Homec4science
Diffusion LPBF Variable Time-Scales (master) Browse

R12010/Network.py
1233a3b1e6demaster

Last Change
Raw File

Network.py
View Options

# -*- coding: utf-8 -*-
"""
Created on Sat Feb 8 22:10:18 2020
---------------------------------------------------------------------
-- Author: Vigneashwara Pandiyan
---------------------------------------------------------------------
Network model
"""
#%%
from torch import nn, optim
from torch.nn import functional as F
from prettytable import PrettyTable
class CNN(nn.Module):
def __init__(self, n_features, n_classes, n_hidden=90, n_layers=1):
super().__init__()
self.conv1 = nn.Conv1d(in_channels=4, out_channels=8, kernel_size=16)
self.bn1 = nn.BatchNorm1d(8)
self.conv2 = nn.Conv1d(in_channels=8, out_channels=16, kernel_size=16)
self.bn2 = nn.BatchNorm1d(16)
self.conv3 = nn.Conv1d(in_channels=16, out_channels=32, kernel_size=16)
self.bn3 = nn.BatchNorm1d(32)
self.conv4 = nn.Conv1d(32, 64, kernel_size=16)
self.bn4 = nn.BatchNorm1d(64)
self.conv5 = nn.Conv1d(64, out_channels=n_features, kernel_size=16)
self.bn5 = nn.BatchNorm1d(n_features)
self.pool = nn.MaxPool1d(2)
self.dropout = nn.Dropout(0.025)
self.lstm = nn.LSTM(input_size=n_features,
hidden_size=n_hidden,
num_layers=n_layers,
batch_first=True,
dropout=0.2
)
self.classifier = nn.Linear(n_hidden, n_classes)
def forward(self, x):
# print(x.shape)
x = F.relu(self.bn1(self.conv1(x)))
x = self.dropout(x)
# print(x.shape)
x = self.pool(x)
# print(x.shape)
x = F.relu(self.bn2(self.conv2(x)))
x = self.dropout(x)
# print(x.shape)
x = self.pool(x)
# print(x.shape)
x = F.relu(self.bn3(self.conv3(x)))
x = self.dropout(x)
# print(x.shape)
x = self.pool(x)
# print(x.shape)
x = F.relu(self.bn4(self.conv4(x)))
x = self.dropout(x)
# print(x.shape)
x = self.pool(x)
# print(x.shape)
x = F.relu(self.bn5(self.conv5(x)))
# print(x.shape)
x = self.pool(x)
# print(x.shape)
# torch.Size([1, 4, 5000])--> torch.Size([1, 10, 141])
# torch.Size([1, 4, 10000])-->torch.Size([1, 10, 297])
x = x.permute(0, 2, 1)
# print(x.shape) #torch.Size([100, 5000, 1])
x,(_,hidden, ) = self.lstm(x)
out = hidden[-1]
return self.classifier(out)
#%%
def initialize_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.kaiming_uniform_(m.weight.data,nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias.data, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight.data, 1)
nn.init.constant_(m.bias.data, 0)
elif isinstance(m, nn.Linear):
nn.init.kaiming_uniform_(m.weight.data)
nn.init.constant_(m.bias.data, 0)
#%%
def get_lr(optimizer):
for param_group in optimizer.param_groups:
print('Learning rate =')
print(param_group['lr'])
return param_group['lr']
#%%
def count_parameters(model):
table = PrettyTable(["Modules", "Parameters"])
total_params = 0
for name, parameter in model.named_parameters():
if not parameter.requires_grad: continue
param = parameter.numel()
table.add_row([name, param])
total_params+=param
print(table)
print(f"Total Trainable Params: {total_params}")
return total_params
c4science · Help