| | from transformers import AutoConfig, AutoModel |
| | from transformers import PreTrainedModel, PretrainedConfig |
| | import torch.nn as nn |
| | import torch |
| |
|
| | class ArchitectureConfig(PretrainedConfig): |
| | model_type = "architecture" |
| |
|
| | def __init__(self, **kwargs): |
| | super().__init__(**kwargs) |
| | self.input_size = kwargs.get("input_size", 9) |
| | self.hidden_size_1 = kwargs.get("hidden_size_1", 9) |
| | self.hidden_size_2 = kwargs.get("hidden_size_2", 9) |
| | self.hidden_size_3 = kwargs.get("hidden_size_3", 9) |
| | self.hidden_size_4 = kwargs.get("hidden_size_4", 9) |
| | self.hidden_size_5 = kwargs.get("hidden_size_5", 9) |
| | self.hidden_size_6 = kwargs.get("hidden_size_6", 9) |
| | self.hidden_size_7 = kwargs.get("hidden_size_7", 9) |
| | self.output_size = kwargs.get("output_size", 9) |
| |
|
| | class Architecture(PreTrainedModel): |
| | config_class = ArchitectureConfig |
| |
|
| | def __init__(self, config: ArchitectureConfig): |
| | super().__init__(config) |
| | self.input_size = config.input_size |
| | self.hidden_size_1 = config.hidden_size_1 |
| | self.hidden_size_2 = config.hidden_size_2 |
| | self.hidden_size_3 = config.hidden_size_3 |
| | self.hidden_size_4 = config.hidden_size_4 |
| | self.hidden_size_5 = config.hidden_size_5 |
| | self.hidden_size_6 = config.hidden_size_6 |
| | self.hidden_size_7 = config.hidden_size_7 |
| | self.output_size = config.output_size |
| |
|
| | self.fc1 = nn.Linear(self.input_size, self.hidden_size_1) |
| | self.fc2 = nn.Linear(self.hidden_size_1, self.hidden_size_2) |
| | self.fc3 = nn.Linear(self.hidden_size_2, self.hidden_size_3) |
| | self.fc4 = nn.Linear(self.hidden_size_3, self.hidden_size_4) |
| | self.fc5 = nn.Linear(self.hidden_size_4, self.hidden_size_5) |
| | self.fc6 = nn.Linear(self.hidden_size_5, self.hidden_size_6) |
| | self.fc7 = nn.Linear(self.hidden_size_6, self.hidden_size_7) |
| | self.fc8 = nn.Linear(self.hidden_size_7, self.output_size) |
| |
|
| | self.relu = nn.ReLU() |
| |
|
| | def forward(self, x): |
| | x1 = self.relu(self.fc1(x)) |
| | x2 = self.relu(self.fc2(x1)) |
| | x3 = self.relu(self.fc3(x2)) |
| | x4 = self.relu(self.fc4(x3)) |
| | x5 = self.relu(self.fc5(x4)) |
| | x6 = self.relu(self.fc6(x5)) |
| | x7 = self.relu(self.fc7(x6)) |
| | x8 = self.fc8(x7) |
| | return x8 |
| |
|
| | def inference(self, x): |
| | return self.forward(x) |
| |
|
| | |
| | def load_model(): |
| | AutoConfig.register("architecture", ArchitectureConfig) |
| | AutoModel.register(ArchitectureConfig, Architecture) |
| | config = ArchitectureConfig() |
| | model = Architecture(config) |
| | model.load_state_dict(torch.load('./model_weights.pth')) |
| | return model |
| |
|
| | load_model() |
| |
|
