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models.py
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models.py
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from torch import nn
import torch
class ANN(nn.Module):
def __init__(self):
super().__init__()
self.linear1 = nn.Linear(100, 1000)
self.linear2 = nn.Linear(1000, 500)
self.linear3 = nn.Linear(500, 100)
self.linear4 = nn.Linear(100, 20)
self.linear5 = nn.Linear(20, 1)
self.dropout1 = nn.Dropout(0.25)
self.Hl_actication = nn.LeakyReLU()
self.output_actication = nn.Sigmoid()
# * L1 Regularization
def compute_l1_loss(self, w):
return torch.abs(w).sum()
# * L2 Regularization
def compute_l2_loss(self, w):
return torch.pow(w, 2).sum()
def forward(self, x):
out = self.dropout1(self.Hl_actication(self.linear1(x)))
out = self.dropout1(self.Hl_actication(self.linear2(out)))
out = self.dropout1(self.Hl_actication(self.linear3(out)))
out = self.dropout1(self.Hl_actication(self.linear4(out)))
out = self.Hl_actication(self.linear5(out))
out = self.output_actication(out)
return out
class Linear_block(nn.Module):
def __init__(
self, in_channel, out_channel, activation=nn.LeakyReLU(), dorp_ratio=0.25
):
super().__init__()
self.Linear_block = nn.Sequential(
nn.Linear(in_channel, out_channel),
activation,
nn.Dropout(dorp_ratio),
)
def forward(self, x):
out = self.Linear_block(x)
return out
class CNN1D(nn.Module):
def __init__(self):
super(CNN1D, self).__init__()
# TODO : #! Why the kernel size must be one? ->
self.conv1 = nn.Conv1d(
in_channels=100, out_channels=64, kernel_size=1, stride=1, padding=1
)
self.conv2 = nn.Conv1d(
in_channels=64, out_channels=32, kernel_size=1, stride=1, padding=1
)
self.conv3 = nn.Conv1d(
in_channels=32, out_channels=16, kernel_size=1, stride=1, padding=1
)
self.pool = nn.MaxPool1d(kernel_size=1, stride=1)
self.mlp = nn.Sequential(
Linear_block(112, 50),
# Linear_block(100, 50),
Linear_block(50, 1),
# Linear_block(20, 1),
)
self.dropout1d = nn.Dropout(0.15)
self.cnn_activation = nn.LeakyReLU()
self.output_actication = nn.Sigmoid()
# * L1 Regularization
def compute_l1_loss(self, w):
return torch.abs(w).sum()
# * L2 Regularization
def compute_l2_loss(self, w):
return torch.pow(w, 2).sum()
def forward(self, x):
# * [128, 100] -> [128, 100, 1] [batch, channel(x)/dimension(x,y), length/depth(c)]
x = x.unsqueeze(-1)
out = self.cnn_activation(self.conv1(x))
out = self.pool(out)
out = self.cnn_activation(self.conv2(out))
out = self.pool(out)
out = self.cnn_activation(self.conv3(out))
out = self.pool(out)
out = nn.Flatten()(out)
out = self.mlp(out)
out = self.output_actication(out)
return out
# * here > https://cnvrg.io/pytorch-lstm/
class BILSTM(nn.Module):
def __init__(
self, input_size, hidden_size, num_layers, num_classes, bidirection=True
):
super(BILSTM, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirection = bidirection
self.lstm = nn.LSTM(
input_size,
hidden_size,
num_layers,
batch_first=True,
bidirectional=self.bidirection,
)
self.FC = nn.Linear(hidden_size * 2, num_classes)
self.FC_activation = nn.LeakyReLU()
self.output_actication = nn.Sigmoid()
def forward(self, x):
if x.dim() == 2:
x = x.unsqueeze(1)
if x.size(1) == 1:
pass
else:
raise ValueError(
"Input dimension must be [batch_size, seq_len, input_size]"
)
if self.bidirection:
state_size = self.num_layers * 2
else:
state_size = self.num_layers
hidden_state_0 = torch.zeros(state_size, x.size(0), self.hidden_size).to(
x.device
)
cell_state_0 = torch.zeros(state_size, x.size(0), self.hidden_size).to(x.device)
out, _ = self.lstm(
x, (hidden_state_0, cell_state_0)
) # _ = (hidden_state, cell_state)
#TODO: #! why do we take out[:, -1, :]?
out = self.FC(out[:, -1, :])
out = self.FC_activation(out)
out = self.output_actication(out)
return out