import torch
from torch import nn

class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=2, padding=1),
            nn.Dropout2d(p=0.3),

            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), 
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=2, padding=1),
            nn.Dropout2d(p=0.3),

            nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1),
            nn.Dropout2d(p=0.4),

            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, stride=2, padding=1),
        )
        self.flatten = nn.Flatten()
        self.fc = nn.Sequential(
            nn.Linear(256*4*4, 256), 
            nn.BatchNorm1d(256),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(256, 151),
        )

    def forward(self, x):
        out = self.conv(x)
        out = self.flatten(out)
        out = self.fc(out)
        
        return out