Cats_and_Dogs/model.py at master · EmmanuelOwusu/Cats_and_Dogs · GitHub

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from torch.utils.data import Dataset
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
import numpy
from torch.utils.data import Dataset
import glob
from PIL import Image
from torch.utils.data import DataLoader


class CNN(nn.Module):    ## 2 Layer Convolution
    def __init__(self, input_size, n_feature, output_size):
        super(CNN, self).__init__()
        self.n_feature = n_feature
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_feature, kernel_size=5)
        self.conv2 = nn.Conv2d(n_feature, n_feature, kernel_size=5)
        self.fc1 = nn.Linear(n_feature*68*68, 50)
        self.fc2 = nn.Linear(50, 2)

    def forward(self, x, verbose=False):
        x = self.conv1(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.view(-1, self.n_feature*68*68)  ### Flattening
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.log_softmax(x, dim=1)
        return x


class CNN4(nn.Module):    ## 4 Layer Convolution
    def __init__(self, input_size, n_feature, output_size):
        super(CNN4, self).__init__()
        self.n_feature = n_feature
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=n_feature, kernel_size=5)
        self.conv2 = nn.Conv2d(n_feature, n_feature, kernel_size=5)
        self.conv3 = nn.Conv2d(n_feature, out_channels=n_feature, kernel_size=5)
        self.conv4 = nn.Conv2d(n_feature, n_feature, kernel_size=5)
        self.fc1 = nn.Linear(n_feature*14*14, 50)
        self.fc2 = nn.Linear(50, 2)

    def forward(self, x, verbose=False):
        x = self.conv1(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv2(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv3(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = self.conv4(x)
        x = F.relu(x)
        x = F.max_pool2d(x, kernel_size=2)
        x = x.view(-1, self.n_feature*14*14)  ### Flattening
        x = self.fc1(x)
        x = F.relu(x)
        x = self.fc2(x)
        x = F.log_softmax(x, dim=1)
        return x