neural_network.py

import torch
import torchvision
import torch.utils.data
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
from torchvision import transforms

class NeuralNetwork(torch.nn.Module):
    def __init__(self, n_input = 1, n_output = 35, n_channel = 32):
        super(NeuralNetwork, self).__init__()
        self.conv1 = nn.Conv1d(n_input, n_channel, kernel_size=80, stride=16)
        self.bn1 = nn.BatchNorm1d(n_channel)
        self.pool1 = nn.MaxPool1d(4)
        self.conv2 = nn.Conv1d(n_channel, n_channel, kernel_size=3)
        self.bn2 = nn.BatchNorm1d(n_channel)
        self.pool2 = nn.MaxPool1d(4)
        self.conv3 = nn.Conv1d(n_channel, 2 * n_channel, kernel_size=3)
        self.bn3 = nn.BatchNorm1d(2 * n_channel)
        self.pool3 = nn.MaxPool1d(4)
        self.conv4 = nn.Conv1d(2 * n_channel, 2 * n_channel, kernel_size=3)
        self.bn4 = nn.BatchNorm1d(2 * n_channel)
        self.pool4 = nn.MaxPool1d(4)
        self.fc1 = nn.Linear(2 * n_channel, n_output)

    def forward(self, inpout):
        x = self.conv1(inpout)
        x = F.relu(self.bn1(x))
        x = self.pool1(x)
        x = self.conv2(x)
        x = F.relu(self.bn2(x))
        x = self.pool2(x)
        x = self.conv3(x)
        x = F.relu(self.bn3(x))
        x = self.pool3(x)
        x = self.conv4(x)
        x = F.relu(self.bn4(x))
        x = self.pool4(x)
        x = F.avg_pool1d(x, x.shape[-1])
        x = x.permute(0, 2, 1)
        x = self.fc1(x)
        x = F.log_softmax(x, dim=2)
        return x

"""
self.conv1 = torch.nn.Conv2d()
self.maxpool1 = torch.nn.MaxPool2d()
self.conv2 = torch.nn.Conv2d()
self.maxpool2 = torch.nn.MaxPool2d()
"""