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import torch
from torch import nn
import torch.nn.functional as F
class NeuralNetwork(nn.Module):
def __init__(self):
super().__init__()
n_filters = 64
self.conv_1 = nn.Conv1d( 1, n_filters, 8, stride=1, padding='same')
self.conv_2 = nn.Conv1d(n_filters, n_filters, 5, stride=1, padding='same')
self.conv_3 = nn.Conv1d(n_filters, n_filters, 3, stride=1, padding='same')
self.conv_4 = nn.Conv1d( 1, n_filters, 1, stride=1, padding='same') # Expanding for addition
self.conv_5 = nn.Conv1d( n_filters, n_filters*2, 8, stride=1, padding='same')
self.conv_6 = nn.Conv1d(n_filters*2, n_filters*2, 5, stride=1, padding='same')
self.conv_7 = nn.Conv1d(n_filters*2, n_filters*2, 3, stride=1, padding='same')
self.conv_8 = nn.Conv1d( n_filters, n_filters*2, 1, stride=1, padding='same')
self.conv_9 = nn.Conv1d(n_filters*2, n_filters*2, 8, stride=1, padding='same')
self.conv_10 = nn.Conv1d(n_filters*2, n_filters*2, 5, stride=1, padding='same')
self.conv_11 = nn.Conv1d(n_filters*2, n_filters*2, 3, stride=1, padding='same')
self.conv_12 = nn.Conv1d(n_filters*2, n_filters*2, 1, stride=1, padding='same')
self.classifier = nn.Linear(128, 5)
self.log_softmax = nn.LogSoftmax(dim=1)
def forward(self, x):
x = x.float()
# Block 1
a = self.conv_1(x)
a = F.relu(a)
b = self.conv_2(a)
b = F.relu(b)
c = self.conv_3(b)
shortcut = self.conv_4(x)
output_1 = torch.add(c, shortcut)
output_1 = F.relu(output_1)
#Block 2
a = self.conv_5(output_1)
a = F.relu(a)
b = self.conv_6(a)
b = F.relu(b)
c = self.conv_7(b)
shortcut = self.conv_8(output_1)
output_2 = torch.add(c, shortcut)
output_2 = F.relu(output_2)
#Block 3
a = self.conv_9(output_2)
a = F.relu(a)
b = self.conv_10(a)
b = F.relu(b)
c = self.conv_11(b)
shortcut = self.conv_12(output_2)
output_3 = torch.add(c, shortcut)
output_3 = F.relu(output_3)
res = self.classifier(output_3.mean((2,)))
logits = self.log_softmax(res)
return logits
if __name__ == '__main__':
model = NeuralNetwork()
print(model)
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