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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
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