'''

    Please Implement your model here.

'''
from torch import nn
import torch.nn.functional as F

class Network(nn.Module):
    def __init__(self):
        super().__init__()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding='same')
        self.conv2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, stride=1, padding='same')
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding='same')
        self.conv4 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding='same')
        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, 128)
        self.fc3 = nn.Linear(128, 10)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.3)

    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool(x)
        x = self.dropout(x)
        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool(x)
        x = self.dropout(x)
        x = x.reshape((x.shape[0], -1))
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.relu(self.fc2(x))
        x = self.dropout(x)
        x = self.fc3(x)
        return x