organize pytorch files

freqtrade/freqai/torch/PyTorchMLPModel.py

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+import logging
+
+import torch
+import torch.nn as nn
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchMLPModel(nn.Module):
+    """
+    A multi-layer perceptron (MLP) model implemented using PyTorch.
+
+    This class mainly serves as a simple example for the integration of PyTorch model's
+    to freqai. It is not optimized at all and should not be used for production purposes.
+
+    :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)
+    """
+
+    def __init__(self, input_dim: int, output_dim: int, **kwargs):
+        super().__init__()
+        hidden_dim: int = kwargs.get("hidden_dim", 256)
+        dropout_percent: int = kwargs.get("dropout_percent", 0.2)
+        n_layer: int = kwargs.get("n_layer", 1)
+        self.input_layer = nn.Linear(input_dim, hidden_dim)
+        self.blocks = nn.Sequential(*[Block(hidden_dim, dropout_percent) for _ in range(n_layer)])
+        self.output_layer = nn.Linear(hidden_dim, output_dim)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(p=dropout_percent)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.relu(self.input_layer(x))
+        x = self.dropout(x)
+        x = self.blocks(x)
+        x = self.output_layer(x)
+        return x
+
+
+class Block(nn.Module):
+    """
+    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.
+
+    :returns: torch.Tensor. with shape (batch_size, hidden_dim)
+    """
+
+    def __init__(self, hidden_dim: int, dropout_percent: int):
+        super().__init__()
+        self.ff = FeedForward(hidden_dim)
+        self.dropout = nn.Dropout(p=dropout_percent)
+        self.ln = nn.LayerNorm(hidden_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ff(self.ln(x))
+        x = self.dropout(x)
+        return x
+
+
+class FeedForward(nn.Module):
+    """
+    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)
+    """
+
+    def __init__(self, hidden_dim: int):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.net(x)