---
license: mit
---

# test rnn

The Models in test rnn is for [Huggingface Candle PR#2542](https://github.com/huggingface/candle/pull/2542) as example test cases.

Test models are refered to Pytorch [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html) and [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html).

Test models are generated by the following codes:

- lstm_test.pt: A simple LSTM model with 1 layer.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.LSTM(10, 20, num_layers=1, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, (hn, cn) = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    state_dict['cn'] = cn
    torch.save(state_dict, "lstm_test.pt")
    ```

- gru_test.pt: A simple GRU model with 1 layer.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.GRU(10, 20, num_layers=1, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, hn = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    torch.save(state_dict, "gru_test.pt")
    ```

- bi_lstm_test.pt: A bidirectional LSTM model with 1 layer.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.LSTM(10, 20, num_layers=1, bidirectional=True, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, (hn, cn) = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    state_dict['cn'] = cn
    torch.save(state_dict, "bi_lstm_test.pt")
    ```

- bi_gru_test.pt: A bidirectional GRU model with 1 layer.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.GRU(10, 20, num_layers=1, bidirectional=True, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, hn = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    torch.save(state_dict, "bi_gru_test.pt")
    ```

- lstm_nlayer_test.pt: A LSTM model with 3 layers.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.LSTM(10, 20, num_layers=3, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, (hn, cn) = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    state_dict['cn'] = cn
    torch.save(state_dict, "lstm_nlayer_test.pt")
    ```

- bi_lstm_nlayer_test.pt: A bidirectional LSTM model with 3 layers.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.LSTM(10, 20, num_layers=3, bidirectional=True, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, (hn, cn) = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    state_dict['cn'] = cn
    torch.save(state_dict, "bi_lstm_nlayer_test.pt")
    ```

- gru_nlayer_test.pt: A GRU model with 3 layers.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.GRU(10, 20, num_layers=3, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, hn = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    torch.save(state_dict, "gru_nlayer_test.pt")
    ```

- bi_gru_nlayer_test.pt: A bidirectional GRU model with 3 layers.

    ```python
    import torch
    import torch.nn as nn

    rnn = nn.GRU(10, 20, num_layers=3, bidirectional=True, batch_first=True)
    input = torch.randn(5, 3, 10)
    output, hn = rnn(input)

    state_dict = rnn.state_dict()
    state_dict['input'] = input
    state_dict['output'] = output.contiguous()
    state_dict['hn'] = hn
    torch.save(state_dict, "bi_gru_nlayer_test.pt")
    ```