis an matrix of shape m × n. Any operations performed on such modules or tensors will be carried out using fast FP16 arithmetic. In pytorch, any Parameter is a special kind of Tensor. So here, we see that this is a three-dimensional PyTorch tensor. By default lightning prints a list of parameters and submodules when it starts training. Here I would like to give a piece of advice too. Being a high-level API on top of TensorFlow, we can say that Keras makes TensorFlow easy. This method is especially powerful when building neural networks to save time on one epoch by calculating differentiation of the parameters at the forward pass. Basically it's the facebook solution to merge torch with python. This function will execute the model and record a trace of what operators are used to compute the outputs. To calculate the loss we first define the criterion then pass in the output of our network and correct labels. its parameters, and will appear e. This is a guide to the main differences I’ve found between PyTorch and TensorFlow. parameters()) If you want to calculate only the trainable parameters:. A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value. For this example we will use a tiny dataset of images from the COCO dataset. When a model is loaded in PyTorch, all its parameters have their ‘requires_grad‘ field set to true by default. classifier's parameters will use a learning rate of 1e-3, and a momentum of 0. The nn modules in PyTorch provides us a higher level API to build and train deep network. Any code dependencies of the model’s class, including the class definition itself, should be included in one of the following locations:. Choosing 'fan_in' preserves the magnitude of the variance of the weights in the forward pass. If this parameter is specified, it will take precedence over other environment-related parameters like use_gpu, custom_docker_image, conda_packages, or pip_packages. That is, PyTorch will silently "spy" on the operations you perform on its datatypes and, behind the scenes, construct - again - a computation graph. Represents a semi-markov or segmental CRF with C classes of max width K. PyTorch is a machine learning framework with a strong focus on deep neural networks. In PyTorch, the learnable parameters (e. The total number of parameters in AlexNet is the sum of all parameters in the 5 Conv Layers + 3 FC Layers. its parameters, and will appear e. parameters optimizer. Unlike the older variant, PyTorch no longer uses the Lua language and LuaJIT. ResNet50 is one of those having a good tradeoff between accuracy and inference time. for any copyright issue contact - [email protected] So here, we see that this is a three-dimensional PyTorch tensor. Achieves good accuracy and keeps perfect privacy. After each. The total number of parameters in AlexNet is the sum of all parameters in the 5 Conv Layers + 3 FC Layers. There are two types of GAN researches, one that applies GAN in interesting problems and one that attempts to stabilize the training. 1 comes with new APIs, support for Boolean tensors and custom recurrent neural networks, and an upgrade of the JIT compiler for optimizing computational graphs. In the previous blog we discussed about PyTorch, it's strengths and why should you learn it. 1-release 0. pytorch中我们有时候可能需要设定某些变量是参与训练的，这时候就需要查看哪些是可训练参数，以确定这些设置是成功的。 pytorch中model. Identifying handwritten digits using Logistic Regression in PyTorch. weights and biases) of an torch. pytorch_model. Tensor` subclasses, that have a very special property when used with :class:`Module` s - when they're assigned as Module attributes they are automatically added to the list of its parameters, and will appear e. Errors will be reported on invalid combinations. It performs the backpropagation starting from a variable. Today we're expanding this ecosystem with: BoTorch (Bayesian Optimization for PyTorch): A package for optimizing hyper-parameters in the model, for training and inference. What is PyTorch? PyTorch is an open-source deep learning library released by Facebook. PyTorch code is simple. In this course, Foundations of PyTorch, you will gain the ability to leverage PyTorch support for dynamic computation graphs, and contrast that with other popular frameworks such as TensorFlow. Unlike Caffe2, I don’t have to write C++ code and write build scripts. A state_dict is simply a Python dictionary object that maps each layer to its parameter tensor. There are a limited number of Anaconda packages with GPU support for IBM POWER 8/9 systems as well. PyTorch is a popular deep learning framework due to its easy-to-understand API and its completely imperative approach. Variable vs Parameter. Count number trainable parameters in a pytorch model - pytorch_count_params. Module model are contained in the model's parameters (accessed with model. Module has model. PyTorch: Defining new autograd functions. This post