freqtrade_origin/freqtrade/freqai/prediction_models/PyTorchMLPModel.py
2023-03-12 14:31:08 +02:00

54 lines
1.6 KiB
Python

import logging
import torch.nn as nn
from torch import Tensor
logger = logging.getLogger(__name__)
class PyTorchMLPModel(nn.Module):
def __init__(self, input_dim: int, output_dim: int, **kwargs):
super(PyTorchMLPModel, self).__init__()
hidden_dim: int = kwargs.get("hidden_dim", 1024)
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: Tensor) -> Tensor:
x = self.relu(self.input_layer(x))
x = self.dropout(x)
x = self.relu(self.blocks(x))
logits = self.output_layer(x)
return logits
class Block(nn.Module):
def __init__(self, hidden_dim: int, dropout_percent: int):
super(Block, self).__init__()
self.ff = FeedForward(hidden_dim)
self.dropout = nn.Dropout(p=dropout_percent)
self.ln = nn.LayerNorm(hidden_dim)
def forward(self, x):
x = self.dropout(self.ff(x))
x = self.ln(x)
return x
class FeedForward(nn.Module):
def __init__(self, hidden_dim: int):
super(FeedForward, self).__init__()
self.net = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
)
def forward(self, x):
return self.net(x)