stable/freqtrade/freqai/prediction_models/PyTorchMLPModel.py

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import logging
import torch
import torch.nn as nn
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from torch import Tensor
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logger = logging.getLogger(__name__)
class PyTorchMLPModel(nn.Module):
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
super(PyTorchMLPModel, self).__init__()
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self.input_layer = nn.Linear(input_dim, hidden_dim)
self.hidden_layer = nn.Linear(hidden_dim, hidden_dim)
self.output_layer = nn.Linear(hidden_dim, output_dim)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.2)
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def forward(self, x: Tensor) -> Tensor:
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x = self.relu(self.input_layer(x))
x = self.dropout(x)
x = self.relu(self.hidden_layer(x))
x = self.dropout(x)
logits = self.output_layer(x)
return logits