should be quick as it is just a port of the previous Keras code. backward() is the main PyTorch magic that uses PyTorch's Autograd feature. Facebook's PyTorch 1. Represents a semi-markov or segmental CRF with C classes of max width K. The idea is to teach you the basics of PyTorch and how it can be used to implement a neural…. PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. PyTorch: PyTorch is one of the newest deep learning framework which is gaining popularity due to its simplicity and ease of use. PyTorch I Biggest diﬀerence: Static vs. This is beyond the scope of this particular lesson. TensorFlow vs. tensor – an n-dimensional torch. Because we are using Y_train as an index for another tensor while calculating the loss, I have converted it into a long tensor. Here, the weights and bias parameters for each layer are initialized as the tensor variables. assigned as Module attributes they are automatically added to the list of. Note, the pretrained model weights that comes with torchvision. PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. 0 preview with many nice features such as a JIT for model graphs (with and without tracing) as well as the LibTorch, the PyTorch C++ API, one of the most important release announcement made today in my opinion. The forward function computes output Tensors from input Tensors. Achieves good accuracy and keeps perfect privacy. 1 comes with new APIs, support for Boolean tensors and custom recurrent neural networks, and an upgrade of the JIT compiler for optimizing computational graphs. The data field of a Tensor or a Parameter is where the actual values are and if you apply indexing to a Parameter or Tensor, the indexing is magically applied to the data field. Variational Autoencoder (VAE) in Pytorch. A category of posts focused on production usage of PyTorch. In this video, we want to concatenate PyTorch tensors along a given dimension. This section describes the training regime for our models. Once we have created an object, we can “call” the object with the relevant. Linear Regression using PyTorch Linear Regression is a very commonly used statistical method that allows us to determine and study the relationship between two continuous variables. 9 will be used for all parameters 很简单就不翻译了。 Taking an optimization step. We will use PyTorch to implement an object detector based on YOLO v3, one of the faster object detection algorithms out there. The result is PyTorch IR, a convenient graph representation of PyTorch programs. The Parameter class extends the tensor class, and so the weight tensor inside every layer is an instance of this Parameter class. data is a Tensor x. in parameters() iterator. A Deep Learning VM with PyTorch can be created quickly from the GCP Marketplace within the GCP Console without having to use the command line. parameters(): p. Among the different deep learning libraries I have used – PyTorch is the most flexible and easy to use. You will then see how the multi-processing data parallel and distributed data parallel approaches to distributed training can be used in PyTorch. parameters` iterator. Pytorch Lightning vs PyTorch Ignite vs Fast. in CNTK, the Parameter and input_variable are subclasses of the Variable class so you do not need to wrap them into a Variable object as in Pytorch, but since you build a graph, so you have to call eval or grad methods on any element of the graph with input values to evaluate them. ← The Difference Between a PyTorch Tensor and a Parameter. Module model are contained in the model's parameters (accessed with model. Remove the passing of the embedding layer parameters to the optimizer. PyTorch documentation¶. These vectors will then be learnt as a parameters by the. PyTorch is one such library. ← The Difference Between a PyTorch Tensor and a Parameter. Every neuron in the network computes the same output \rightarrow computes the same gradient \rightarrow same parameter updates ; PyTorch uses Lecun initialization. Args: pos_u: list of center word ids for positive word pairs. Trainer [Github Code]The lightning trainer abstracts best practices for running a training, val, test routine. The in-person PyTorch Summer Hackathon at Menlo Park has come to an end. In each iteration, we execute the forward pass, compute the derivatives of output w. It performs the backpropagation starting from a variable. Sometimes for compound types we use type variables. Facebook AI has created and is now open-sourcing PyTorch-BigGraph (PBG), a tool that makes it much faster and easier to produce graph embeddings for extremely large graphs. In my opinion, PyTorch's automatic differentiation engine, called Autograd is a brilliant tool to understand how automatic differentiation works. This is done with the aid of the torch. Linear Regression is linear approach for modeling the relationship between inputs and the predictions. Be