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Fri, Jan 24, 00:31

Network.py
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# -*- coding: utf-8 -*-
"""
@author: srpv
contact: vigneashwara.solairajapandiyan@empa.ch, vigneashpandiyan@gmail.com
The codes in this following script will be used for the publication of the following work
"Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando 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
class Network(nn.Module):
"""
This class represents a neural network model for processing 1D input data.
Args:
emb_dim (int): The dimension of the output embedding. Default is 4.
Attributes:
conv (nn.Sequential): The convolutional layers of the network.
fc (nn.Sequential): The fully connected layers of the network.
"""
def __init__(self, emb_dim=4):
super(Network, self).__init__()
self.conv = nn.Sequential(
nn.Conv1d(in_channels=1, out_channels=4, kernel_size=8, stride=8),
nn.BatchNorm1d(4),
nn.PReLU(),
nn.Dropout(0.1),
nn.Conv1d(4, 8, kernel_size=8, stride=8),
nn.BatchNorm1d(8),
nn.PReLU(),
nn.Dropout(0.1),
nn.Conv1d(8, 16, kernel_size=8, stride=4),
nn.BatchNorm1d(16),
nn.PReLU(),
nn.Dropout(0.1),
nn.Conv1d(16, 32, kernel_size=8, stride=4),
nn.BatchNorm1d(32),
nn.PReLU(),
nn.Dropout(0.1),
)
self.fc = nn.Sequential(
nn.Linear(32*3, 64),
nn.PReLU(),
nn.Linear(64, emb_dim),
)
def forward(self, x):
"""
Forward pass of the network.
Args:
x (torch.Tensor): The input tensor of shape (batch_size, 1, sequence_length).
Returns:
torch.Tensor: The output tensor of shape (batch_size, emb_dim).
"""
x = self.conv(x)
x = x.view(-1, 32*3)
x = self.fc(x)
return x

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