import torch
import torch.nn as nn
import torch.optim as optim

class CNNModel(nn.Module):

    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, padding=2)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.relu3 = nn.ReLU()
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv4 = nn.Conv2d(256, 384, kernel_size=5, padding=1)
        self.relu4 = nn.ReLU()
        self.conv5 = nn.Conv2d(384, 256, kernel_size=1, padding=0)
        self.relu5 = nn.ReLU()
        self.avgpool = nn.AdaptiveAvgPool2d((3, 3))
        self.flatten = nn.Flatten()
        self.dropout1 = nn.Dropout(p=0.1)
        self.fc1 = nn.Linear(256 * 3 * 3, 1024)
        self.relu6 = nn.ReLU()
        self.dropout2 = nn.Dropout(p=0.1)
        self.fc2 = nn.Linear(1024, 512)
        self.relu7 = nn.ReLU()
        self.fc3 = nn.Linear(512, 200)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        x = self.pool1(self.relu1(self.conv1(x)))
        x = self.pool2(self.relu2(self.conv2(x)))
        x = self.pool3(self.relu3(self.conv3(x)))
        x = self.relu4(self.conv4(x))
        x = self.relu5(self.conv5(x))
        x = self.avgpool(x)
        x = self.flatten(x)
        x = self.dropout1(x)
        x = self.relu6(self.fc1(x))
        x = self.dropout2(x)
        x = self.relu7(self.fc2(x))
        x = self.fc3(x)
        x = self.softmax(x)
        return x
        
model = CNNModel()
loss_fn = nn.NLLLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0003)