especially careful about defaults values. Series: YOLO object detector in PyTorch How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1. In this tutorial we will implement a simple neural network from scratch using PyTorch and Google Colab. We have created variables x … - Selection from Deep Learning with PyTorch [Book]. This parameter defines the dimensions of the network output and is typically set to the number of classes in the dataset. SGD optimizer that says: The implementation of SGD with Momentum/Nesterov subtly differs from Sutskever et. This implementation is distorted because PyTorch's autograd is undergoing refactoring right now. 04 Nov 2017 | Chandler. In this notebook we will use PyTorch to construct a convolutional neural network. Pytorch Lightning vs PyTorch Ignite vs Fast. This is a simple but complete pytorch-version implementation of Nvidia's Style-based GAN[3]. PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. GRUs are faster to train as compared to LSTMs due to the fewer number of weights and parameters to update during training. Unlike Caffe2, I don’t have to write C++ code and write build scripts. However, the major advantage of TensorFlow is that the entire graph can be saved as a protocol buffer and yes this includes parameters and operations as well. Visualize results with TensorBoard. Most graph embedding methods result quite constrained when applied to large graph structures. More work will be done to ensure the hand off between PyTorch and TVM code is efficient. Parameter objects, we will learn about this class in a next part where we explore advanced PyTorch functionality. You can vote up the examples you like or vote down the ones you don't like. weights and biases) of an torch. Often, custom backends and hardware require specialized compilation technqiues. Then we have seen how to download and visualize the FashionMNIST dataset. PyTorch Documentation. Neural networks in Pytorch As you know, a neural network : Is a function connecting an input to an output Depends on (a lot of) parameters In Pytorch, a neural network is a class that implements the base class torch. PyTorch example: freezing a part of the net (including fine-tuning) - freeze_example. pytorch中我们有时候可能需要设定某些变量是参与训练的，这时候就需要查看哪些是可训练参数，以确定这些设置是成功的。 pytorch中model. You will then see how the multi-processing data parallel and distributed data parallel approaches to distributed training can be used in PyTorch. It is a two-step process to tell PyTorch not to change the weights of the embedding layer: Set the requires_grad attribute to False, which instructs PyTorch that it does not need gradients for these weights. PyTorch is a machine learning framework with a strong focus on deep neural networks. It’s also important to check default parameters of each module like epsilons and make sure you are using the same ones in PyTorch than the TensorFlow. The data field of a Tensor or a Parameter is where the actual values are and if you apply indexing to a Parameter or Tensor, the indexing is magically applied to the data field. In this course, Foundations of PyTorch, you will gain the ability to leverage PyTorch support for dynamic computation graphs, and contrast that with other popular frameworks such as TensorFlow. The idea is to teach you the basics of PyTorch and how it can be used to implement a neural…. The main loop iterates over a number of epochs and on each epoch we iterate through the train DataLoader. Construct the loss function with the help of Gradient Descent optimizer as shown below − Construct the. Conv2d, the authors of PyTorch defined the weights and biases to be parameters to that of a layer. tensor – an n-dimensional torch. Types for each parameter, and the return type, are displayed following standard Python type hint syntax. This is one of the major feature which I like about PyTorch :D. While PyTorch provides a similar level of flexibility as TensorFlow, it has a much cleaner interface. PyTorch deviates from the basic intuition of programming in Python in one particular way: it records the execution of the running program. Applications are: Incorporate SpeedTorch into your data pipelines for fast data transfer to/from CPU <-> GPU. So, firstly, we have to understand what deep learning is? Deep learning & machine learning are ways to achieve Artificial Intelligence. Creating object for PyTorch’s Linear class with parameters in_features and out_features. ResNet50 is one of those having a good tradeoff between accuracy and inference time. As you know, tensors are arrays with an arbitrary number of dimensions, corresponding to NumPy's ndarrays. Module object returns it's so called parameters (Implemented as nn. The major difference from Tensorflow is that PyTorch methodology is considered "define-by-run" while Tensorflow is considered "defined-and-run", so on PyTorch you can for instance change your model on