[Buy Me a Coffee](ko-fi.com/superkai)☕ *Memos: - [My post](https://dev.to/hyperkai/layers-in-pytorch-2-3f8d) explains Recurrent Layer. - [My post](https://dev.to/hyperkai/lstm-in-pytorch-53k7) explains [LSTM()](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html). - [My post](https://dev.to/hyperkai/gru-in-pytorch-4l7d) explains [GRU()](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html). - [My post](https://dev.to/hyperkai/transformer-in-pytorch-24ok) explains [Transformer()](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html). - [My post](https://dev.to/hyperkai/manualseed-initialseed-and-seed-in-pytorch-5gm8) explains [manual_seed()](https://pytorch.org/docs/stable/generated/torch.manual_seed.html). - [My post](https://dev.to/hyperkai/requiresgradtrue-with-a-tensor-backward-and-retaingrad-in-pytorch-4kf7) explains [requires_grad](https://pytorch.org/docs/stable/generated/torch.Tensor.requires_grad.html). [RNN()](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html) can get the two 2D or 3D tensors of the one or more elements computed by RNN from the 2D or 3D tensor of zero or more elements as shown below: *Memos: - The 1st argument for initialization is `input_size`(Required-Type:`int`). *It must be `0 <= x`. - The 2nd argument for initialization is `hidden_size`(Required-Type:`int`). *It must be `1 <= x`. - The 3rd argument for initialization is `num_layers`(Optional-Default:`1`-Type:`int`): *Memos: - It must be `1 <= x`. - It must be `1 < x`, if `dropout` is `0 < x`. - Its number is same as the number of `bias_ih_lx`, `bias_ih_lx_reverse`, `bias_hh_lx`, `bias_hh_lx_reverse`, `weight_ih_lx`, `weight_ih_lx_reverse`, `weight_hh_lx` and `weight_hh_lx_reverse` so if it's `3`, there are `bias_ih_l0`, `bias_ih_l1`, `bias_ih_l2`, `bias_ih_l0_reverse`, `bias_ih_l1_reverse`, `bias_ih_l2_reverse`, `bias_hh_l0`, `bias_hh_l1` etc. - The 4th argument for initialization is `nonlinearity`(Optional-Default:`1`-Type:`str`). *`'tanh'` or `'relu'` can be set. - The 5th argument for initialization is `bias`(Optional-Default:`True`-Type:`bool`). *[My post](https://dev.to/hyperkai/set-bias-argument-in-pytorch-1djp) explains `bias` argument. - The 6th argument for initialization is `batch_first`(Optional-Default:`False`-Type:`bool`). - The 7th argument for initialization is `dropout`(Optional-Default:`0.0`-Type:`int` or `float`). *It must be `0 <= x <= 1`. - The 8th argument for initialization is `bidirectional`(Optional-Default:`False`-Type:`bool`). *If it's `True`, `bias_ih_lx_reverse`, `bias_hh_lx_reverse`, `weight_ih_lx_reverse` and `weight_hh_lx_reverse` are added. - The 9th argument for initialization is `device`(Optional-Default:`None`-Type:`str`, `int` or [device()](https://pytorch.org/docs/stable/tensor_attributes.html#torch.device)): *Memos: - If it's `None`, [get_default_device()](https://pytorch.org/docs/stable/generated/torch.get_default_device.html) is used. *[My post](https://dev.to/hyperkai/setdefaultdtype-setdefaultdevice-and-setprintoptions-in-pytorch-55g8) explains `get_default_device()` and [set_default_device()](https://pytorch.org/docs/stable/generated/torch.set_default_device.html). - `device=` must be used. - [My post](https://dev.to/hyperkai/set-and-get-device-in-pytorch-3o7h) explains `device` argument. - The 10th argument for initialization is `dtype`(Optional-Default:`None`-Type:[dtype](https://pytorch.org/docs/stable/tensor_attributes.html#torch.dtype)): *Memos: - If it's `None`, [get_default_dtype()](https://pytorch.org/docs/stable/generated/torch.get_default_dtype.html) is used. *[My post](https://dev.to/hyperkai/setdefaultdtype-setdefaultdevice-and-setprintoptions-in-pytorch-55g8) explains `get_default_dtype()` and [set_default_dtype()](https://pytorch.org/docs/stable/generated/torch.set_default_dtype.html). - `dtype=` must be used. - [My post](https://dev.to/hyperkai/set-and-get-dtype-in-pytorch-2apo) explains `dtype` argument. - The 1st argument is `input`(Required-Type:`tensor` of `float` or `complex`): *Memos: - It must be the 2D or 3D tensor of zero or more elements. - The number of the elements of the deepest dimension must be same as `input_size`. - Its `device` and `dtype` must