HowToSD-ComfyUI-Pt-Wrapper/docs/reference/ptn_rnn.md

Ptn RNN

A recurrent neural network (RNN) model consisting of one or more of a recurrent layer.

Args:
    input_size (int): The number of input features.
    hidden_size (int): The number of output features of the hidden layer matrix.
    num_layers (int): Number of hidden layers.
    nonlinearity (str): Activation function to apply after each hidden layer. Specify 'tanh' or 'relu'.
    bias (bool): Use bias or not.
    batch_first (bool): Indicates the shape of the input. Input is assumed to be a rank-3 tensor. If True, the shape is [Batch, Seq, Token]. If False, the shape is [Seq, Batch, Token]. Note that default is set to `True` unlike PyTorch's RNN default model parameter.
    dropout (float): Dropout probability to be applied to the output of intermediate layers if non-zero. This will not be applied to the last layer's output.
    bidirectional (bool): Processes the input in a backward direction and append the result to the output of the forward direction.

Input

Name	Data type
input_size	Int
hidden_size	Int
num_layers	Int
nonlinearity
bias	Boolean
batch_first	Boolean
dropout	Float
bidirectional	Boolean

Output

Data type
Ptmodel

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Category: PyTorch wrapper - Model

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