run-time, debug easily with any python debugger, while tensorflow has always a graph definition/build. HyperOpt uses random search and tree parzen estimators, which won't be as data-efficient as BayesOpt / Ax / BoTorch. Module model are contained in the model’s parameters (accessed with model. Thus, our input size is 784. Linear() creates a parameter that can be adjusted. the link weights that we're familiar with. The cookie settings on this website are set to "allow cookies" to give you the best browsing experience possible. parameters (), lr = learning_rate) ''' STEP 7: TRAIN THE MODEL ''' iter = 0 for epoch in range (num_epochs): for i, (images, labels) in enumerate (train_loader): # Load images as tensors with gradient accumulation abilities images = images. Without further ado, let's get to it!. PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the back-end code. parameters() to the Optimizer instance so they can be updated. PyTorch’s website has a 60 min. The DataLoader yields one batch of data and targets which we pass through the model. In PyTorch 1. So, to make an RNN in PyTorch, we need to pass 2 mandatory parameters to the class — input_size and hidden_size. 前言这篇文章算是论坛PyTorch Forums关于参数初始化和finetune的总结，也是我在写代码中用的算是"最佳实践"吧。最后希望大家没事多逛逛论坛，有很多高质量的回答。. I started with the VAE example on the PyTorch github, adding explanatory comments and Python type annotations as I was working my way through it. Additionally, if module goes to GPU, parameters goes as well. Types that are defined by fastai or Pytorch link directly to more information about that type; try clicking Image in the function above for an example. This includes unifying the threading model, allocators and reducing the overhead associated with copying inputs into TVM. prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. This post should be quick as it is just a port of the previous Keras code. Trainer [Github Code]The lightning trainer abstracts best practices for running a training, val, test routine. Pytorch Lightning vs PyTorch Ignite vs Fast. Read the Docs. pytorch中我们有时候可能需要设定某些变量是参与训练的，这时候就需要查看哪些是可训练参数，以确定这些设置是成功的。 pytorch中model. Abstract:PyTorch is a deep learning framework based on Python language. I think ssRBM needs to be tested on data set such as the Cifar but first, we are required to confirm that the existing implementation of ssRBM do achieve good results on some parameters so we can replicate the same results. 1 Layer LSTM Groups of Parameters. Exporting models in PyTorch is done via tracing. 0 early this year with integrations for Google Cloud, AWS , and Azure Machine Learning. Basically it's the facebook solution to merge torch with python. Losses in PyTorch. We've train this model on our new anime face dataset and a subset of FFHQ, you can download our pre-trained model to evaluate or continue training by yourself. PyTorch uses a method called automatic differentiation. FloatTensor as input and produce a single output tensor. In PyTorch, the computation graph is created for each iteration in an epoch. Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/pytorch. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this. pytorch-rl implements some state-of-the art deep reinforcement learning algorithms in Pytorch, especially those concerned with continuous action spaces. So two different PyTorch IntTensors. The first step is to do parameter initialization. weights and biases) of an torch. By jamesdmccaffrey | Published August 30, 2019. The PyTorch variant this article discusses, however, is a completely new development. This is why we see the Parameter containing text at the top of the string representation output. In PyTorch, the learnable parameters (e. Variable is the central class of the package. 0 early this year with integrations for Google Cloud, AWS , and Azure Machine Learning. Also, 10 digits are present in this and hence, we can have 10 different outputs. By selecting different configuration options, the tool in the PyTorch site shows you the required and the latest wheel for your host platform. During training, I will pass m. blitz tutorial, which is laid out pretty well. PyTorch redesigns and implements Torch in Python while sharing the same core C libraries for the back-end code. The nn modules in PyTorch provides us a higher level API to build and train deep network. Use PySyft over PyTorch to perform Federated Learning on the MNIST dataset with less than 10 lines to change. PyTorch is one of the most popular Deep Learning frameworks that is based on Python and is supported by Facebook. A major drawback of manual search is the difﬁculty in reproducing results. After importing PyTorchModel from sagemaker. Linear Regression using PyTorch Linear Regression is a very commonly used statistical method that allows us to determine and study the relationship between two continuous variables. half() on a tensor converts