add mlp documentation
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import logging
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import torch.nn as nn
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from torch import Tensor
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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.
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:param output_dim: The number of output classes.
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:param hidden_dim: The number of hidden units in each layer. Default: 256
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:param dropout_percent: The dropout rate for regularization. Default: 0.2
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:param n_layer: The number of layers in the MLP. Default: 1
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:returns: The output of the MLP, with shape (batch_size, output_dim)
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A neural network typically consists of input, output, and hidden layers, where the
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information flows from the input layer through the hidden layers to the output layer.
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In a feedforward neural network, also known as a multilayer perceptron (MLP), the
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information flows in one direction only. Each hidden layer contains multiple units
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or nodes that take input from the previous layer and produce output that goes to the
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next layer.
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The hidden_dim parameter in the FeedForward class refers to the number of units
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(or nodes) in the hidden layer. This parameter controls the complexity of the neural
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network and determines how many nonlinear relationships the network can represent.
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A higher value of hidden_dim allows the network to represent more complex functions
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but may also make the network more prone to overfitting, where the model memorizes
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the training data instead of learning general patterns.
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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", 1024)
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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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@ -19,7 +45,7 @@ class PyTorchMLPModel(nn.Module):
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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: Tensor) -> Tensor:
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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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@ -28,19 +54,35 @@ class PyTorchMLPModel(nn.Module):
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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) implemented using PyTorch.
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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):
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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 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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@ -48,5 +90,5 @@ class FeedForward(nn.Module):
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nn.ReLU(),
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)
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def forward(self, x):
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.net(x)
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