EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 600+ Courses All in One Bundle
  • Login
Home Software Development Software Development Tutorials PyTorch Tutorial PyTorch Model
Secondary Sidebar
PyTorch Tutorial
  • PyTorch
    • PyTorch Image Classification
    • PyTorch Random
    • PyTorch Variable
    • PyTorch Activation Function
    • Python Formatted String
    • PyTorch GPU
    • PyTorch CUDA
    • PyTorch DataLoader
    • PyTorch LSTM
    • PyTorch Pad
    • PyTorch OpenCL
    • PyTorch Lightning
    • PyTorch SoftMax
    • PyTorch Flatten
    • PyTorch gan
    • PyTorch max
    • PyTorch pip
    • PyTorch Parameter
    • PyTorch Load Model
    • PyTorch Distributed
    • PyTorch BERT
    • PyTorch interpolate
    • PyTorch JIT
    • PyTorch expand
    • PyTorch AMD
    • PyTorch GRU
    • PyTorch rnn
    • PyTorch permute
    • PyTorch argmax
    • PyTorch SGD
    • PyTorch nn
    • PyTorch One Hot Encoding
    • PyTorch Tensors
    • What is PyTorch?
    • PyTorch MSELoss()
    • PyTorch NLLLOSS
    • PyTorch MaxPool2d
    • PyTorch Pretrained Models
    • PyTorch Squeeze
    • PyTorch Reinforcement Learning
    • PyTorch zero_grad
    • PyTorch norm
    • PyTorch VAE
    • PyTorch Early Stopping
    • PyTorch requires_grad
    • PyTorch MNIST
    • PyTorch Conv2d
    • Dataset Pytorch
    • PyTorch tanh
    • PyTorch bmm
    • PyTorch profiler
    • PyTorch unsqueeze
    • PyTorch adam
    • PyTorch backward
    • PyTorch concatenate
    • PyTorch Embedding
    • PyTorch Tensor to NumPy
    • PyTorch Normalize
    • PyTorch ReLU
    • PyTorch Autograd
    • PyTorch Transpose
    • PyTorch Object Detection
    • PyTorch Autoencoder
    • PyTorch Loss
    • PyTorch repeat
    • PyTorch gather
    • PyTorch sequential
    • PyTorch U-NET
    • PyTorch Sigmoid
    • PyTorch Neural Network
    • PyTorch Quantization
    • PyTorch Ignite
    • PyTorch Versions
    • PyTorch TensorBoard
    • PyTorch Dropout
    • PyTorch Model
    • PyTorch optimizer
    • PyTorch ResNet
    • PyTorch CNN
    • PyTorch Detach
    • Single Layer Perceptron
    • PyTorch vs Keras
    • torch.nn Module

PyTorch Model

PyTorch Model

Introduction to PyTorch Model

Python class represents the model where it is taken from the module with atleast two parameters defined in the program which we call as PyTorch Model. One model will have other models or attributes of other models in the same network which represents other parameters as well. Whole model should be called for each computation and predictions of the output results. An iterator is retrieved over all the parameters of the model.

What is PyTorch Model?

A model with different parameters in the same module and the same dataset where the data is from tensors or CUDA from which we can create different iterators is called PyTorch Model. We can set the model to a training model which does not train the model as such but will set the dataset to different methods of dropout and others. We can create a linear or nested model based on our requirements and manage the model with the parameters.

PyTorch Model Overviews

The initial step is to prepare the model where input and output data will be numerical. We can use Python libraries to load the data and PyTorch to customize the dataset. Also, any transforms can be done to the dataset using scaling or encoding activities. A DataLoader class is provided that helps in navigating the dataset while the training and evaluation of the model happen.

We must define the model as well where the class is defined with different layers such as linear, Conv2d, and maxpool2d layers. Activation functions can also be used to define the models. The next step is to train the model with loss function and optimization function. We have to clear the last errors followed by a forward pass of input to the model. The next step is to calculate the loss of the function and we have to check for errors in the function by backpropagation. We must update the model so that we can reduce the loss in the function.

Start Your Free Software Development Course

Web development, programming languages, Software testing & others

We should evaluate the model using the test dataset. Again, DataLoader is useful here where we can collect the predictions and a performance metric can be calculated. This model can be used for the next set of predictions of new data. Hence, PyTorch tensor is used to wrap the dataset where we can do differentiation tasks along with NumPy functions.

Use PyTorch Model

We can use the PyTorch model for new predictions from the old model so that a new dataset can be created. It is good to import NumPy and matplotlib while doing predictions in the model. If NumPy is imported, we should disable grad or else numpy will not work and may give inconsistent results. The collected dataset should be checked thoroughly and the information should be specified such as index, and item loads. We can split the data into image and target. The next step is to generate the prediction where mlp instance is fed into the module and we can name it according to the neural network in the module.