its data to FP16. This tutorial is broken into 5 parts: Part 1 (This one): Understanding How YOLO works. I will demonstrate basic PyTorch operations and show you how similar they are to NumPy. PyTorch is a popular deep learning framework due to its easy-to-understand API and its completely imperative approach. PyTorch-Transformers (formerly known as pytorch-pretrained-bert) is a library of state-of-the-art pre-trained models for Natural Language Processing (NLP). This Estimator executes an PyTorch script in a managed PyTorch execution environment, within a SageMaker Training Job. A system, which requires multiple numbers of variables to describe, can be described with a lesser number of parameters. PyTorch code is simple. parameters()) return total_params Sign up for free to join this conversation on GitHub. "PyTorch - Neural networks with nn modules" Feb 9, 2018. In this blog, we will jump into some hands-on examples of using pre-trained networks present in TorchVision module for Image Classification. However, it's implemented with pure C code and the gradient are computed manually. A place to discuss PyTorch code, issues, install, research. Computation graph in PyTorch is defined during runtime. Example PyTorch script for finetuning a ResNet model on your own data. A Deep Learning VM with PyTorch can be created quickly from the GCP Marketplace within the GCP Console without having to use the command line. Every once in a while, a python library is developed that has the potential of changing the landscape in the field of deep learning. PyTorch’s website has a 60 min. Variable vs Parameter. parameters()) If you want to calculate only the trainable parameters:. In this post, we discussed the FashionMNIST dataset and the need to replace MNIST dataset. autograd和 使用我们的 C 库编写自定义的C扩展。. PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. It was great seeing researchers and developers from the PyTorch community come together to build creative solutions that can have a positive impact on people and businesses. Conditional random fields in PyTorch. parameters()函数定义如下:defp 博文 来自： 刘伟的博客. Looking at the x, we have 58, 85, 74. Also, we shall train for five times on the entire dataset. PyTorch Errors Series: ValueError: optimizer got an empty parameter list 07 Nov 2018 • PyTorch Errors Series We are going to write a flexible fully connected network, also called a dense network. 11_5 model=torchvision. If replacement is True, samples are drawn with replacement. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features. Use PySyft over PyTorch to perform Federated Learning on the MNIST dataset with less than 10 lines to change. Facebook launched PyTorch 1. Working with transfer learning models in Pytorch means choosing which layers to freeze and which to unfreeze. size()) for x in model. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 6 - 3 April 19, 2018April 18, 2019 Administrative Friday’s section on PyTorch and Tensorflow will be at. Among the various deep. 一般来说,pytorch的Parameter是一个tensor,但是跟通常意义上的tensor有些不一样1)通常意义上的tensor仅仅是数据2)而Parameter所对应的tensor除了包含数据之 博文 来自： Strive_For_Future的博客. Word2vec is so classical ans widely used. Any environment option not directly exposed through other parameters to the Estimator construction can be set using this parameter. Briefly, Parameter objects are just Tensor objects that are maintained in an internal PyTorch list so that performance can be improved. Parameter [source] ¶ A kind of Tensor that is to be considered a module parameter. It's worth stepping back and spending a little while exploring why that's the case, and what PyTorch does differently. When we print it, we can see that we have a PyTorch IntTensor of size 2x3x4. PyTorch Documentation, 0. PyTorch has a special class called Parameter. PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. These learnable parameters, once randomly set, will update over time as we learn. However, it's implemented with pure C code and the gradient are computed manually. It can be found in it's entirety at this Github repo. The neural network architecture is the same as DeepMind used in the paper Human-level control through deep reinforcement learning. 