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fake-news-detection-LIAR-pytorch | Fake News Detection by Learning Convolution | Machine Learning library

 by   ekagra-ranjan Python Version: Current License: No License

 by   ekagra-ranjan Python Version: Current License: No License

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kandi X-RAY | fake-news-detection-LIAR-pytorch Summary

fake-news-detection-LIAR-pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. fake-news-detection-LIAR-pytorch has no bugs, it has no vulnerabilities and it has low support. However fake-news-detection-LIAR-pytorch build file is not available. You can download it from GitHub.
Fake News Detection by Learning Convolution Filters through Contextualized Attention
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  • fake-news-detection-LIAR-pytorch has a low active ecosystem.
  • It has 19 star(s) with 7 fork(s). There are 1 watchers for this library.
  • It had no major release in the last 12 months.
  • fake-news-detection-LIAR-pytorch has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of fake-news-detection-LIAR-pytorch is current.
This Library - Support
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Average in #Machine Learning
This Library - Support
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Average in #Machine Learning

quality kandi Quality

  • fake-news-detection-LIAR-pytorch has 0 bugs and 0 code smells.
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This Library - Quality
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Average in #Machine Learning

securitySecurity

  • fake-news-detection-LIAR-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • fake-news-detection-LIAR-pytorch code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
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Average in #Machine Learning

license License

  • fake-news-detection-LIAR-pytorch does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
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This Library - License
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  • fake-news-detection-LIAR-pytorch releases are not available. You will need to build from source code and install.
  • fake-news-detection-LIAR-pytorch 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.
  • fake-news-detection-LIAR-pytorch saves you 283 person hours of effort in developing the same functionality from scratch.
  • It has 683 lines of code, 15 functions and 6 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
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Top functions reviewed by kandi - BETA

kandi has reviewed fake-news-detection-LIAR-pytorch and discovered the below as its top functions. This is intended to give you an instant insight into fake-news-detection-LIAR-pytorch implemented functionality, and help decide if they suit your requirements.

  • Driver function for training data
    • Prepare training data
    • Train model
    • Load test data
    • Construct a BaselineNet
    • Convert a dataset into a tensor
    • Calculate validation accuracy
    • Return the number of words in a vocabulary
    • Find word in word2num

Get all kandi verified functions for this library.

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fake-news-detection-LIAR-pytorch Key Features

Fake News Detection by Learning Convolution Filters through Contextualized Attention

Community Discussions

Trending Discussions on Machine Learning
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  • CUDA OOM - But the numbers don't add upp?
  • How to compare baseline and GridSearchCV results fair?
  • Getting Error 524 while running jupyter lab in google cloud platform
  • TypeError: brain.NeuralNetwork is not a constructor
  • Ordinal Encoding or One-Hot-Encoding
  • How to increase dimension-vector size of BERT sentence-transformers embedding
  • How to identify what features affect predictions result?
Trending Discussions on Machine Learning

QUESTION

Using RNN Trained Model without pytorch installed

Asked 2022-Feb-28 at 20:17

I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
    self.hidden = self.init_hidden()
  def forward(self, feature_list):
    feature_list=torch.tensor(feature_list)
    
    if self.matching_in_out:
      lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
      output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
      output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
      return output_scores #output_scores
    else:
      for i in range(len(feature_list)):
        cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
        cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
        lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
        outs=self.hidden2out(lstm_out)
      return outs
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))

I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

  • nn.LSTM
  • nn.Linear
  • torch.sigmoid

I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

EDIT

I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

rnn = nn.LSTM(10, 20, 2)
input = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
c0 = torch.randn(2, 3, 20)
output, (hn, cn) = rnn(input, (h0, c0))

and also for linear:

m = nn.Linear(20, 30)
input = torch.randn(128, 20)
output = m(input)

ANSWER

Answered 2022-Feb-17 at 10:47

You should try to export the model using torch.onnx. The page gives you an example that you can start with.

An alternative is to use TorchScript, but that requires torch libraries.

Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

A running example

Just modifying a little your example to go over the errors I found

Notice that via tracing any if/elif/else, for, while will be unrolled

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
  def forward(self, x, h0, c0):
    lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
    outs=self.hidden2out(lstm_out)
    return outs, (hidden_a, hidden_b)
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())

# convert the arguments passed during onnx.export call
class MWrapper(nn.Module):
    def __init__(self, model):
        super(MWrapper, self).__init__()
        self.model = model;
    def forward(self, kwargs):
        return self.model(**kwargs)

Run an example

rnn = RNN(10, 10, 10, 3)
X = torch.randn(3,1,10)
h0,c0  = rnn.init_hidden()
print(rnn(X, h0, c0)[0])

Use the same input to trace the model and export an onnx file


torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                  dynamic_axes={'x':{1:'N'},
                               'c0':{1: 'N'},
                               'h0':{1: 'N'}
                               },
                  input_names=['x', 'h0', 'c0'],
                  output_names=['y', 'hn', 'cn']
                 )

Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

Next we load the ONNX model and pass the same inputs

import onnxruntime
ort_model = onnxruntime.InferenceSession('rnn.onnx')
print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))

Source https://stackoverflow.com/questions/71146140

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

Vulnerabilities

No vulnerabilities reported

Install fake-news-detection-LIAR-pytorch

You can download it from GitHub.
You can use fake-news-detection-LIAR-pytorch 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.

Support

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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