Network.py
d902e79b9eaemaster

Network.py
View Options

			# -- coding: utf-8 --
			"""
			Created on Tue Dec 26 07:14:15 2023

			@author: srpv
			contact: vigneashwara.solairajapandiyan@empa.ch
			contact: vigneashwara.pandiyan@tii.ae


			The codes in this following script will be used for the publication of the following work

			"Dynamics of in-situ alloying of Ti6Al4V-Fe by means of acoustic emission monitoring
			supported by operando synchrotron X-ray diffraction"

			@any reuse of this code should be authorized by the first owner, code author
			"""


			# %%

			# Libraries to import

			import matplotlib.pyplot as plt
			import numpy as np
			from prettytable import PrettyTable
			import torch.nn as nn
			import torch

			''' Network to be trained'''


			class Network(nn.Module):
			def __init__(self, emb_dim=4):
			super(Network, self).__init__()

			self.conv = nn.Sequential(
			nn.Conv1d(in_channels=1, out_channels=8, kernel_size=32, stride=5),
			nn.BatchNorm1d(8),
			nn.PReLU(),
			nn.Dropout(0.1),


			nn.Conv1d(8, 16, kernel_size=16, stride=3),
			nn.BatchNorm1d(16),
			nn.PReLU(),
			nn.Dropout(0.1),

			nn.Conv1d(16, 32, kernel_size=8, stride=3),
			nn.BatchNorm1d(32),
			nn.PReLU(),
			nn.Dropout(0.1),

			nn.Conv1d(32, 64, kernel_size=5, stride=3),
			nn.BatchNorm1d(64),
			nn.PReLU(),
			nn.Dropout(0.1),

			nn.Conv1d(64, 128, kernel_size=3, stride=2),
			nn.BatchNorm1d(128),
			nn.PReLU(),
			nn.Dropout(0.1),

			)

			self.fc = nn.Sequential(
			nn.Linear(128*17, 256),
			nn.PReLU(),
			nn.Linear(256, 64),
			nn.PReLU(),
			nn.Linear(64, emb_dim),

			)

			def forward(self, x):

			x = self.conv(x)
			x = x.view(-1, 128*17)
			x = self.fc(x)

			return x