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SimpleLSTM | recurrent neural network heavily inspired by Long Short | Machine Learning library

 by   evolvingstuff Java Version: Current License: MIT

 by   evolvingstuff Java Version: Current License: MIT

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kandi X-RAY | SimpleLSTM Summary

SimpleLSTM is a Java library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Neural Network applications. SimpleLSTM has no vulnerabilities, it has a Permissive License and it has low support. However SimpleLSTM has 1 bugs and it build file is not available. You can download it from GitHub.
This is a recurrent neural network architecture inspired by Long Short Term Memory, but with a much simpler architecture. I will be adding more documentation to describe this architecture shortly. For now, a brief summary is that it has only a single gate (similar to the Forget Gate in LSTM) and an input squashing function. Rather than acting to reset the state of the cell, the gate modulates a weighted average of the current input with the state of the cell at the previous time step. So if the gate is fully active, the cell will ignore its current input and fully retain its state. If the gate is inactive then the cell will lose all traces of its previous state and shift to match the current input. So far on a few tests I've done, it appears to perform fairly well, although it tends to need more cell blocks than LSTM to do the same thing.
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kandi-support Support

  • SimpleLSTM has a low active ecosystem.
  • It has 20 star(s) with 11 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of SimpleLSTM is current.
SimpleLSTM Support
Best in #Machine Learning
Average in #Machine Learning
SimpleLSTM Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • SimpleLSTM has 1 bugs (0 blocker, 1 critical, 0 major, 0 minor) and 65 code smells.
SimpleLSTM Quality
Best in #Machine Learning
Average in #Machine Learning
SimpleLSTM Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • SimpleLSTM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • SimpleLSTM code analysis shows 0 unresolved vulnerabilities.
  • There are 1 security hotspots that need review.
SimpleLSTM Security
Best in #Machine Learning
Average in #Machine Learning
SimpleLSTM Security
Best in #Machine Learning
Average in #Machine Learning

license License

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

buildReuse

  • SimpleLSTM releases are not available. You will need to build from source code and install.
  • SimpleLSTM has no build file. You will be need to create the build yourself to build the component from source.
  • It has 353 lines of code, 25 functions and 10 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
SimpleLSTM Reuse
Best in #Machine Learning
Average in #Machine Learning
SimpleLSTM Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Generates a list of interactions for each test
    • Evaluate the fitness for the agent .
      • Returns the index of the vector .
        • Find the element of a vector .
          • Factory method to create the neurons .
            • Computes the derivative .
              • Function activation function activation function .

                Get all kandi verified functions for this library.

                Get all kandi verified functions for this library.

                SimpleLSTM Key Features

                A recurrent neural network heavily inspired by Long Short Term Memory, but simpler.

                Community Discussions

                Trending Discussions on SimpleLSTM
                • TypeError: forward() missing 1 required positional argument with Tensorboard PyTorch
                Trending Discussions on SimpleLSTM

                QUESTION

                TypeError: forward() missing 1 required positional argument with Tensorboard PyTorch

                Asked 2020-Jun-18 at 20:48

                I am trying to write my model to tensorboard with the following code:

                model = SimpleLSTM(4, HIDDEN_DIM, HIDDEN_LAYERS, 1, BATCH_SIZE, device)
                writer = tb.SummaryWriter(log_dir=tb_path)
                sample_data = iter(trainloader).next()[0]
                writer.add_graph(model, sample_data.to(device))
                

                I get the error: TypeError: forward() missing 1 required positional argument: 'batch_size'

                My model looks like this:

                class SimpleLSTM(nn.Module):
                
                    def __init__(self, input_dims, hidden_units, hidden_layers, out, batch_size, device):
                        super(SimpleLSTM, self).__init__()
                        self.input_dims = input_dims
                        self.hidden_units = hidden_units
                        self.hidden_layers = hidden_layers
                        self.batch_size = batch_size
                        self.device = device
                        self.lstm = nn.LSTM(self.input_dims, self.hidden_units, self.hidden_layers,
                                            batch_first=True, bidirectional=False)
                        self.output_layer = nn.Linear(self.hidden_units, out)
                
                    def init_hidden(self, batch_size):
                
                        hidden = torch.rand(self.hidden_layers, batch_size, self.hidden_units, device=self.device, dtype=torch.float32)
                        cell = torch.rand(self.hidden_layers, batch_size, self.hidden_units, device=self.device, dtype=torch.float32)
                        hidden = nn.init.xavier_normal_(hidden)
                        cell = nn.init.xavier_normal_(cell)
                        return (hidden, cell)
                
                    def forward(self, input, batch_size):
                        hidden = self.init_hidden(batch_size)  incomplete batch
                        lstm_out, (h_n, c_n) = self.lstm(input, hidden)
                        raw_out = self.output_layer(h_n[-1])
                        return raw_out
                

                How can I write this model to TensorBoard?

                ANSWER

                Answered 2020-Jun-18 at 20:48

                Your model takes two arguments input and batch_size, but you only provide one argument for add_graph to call your model with.

                The inputs (second argument to add_graph) should be a tuple with the input and the batch_size:

                writer.add_graph(model, (sample_data.to(device), BATCH_SIZE))
                

                You don't really need to provide the batch size to the forward method, because you can infer it from the input. As your LSTM uses batch_first=True, it means that the input is required to have size [batch_size, seq_len, num_features], therefore the size of the first dimension is the current batch size.

                def forward(self, input):
                    batch_size = input.size(0)
                    # ...
                

                Source https://stackoverflow.com/questions/62453430

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

                Vulnerabilities

                No vulnerabilities reported

                Install SimpleLSTM

                You can download it from GitHub.
                You can use SimpleLSTM like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the SimpleLSTM component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                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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