Network.py
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	# -- 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
	"""

	# %%
	# Librariies 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