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model_architecture.py
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model_architecture.py
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import torch
from torch import nn
from torch.nn import Module
from torch.cuda import init
from torch.utils.data import Dataset
class RNNDataset(Dataset):
def __init__(self, x, y, window_size, offset):
self.x = x
self.y = y
self.window = window_size
self.offset = offset
def __getitem__(self, index):
# if index <= len()
_x = self.x[index:index+self.window]
_y = self.y[index + self.window + self.offset] # 1 y for every (window) x
return _x, _y
def __len__(self):
return len(self.x) - self.window - self.offset
class RNNModel(Module):
def __init__(self, batch_size, sequence_size, hidden_size, rnn_layers) -> None:
super().__init__()
self.h_0 = torch.zeros(rnn_layers, batch_size, 9)
# Model layers
self.rnn_input = nn.LSTM(input_size=9, hidden_size=hidden_size, num_layers=rnn_layers,
dropout=0.2, batch_first=True)
# out --> (direction * num_layers, batch, hidden) (hidden state) SQUEEZE
self.linear1 = nn.Linear(hidden_size, 128)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(p=0.2)
self.linear2 = nn.Linear(128, 64)
self.linear3 = nn.Linear(64, 16)
self.fc_out = nn.Linear(16, 1)
def forward(self, x):
out, (hidden, cell) = self.rnn_input(x)
x = self.linear1(hidden)
x = self.relu(x)
x = self.linear2(x)
x = self.relu(x)
x = self.linear3(x)
x = self.relu(x)
return self.fc_out(x)
class NNDataset(Dataset):
def __init__(self, x, y) -> None:
super().__init__()
self.x = x
self.y = y
def __getitem__(self, index):
return self.x[index], self.y[index]
def __len__(self):
return len(self.x)
class NNModel(Module):
def __init__(self, features_in) -> None:
super().__init__()
print('FEATS', features_in)
self.linear1 = nn.Linear(9, 16)
self.linear2 = nn.Linear(16, 8)
self.dense_out = nn.Linear(8, 1)
self.relu = nn.ReLU()
def forward(self, x):
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
x = self.relu(x)
x = self.dense_out(x)
return x
class ConvDataset(Dataset):
def __init__(self, X, y) -> None:
super().__init__()
self.X = X
self.y = y
def __getitem__(self, index):
_x = self.x[index]
class ConvModel(Module):
# Assuming channels = features,
def __init__(self, in_channels, out_channels, kernel_size):
super().__init__()
self.conv = torch.nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size)
self.linear1 = torch.nn.Linear(out_channels, 8)
self.relu = torch.nn.ReLU()
self.fc_out = torch.nn.Linear(8, 1)
def forward(self, x):
x = self.conv(x)
x = self.relu(x)
x = self.linear1(x)
x = self.fc_out(x)
return x