The prediction gives a list of probabilities by Softmax and hence this must be converted to a class with np. argmax. The highest probable value is taken into consideration here. The next step is to reshape the model image where matplotlib can be used to check the visualization of the model. Now, visualization is completed and the prediction can be set where the actual target is considered as the title. The saved model can also be considered for practice.

Load a pre-trained Model

We have to create the instance of the network where an argument should be passed. We should note that the keys of state_dict given as input to the model should exactly be the same as keys of state_dict output of the model. KeyError will be raised if the names and keys are not present in the new model.

All in One Software Development Bundle(600+ Courses, 50+ projects)
Python TutorialC SharpJavaJavaScript
C Plus PlusSoftware TestingSQLKali Linux
Price
View Courses
600+ Online Courses | 50+ projects | 3000+ Hours | Verifiable Certificates | Lifetime Access
4.6 (86,560 ratings)

def my_keys_dict(self, state_dict):
model_keys = self.state_dict()
for key, parameters in state_dict.items():
if key not in model_keys:
continue
if instancegiven(parameters, values):
parameters = parameters.data
model_keys[key].copy_(parameters)

If needed, we can remove all the keys while loading the model and then load the weights once the model is loaded.

pretrained_model = ...
stat_dict = model.state_dict()
pretrained_model = {a: i for a, i in pretrained_model.items() if a in stat_dict}
stat_dict.update(pretrained_model)
model.load_state_dict(pretrained_model)

We can also restore the weights in the model as per the previous model.

self.dict = models.__stat.dict__[args.arch](pretrained = False,
numbered_classes = args.classes,
auxiliary_logits = False)

if self. args.pretrained:

print("=> Pretrained model '{}'".format(args.arch))
pretrained_dict = model_zoo.load_url(model [args.arch])
model = self._model._dict()
pretrained_state = { a:i for a,i in pretrained_state.iteritems() if a in model_state and i.size() == model_state[a].size() }
model_state.update(pretrained_state)
self._model.load_state_dict(model_state)

We can use model.named_parameters() where a key name is returned along with the corresponding parameter name. This helps in identifying the parameter along with the dictionary stats.

PyTorch Model – Load the entire model

We should save the model first before loading the same. We can use the following command to save the model.

Torch.save(modelname, path_where_model_is_saved)

We can load the model with simple command.

Modelname = torch.load(path_where_model_is_saved)
Model.eval()

This method helps to save the model with the least code and we can save the entire Python module using this code. When we save the code using this method, data is stored in a serialized manner and directory structure is used in the code. Model class is not saved as normal methods, but the path where the model is stored is saved and this makes certain changes in the model when the same model is used in other classes.

We can use the file extension .pt or .pth to save the model. It is important to call a model. eval() where dropout and normalization layers are set so that the model will be set to evaluation mode. Inconsistent results will be received from the output end if we do not do this step.

Conclusion

We should save the dictionary and an optimizer so that we can resume the training of the model in any later point. Models and optimizers should be initialized following the dictionary of the model. PyTorch model is very important for the entire network and it is necessary to know the basic steps in the model.

Recommended Articles

This is a guide to PyTorch Model. Here we discuss Introduction, overview, What is PyTorch Model is, Examples along with the codes and outputs. You may also have a look at the following articles to learn more –

  1. What is PyTorch?
  2. PyTorch vs Keras
  3. PyTorch Versions
  4. Tensorflow vs Pytorch
Popular Course in this category
Machine Learning Training (20 Courses, 29+ Projects)
  19 Online Courses |  29 Hands-on Projects |  178+ Hours |  Verifiable Certificate of Completion
4.7
Price

View Course
0 Shares
Share
Tweet
Share
Primary Sidebar
Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Live Classes
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Java Tutorials
  • Python Tutorials
  • All Tutorials
Certification Courses
  • All Courses
  • Software Development Course - All in One Bundle
  • Become a Python Developer
  • Java Course
  • Become a Selenium Automation Tester
  • Become an IoT Developer
  • ASP.NET Course
  • VB.NET Course
  • PHP Course

ISO 10004:2018 & ISO 9001:2015 Certified

© 2022 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Software Development Course

C# Programming, Conditional Constructs, Loops, Arrays, OOPS Concept

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA Login

Forgot Password?

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA
Free Software Development Course

Web development, programming languages, Software testing & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you

By signing up, you agree to our Terms of Use and Privacy Policy.

Let’s Get Started

By signing up, you agree to our Terms of Use and Privacy Policy.

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

Loading . . .
Quiz
Question:

Answer:

Quiz Result
Total QuestionsCorrect AnswersWrong AnswersPercentage

Explore 1000+ varieties of Mock tests View more