adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

improve mlp documentation

Browse Source
This commit is contained in:
Yinon Polak 2023-03-19 18:04:01 +02:00
parent 6f9a8a089c
commit 903a1dc3e5
1 changed files with 19 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

41
freqtrade/freqai/prediction_models/PyTorchMLPModel.py
View File

@ -10,28 +10,25 @@ class PyTorchMLPModel(nn.Module):
"""
A multi-layer perceptron (MLP) model implemented using PyTorch.
:param input_dim: The number of input features.
:param output_dim: The number of output classes.
:param hidden_dim: The number of hidden units in each layer. Default: 256
:param dropout_percent: The dropout rate for regularization. Default: 0.2
:param n_layer: The number of layers in the MLP. Default: 1
:param input_dim: The number of input features. This parameter specifies the number
of features in the input data that the MLP will use to make predictions.
:param output_dim: The number of output classes. This parameter specifies the number
of classes that the MLP will predict.
:param hidden_dim: The number of hidden units in each layer. This parameter controls
the complexity of the MLP and determines how many nonlinear relationships the MLP
can represent. Increasing the number of hidden units can increase the capacity of
the MLP to model complex patterns, but it also increases the risk of overfitting
the training data. Default: 256
:param dropout_percent: The dropout rate for regularization. This parameter specifies
the probability of dropping out a neuron during training to prevent overfitting.
The dropout rate should be tuned carefully to balance between underfitting and
overfitting. Default: 0.2
:param n_layer: The number of layers in the MLP. This parameter specifies the number
of layers in the MLP architecture. Adding more layers to the MLP can increase its
capacity to model complex patterns, but it also increases the risk of overfitting
the training data. Default: 1
:returns: The output of the MLP, with shape (batch_size, output_dim)
A neural network typically consists of input, output, and hidden layers, where the
information flows from the input layer through the hidden layers to the output layer.
In a feedforward neural network, also known as a multilayer perceptron (MLP), the
information flows in one direction only. Each hidden layer contains multiple units
or nodes that take input from the previous layer and produce output that goes to the
next layer.
The hidden_dim parameter in the FeedForward class refers to the number of units
(or nodes) in the hidden layer. This parameter controls the complexity of the neural
network and determines how many nonlinear relationships the network can represent.
A higher value of hidden_dim allows the network to represent more complex functions
but may also make the network more prone to overfitting, where the model memorizes
the training data instead of learning general patterns.
"""
def __init__(self, input_dim: int, output_dim: int, **kwargs):
@ -55,7 +52,7 @@ class PyTorchMLPModel(nn.Module):
class Block(nn.Module):
"""
A building block for a multi-layer perceptron (MLP) implemented using PyTorch.
A building block for a multi-layer perceptron (MLP).
:param hidden_dim: The number of hidden units in the feedforward network.
:param dropout_percent: The dropout rate for regularization.
@ -77,7 +74,7 @@ class Block(nn.Module):
class FeedForward(nn.Module):
"""
A fully-connected feedforward neural network block.
A simple fully-connected feedforward neural network block.
:param hidden_dim: The number of hidden units in the block.
:return: torch.Tensor. with shape (batch_size, hidden_dim)