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AI-Expert-Roadmap | Roadmap to becoming an Artificial Intelligence Expert | Machine Learning library

 by   AMAI-GmbH JavaScript Version: Current License: MIT

 by   AMAI-GmbH JavaScript Version: Current License: MIT

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kandi X-RAY | AI-Expert-Roadmap Summary

AI-Expert-Roadmap is a JavaScript library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. AI-Expert-Roadmap has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
Roadmap to becoming an Artificial Intelligence Expert in 2021
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  • AI-Expert-Roadmap has a medium active ecosystem.
  • It has 13925 star(s) with 1218 fork(s). There are 723 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 9 open issues and 17 have been closed. On average issues are closed in 6 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of AI-Expert-Roadmap is current.
AI-Expert-Roadmap Support
Best in #Machine Learning
Average in #Machine Learning
AI-Expert-Roadmap Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • AI-Expert-Roadmap has 0 bugs and 0 code smells.
AI-Expert-Roadmap Quality
Best in #Machine Learning
Average in #Machine Learning
AI-Expert-Roadmap Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • AI-Expert-Roadmap has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • AI-Expert-Roadmap code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
AI-Expert-Roadmap Security
Best in #Machine Learning
Average in #Machine Learning
AI-Expert-Roadmap Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • AI-Expert-Roadmap is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
AI-Expert-Roadmap License
Best in #Machine Learning
Average in #Machine Learning
AI-Expert-Roadmap License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • AI-Expert-Roadmap releases are not available. You will need to build from source code and install.
  • It has 2577 lines of code, 0 functions and 8 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
AI-Expert-Roadmap Reuse
Best in #Machine Learning
Average in #Machine Learning
AI-Expert-Roadmap Reuse
Best in #Machine Learning
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AI-Expert-Roadmap Key Features

Roadmap to becoming an Artificial Intelligence Expert in 2021

Community Discussions

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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 AI-Expert-Roadmap

You can download it from GitHub.

Support

Have a look at the contribution docs for how to update any of the roadmaps.

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