15 if you are not using RoCE or InfiniBand. PyTorch executes and Variables and operations immediately. Working with transfer learning models in Pytorch means choosing which layers to freeze and which to unfreeze. xavier_uniform (p) return model # Small example model. The managed PyTorch environment is an Amazon-built Docker container that executes functions defined in the supplied entry_point Python script. The Parameter class extends the tensor class, and so the weight tensor inside every layer is an instance of this Parameter class. A model can be defined in PyTorch by subclassing the torch. Visualize results with TensorBoard. Use Adadelta, Adamax, RMSprop, Rprop, ASGD, AdamW, and Adam optimizers for sparse embeddings training. You can read more nn. PyTorch is another deep learning library that's is actually a fork of Chainer(Deep learning library completely on python) with the capabilities of torch. The method will return the predicted values for the tensores that. PyTorch has a simple API. Pytorch is also faster in some cases than other frameworks, but you will discuss this later in the other section. Defining the forward function for passing the inputs to the regressor object initialized by the constructor. THIS IS A DIRECTORY PAGE. In our dataset, the image size is 28*28. The DataLoader yields one batch of data and targets which we pass through the model. Config parameters for specific pipeline components, keyed by component name. This is important because it helps accelerate numerical computations, which can increase the speed of neural networks by 50 times or greater. PyTorch is an optimized tensor library for deep learning using GPUs and CPUs. In each iteration, we execute the forward pass, compute the derivatives of output w. This shows how to train a neural network in a completely encrypted way to learn to predict on MNIST. PyTorch code is simple. add_(-lr, p. for any copyright issue contact - [email protected] Being a high-level API on top of TensorFlow, we can say that Keras makes TensorFlow easy. PyTorch vs Apache MXNet¶. "PyTorch - Basic operations" Feb 9, 2018. When a model is loaded in PyTorch, all its parameters have their 'requires_grad' field set to true by default. Before starting PyTorch, we should know about deep learning. Example PyTorch script for finetuning a ResNet model on your own data. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this. This infers in creating the respective convent or sample neural network with torch. Facebook AI has created and is now open-sourcing PyTorch-BigGraph (PBG), a tool that makes it much faster and easier to produce graph embeddings for extremely large graphs. models went into a home folder ~/. However, this comes at a cost of requiring a large amount of data, which is sometimes not available. pos_v: list of neibor word ids for positive word pairs. Sometimes for compound types we use type variables. These parameters are the number of inputs and outputs at a time to the regressor. parameters (), lr = learning_rate) ''' STEP 7: TRAIN THE MODEL ''' iter = 0 for epoch in range (num_epochs): for i, (images, labels) in enumerate (train_loader): # Load images as tensors with gradient accumulation abilities images = images. Federated Learning made easy and scalable. Because we are using Y_train as an index for another tensor while calculating the loss, I have converted it into a long tensor. Remove the passing of the embedding layer parameters to the optimizer. So here, we see that this is a three-dimensional PyTorch tensor. The most convenient way of defining our network is by creating a new class which extends nn. Looking at the x, we have 58, 85, 74. The Difference Between a PyTorch Tensor and a Parameter Posted on August 30, 2019 by jamesdmccaffrey A couple of days ago, I was asked if I could give a few talks/lectures on PyTorch to a small group of new employees (well, not exactly, but a complete explanation would take too long). So, to make an RNN in PyTorch, we need to pass 2 mandatory parameters to the class — input_size and hidden_size. In this tutorial we will implement a simple neural network from scratch using PyTorch and Google Colab. Here, the weights and bias parameters for each layer are initialized as the tensor variables. Data augmentation and preprocessing. PyTorch script. (Preferably this code should be run on GPU since it would lead to faster results and hence faster hyper parameter tuning). quantization and pruning of model parameters) can be applied to a deep neural network after it has been trained. backward()’, the gradients for the parameters are calculated. Skip to content. parameters()) return total_params Sign up for free to join this conversation on GitHub. A category of posts focused on production usage of PyTorch. all the parameters automatically based on the computation graph that it creates dynamically. If you continue to use this website without changing your cookie settings or you click "Accept" below then you are consenting to this. 前言这篇文章算是论坛PyTorch Forums关于参数初始化和finetune的总结，也是我在写代码中用的算是"最佳实践"吧。最后希望大家没事多逛逛论坛，有很多高质量的回答。. xavier_uniform (p) return model # Small example model. When the constructor is called, torch initializes and sets a Lua table with the user-defined metatable, which makes the table an object. PyTorch Advantages and Weakness. PyTorch Tutorial: Check the TorchVision version by printing the version parameter. Linear() creates a parameter that can be adjusted. I tried to used PyTorch for implementing the LSTM for speech tagging and I don't quite understand some of the parameters for torch.