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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)
# Loading the model from saved weights
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()
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