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92 lines
3.5 KiB
Python
92 lines
3.5 KiB
Python
import logging
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import torch.nn as nn
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import torch
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logger = logging.getLogger(__name__)
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class PyTorchMLPModel(nn.Module):
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"""
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A multi-layer perceptron (MLP) model implemented using PyTorch.
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:param input_dim: The number of input features. This parameter specifies the number
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of features in the input data that the MLP will use to make predictions.
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:param output_dim: The number of output classes. This parameter specifies the number
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of classes that the MLP will predict.
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:param hidden_dim: The number of hidden units in each layer. This parameter controls
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the complexity of the MLP and determines how many nonlinear relationships the MLP
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can represent. Increasing the number of hidden units can increase the capacity of
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the MLP to model complex patterns, but it also increases the risk of overfitting
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the training data. Default: 256
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:param dropout_percent: The dropout rate for regularization. This parameter specifies
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the probability of dropping out a neuron during training to prevent overfitting.
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The dropout rate should be tuned carefully to balance between underfitting and
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overfitting. Default: 0.2
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:param n_layer: The number of layers in the MLP. This parameter specifies the number
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of layers in the MLP architecture. Adding more layers to the MLP can increase its
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capacity to model complex patterns, but it also increases the risk of overfitting
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the training data. Default: 1
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:returns: The output of the MLP, with shape (batch_size, output_dim)
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"""
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def __init__(self, input_dim: int, output_dim: int, **kwargs):
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super(PyTorchMLPModel, self).__init__()
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hidden_dim: int = kwargs.get("hidden_dim", 256)
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dropout_percent: int = kwargs.get("dropout_percent", 0.2)
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n_layer: int = kwargs.get("n_layer", 1)
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self.input_layer = nn.Linear(input_dim, hidden_dim)
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self.blocks = nn.Sequential(*[Block(hidden_dim, dropout_percent) for _ in range(n_layer)])
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self.output_layer = nn.Linear(hidden_dim, output_dim)
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self.relu = nn.ReLU()
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self.dropout = nn.Dropout(p=dropout_percent)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.relu(self.input_layer(x))
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x = self.dropout(x)
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x = self.blocks(x)
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logits = self.output_layer(x)
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return logits
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class Block(nn.Module):
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"""
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A building block for a multi-layer perceptron (MLP).
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:param hidden_dim: The number of hidden units in the feedforward network.
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:param dropout_percent: The dropout rate for regularization.
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:returns: torch.Tensor. with shape (batch_size, hidden_dim)
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"""
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def __init__(self, hidden_dim: int, dropout_percent: int):
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super(Block, self).__init__()
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self.ff = FeedForward(hidden_dim)
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self.dropout = nn.Dropout(p=dropout_percent)
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self.ln = nn.LayerNorm(hidden_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.ff(self.ln(x))
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x = self.dropout(x)
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return x
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class FeedForward(nn.Module):
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"""
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A simple fully-connected feedforward neural network block.
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:param hidden_dim: The number of hidden units in the block.
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:return: torch.Tensor. with shape (batch_size, hidden_dim)
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"""
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def __init__(self, hidden_dim: int):
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super(FeedForward, self).__init__()
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self.net = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.net(x)
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