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NN.py
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NN.py
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import torch
import torch.nn as nn
import warnings
warnings.filterwarnings("ignore")
torch.manual_seed(42)
class NN1(nn.Module):
"""
Neural network configuration.
The neural network consists of 6 Conv. layers in two branches (6x2 in total)
one branch takes the scaled data as input and the other takes the non-scaled data as an input.
The ouputs of each branch are concatenated at the end and connected to two output layers:
1- layer for predicting the RCs
2- layer for predicting the binary elements in the circuit
(refer to paper)
"""
def __init__(self,deg_out=4,bin_out=6):
super().__init__()
self.main_activation = nn.Tanh()
self.main_activation2 = nn.Tanh()
self.relu = nn.ReLU()
self.relu6 = nn.ReLU6()
self.tanh = nn.Tanh()
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.dropout = nn.Dropout(.15)
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=300,
kernel_size=(1, 7),
stride=1,
padding=0,
),
self.main_activation2,
nn.AvgPool2d(kernel_size=(1, 2)),
)
self.conv2 = nn.Sequential(
nn.Conv2d(300, 250, (1,2), 1, 0),
self.main_activation2,
nn.MaxPool2d((1,2)),
)
self.conv3 = nn.Sequential(
nn.Conv2d(250, 200, (1, 2), 1, 0),
self.main_activation2,
nn.MaxPool2d((1, 2)),
)
self.conv4 = nn.Sequential(
nn.Conv2d(200, 100, (1, 3), 1, 0),
self.main_activation2,
nn.MaxPool2d((1, 3)),
)
self.conv5 = nn.Sequential(
nn.Conv2d(100, 30, (1, 2), 1, 0),
self.main_activation2,
nn.MaxPool2d((1, 2)),
)
self.conv6 = nn.Sequential(
nn.Conv2d(30, 10, (1, 2), 1, 0),
self.main_activation2,
nn.MaxPool2d((1, 2)),
)
self.conv21 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=200,
kernel_size=(1, 3),
stride=1,
padding=0,
),
self.main_activation,
nn.MaxPool2d(kernel_size=(1, 2)),
)
self.conv22 = nn.Sequential(
nn.Conv2d(200, 140, (1, 2), 1, 0),
self.main_activation,
nn.MaxPool2d((1, 2)),
)
self.conv23 = nn.Sequential(
nn.Conv2d(140, 100, (1, 2), 1, 0),
self.main_activation,
nn.MaxPool2d((1, 2)),
)
self.conv24 = nn.Sequential(
nn.Conv2d(100, 70, (1, 3), 1, 0),
self.main_activation,
nn.MaxPool2d((1, 3)),
)
self.conv25 = nn.Sequential(
nn.Conv2d(70, 30, (1, 2), 1, 0),
self.main_activation,
nn.MaxPool2d((1, 2)),
)
self.conv26 = nn.Sequential(
nn.Conv2d(30, 6, (1, 2), 1, 0),
self.main_activation,
nn.MaxPool2d((1, 2)),
)
self.out = nn.Linear(32, deg_out-1)
self.pre_out_b = nn.Linear(8,8)
self.out_b = nn.Linear(32,bin_out)
def forward(self, x):
self.test=torch.clone(x)
x1 = x[:,:,:2,:]
x_p = self.conv21(x1)
x_p = self.conv22(x_p)
x_p = self.conv23(x_p)
x_p = self.conv24(x_p)
x_p = self.conv25(x_p)
x_p = self.conv26(x_p)
x_p = x_p.view(x_p.size(0), -1)
x2 = x[:,:,2:,:]
x = self.conv1(x2)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = x.view(x.size(0), -1)
x = torch.cat((x, x_p), 1)
self.x = torch.clone(x)
self.output_b = self.sigmoid(self.out_b(x))
output = self.out(x)
self.degree = self.softmax(output)
self.deg_decimal = torch.argmax(self.degree,axis=1)+2
return self.degree,self.deg_decimal,self.output_b
class NN2(nn.Module):
def __init__(self,deg_out=4,bin_out=6):
super().__init__()
self.main_activation = nn.Tanh()
self.main_activation2 = nn.ReLU()
self.relu = nn.ReLU()
self.relu6 = nn.ReLU6()
self.tanh = nn.Tanh()
self.sigmoid = nn.Sigmoid()
self.softmax = nn.Softmax()
self.dropout = nn.Dropout(.2)
self.conv1 = nn.Sequential(
nn.Conv1d(
in_channels=2,
out_channels=300,
kernel_size=(7),
stride=1,
padding=0,
),
self.main_activation2,
nn.AvgPool1d(kernel_size=(2)),
)
self.conv2 = nn.Sequential(
nn.Conv1d(300, 250, (2), 1, 0),
self.main_activation2,
nn.MaxPool1d((2)),
)
self.conv3 = nn.Sequential(
nn.Conv1d(250, 200, (2), 1, 0),
self.main_activation2,
nn.MaxPool1d((2)),
)
self.conv4 = nn.Sequential(
nn.Conv1d(200, 100, (3), 1, 0),
self.main_activation2,
nn.MaxPool1d((2)),
)
self.conv5 = nn.Sequential(
nn.Conv1d(100, 30, (2), 1, 0),
self.main_activation2,
nn.MaxPool1d((2)),
)
self.conv6 = nn.Sequential(
nn.Conv1d(30, 15, (2), 1, 0),
self.main_activation2,
nn.MaxPool1d((2)),
)
self.conv21 = nn.Sequential(
nn.Conv1d(
in_channels=2,
out_channels=200,
kernel_size=(3),
stride=1,
padding=0,
),
self.main_activation,
nn.MaxPool1d(kernel_size=(2)),
)
self.conv22 = nn.Sequential(
nn.Conv1d(200, 140, (2), 1, 0),
self.main_activation,
nn.MaxPool1d((2)),
)
self.conv23 = nn.Sequential(
nn.Conv1d(140, 100, (2), 1, 0),
self.main_activation,
nn.MaxPool1d((2)),
)
self.conv24 = nn.Sequential(
nn.Conv1d(100, 70, (3), 1, 0),
self.main_activation,
nn.MaxPool1d((3)),
)
self.conv25 = nn.Sequential(
nn.Conv1d(70, 30, (2), 1, 0),
self.main_activation,
nn.MaxPool1d((2)),
)
self.conv26 = nn.Sequential(
nn.Conv1d(30, 10, (2), 1, 0),
self.main_activation,
nn.MaxPool1d((2)),
)
self.out = nn.Linear(25, deg_out-1)
self.pre_out_b = nn.Linear(8,8)
self.out_b = nn.Linear(25,bin_out)
def forward(self, x):
self.test=torch.clone(x)
x1 = x[:,:2,:]
x_p = self.conv21(x1)
x_p = self.conv22(x_p)
x_p = self.conv23(x_p)
x_p = self.conv24(x_p)
x_p = self.conv25(x_p)
x_p = self.conv26(x_p)
x_p = x_p.view(x_p.size(0), -1)
x2 = x[:,2:,:]
x = self.conv1(x2)
x = self.dropout(x)
x = self.conv2(x)
x = self.dropout(x)
x = self.conv3(x)
x = self.dropout(x)
x = self.conv4(x)
x = self.conv5(x)
x = self.conv6(x)
x = x.view(x.size(0), -1)
x = torch.cat((x, x_p), 1)
self.x = torch.clone(x)
self.output_b = self.sigmoid(self.out_b(x))
output = self.out(x)
self.degree = self.softmax(output)
self.deg_decimal = torch.argmax(self.degree,axis=1)+2
return self.degree, self.deg_decimal, self.output_b