be same as `RNN()`'s. - `complex` must be set to `dtype` of `RNN()` to use a `complex` tensor. - The tensor's `requires_grad` which is `False` by default is set to `True` by `RNN()`. - The 2nd argument is `hx`(Optional-Default:`None`-Type:`tensor` of `float` or `complex`). *Its D, `device` and `dtype` must be same as `input`'s. - `rnn1.device` and `rnn1.dtype` don't work. ```python import torch from torch import nn tensor1 = torch.tensor([[8., -3., 0., 1., 5., -2.]]) tensor1.requires_grad # False torch.manual_seed(42) rnn1 = nn.RNN(input_size=6, hidden_size=3) tensor2 = rnn1(input=tensor1) tensor2 # (tensor([[0.9590, -0.9501, 0.9999]], grad_fn=), # tensor([[0.9590, -0.9501, 0.9999]], grad_fn=)) tensor2[0].requires_grad tensor2[1].requires_grad # True rnn1 # RNN(6, 3) rnn1.input_size # 6 rnn1.hidden_size # 3 rnn1.num_layers # 1 rnn1.nonlinearity # 'tanh' rnn1.bias # True rnn1.batch_first # False rnn1.dropout # 0.0 rnn1.bidirectional # False rnn1.bias_ih_l0 # Parameter containing: # tensor([-0.3471, 0.0545, -0.5702], requires_grad=True) rnn1.bias_hh_l0 # Parameter containing: # tensor([0.5214, -0.4904, 0.4457], requires_grad=True) rnn1.weight_ih_l0 # Parameter containing: # tensor([[0.4414, 0.4792, -0.1353, 0.5304, -0.1265, 0.1165], # [-0.2811, 0.3391, 0.5090, -0.4236, 0.5018, 0.1081], # [0.4266, 0.0782, 0.2784, -0.0815, 0.4451, 0.0853]], # requires_grad=True) rnn1.weight_hh_l0 # Parameter containing: # tensor([[-0.2695, 0.1472, -0.2660], # [-0.0677, -0.2345, 0.3830], # [-0.4557, -0.2662, -0.1630]], # requires_grad=True) torch.manual_seed(42) rnn2 = nn.RNN(input_size=3, hidden_size=3) rnn2(input=tensor2[0]) rnn2(input=tensor2[1]) # (tensor([[-0.4656, 0.5770, 0.0342]], grad_fn=), # tensor([[-0.4656, 0.5770, 0.0342]], grad_fn=)) torch.manual_seed(42) rnn = nn.RNN(input_size=6, hidden_size=3, num_layers=1, nonlinearity='tanh', bias=True, batch_first=False, dropout=0.0, bidirectional=False, device=None, dtype=None) rnn(input=tensor1, hx=None) rnn(input=tensor1, hx=torch.tensor([[0., 0., 0.]])) # (tensor([[0.9590, -0.9501, 0.9999]], grad_fn=), # tensor([[0.9590, -0.9501, 0.9999]], grad_fn=)) my_tensor = torch.tensor([[8., -3., 0.], [1., 5., -2.]]) torch.manual_seed(42) rnn = nn.RNN(input_size=3, hidden_size=3) rnn(input=my_tensor) # (tensor([[0.9421, 0.9998, -0.9963], # [0.9921, -0.2163, 0.6621]], grad_fn=), # tensor([[0.9921, -0.2163, 0.6621]], grad_fn=)) my_tensor = torch.tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]]) torch.manual_seed(42) rnn = nn.RNN(input_size=1, hidden_size=3) rnn(input=my_tensor) # (tensor([[0.9991, 0.9997, -0.6159], # [-0.5700, -0.8242, 0.6535], # [0.2195, 0.6271, 0.2563], # [0.6928, 0.8575, 0.4446], # [0.9935, 0.9969, -0.1270], # [-0.1506, -0.4145, 0.6044]], grad_fn=), # tensor([[-0.1506, -0.4145, 0.6044]], grad_fn=)) my_tensor = torch.tensor([[[8.], [-3.], [0.]], [[1.], [5.], [-2.]]]) torch.manual_seed(42) rnn = nn.RNN(input_size=1, hidden_size=3) rnn(input=my_tensor) # (tensor([[[0.9991, 0.9997, -0.6159], # [-0.7527, -0.7232, 0.6466], # [0.3320, 0.4802, 0.3485]], # [[0.8069, 0.6334, 0.2359], # [0.9809, 0.9968, -0.2792], # [-0.3643, -0.1861, 0.6483]]], grad_fn=), # tensor([[[0.8069, 0.6334, 0.2359], # [0.9809, 0.9968, -0.2792], # [-0.3643, -0.1861, 0.6483]]], grad_fn=)) my_tensor = torch.tensor([[[8.+0.j], [-3.+0.j], [0.+0.j]], [[1.+0.j], [5.+0.j], [-2.+0.j]]]) torch.manual_seed(42) rnn = nn.RNN(input_size=1, hidden_size=3, dtype=torch.complex64) rnn(input=my_tensor) # (tensor([[[0.9985-1.9315e-04j, -0.9702+4.0555e-01j, -0.9542+1.9353e-01j], # [-1.0401+1.6018e-01j, 1.4827+4.1275e-01j, 0.3302-5.8074e-01j], # [0.1235-9.3636e-01j, 0.2921-2.3584e-01j, -0.1383-2.1280e-01j]], # [[-0.7388+4.8243e-01j, -0.1439+6.0930e-01j, -0.1099-4.2196e-01j], # [0.9995+1.1798e-03j, -0.6179-1.3320e+00j, -0.6428+1.3737e-01j], # [-1.8129+5.6506e-01j, 1.4051+4.5077e-01j, 0.2443-1.2884e-01j]]], # grad_fn=), # tensor([[[-0.7388+4.8243e-01j, -0.1439+6.0930e-01j, -0.1099-4.2196e-01j], # [0.9995+1.1798e-03j, -0.6179-1.3320e+00j, -0.6428+1.3737e-01j], # [-1.8129+5.6506e-01j, 1.4051+4.5077e-01j, 0.2443-1.2884e-01j]]], # grad_fn=)) ```

recurrentlayer

RNN in PyTorch

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