cnn_main.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data.sampler import SubsetRandomSampler

import torchvision
import torchvision.transforms as transforms
from torchvision import datasets

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
#from resources.plotcm import plot_confusion_matrix
from itertools import product
torch.set_grad_enabled(True)

class Network(nn.Module):
    def __init__(self):
        super(Network, self).__init__()
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=12, kernel_size=5)
        #dropout example
        #self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(in_features=12*4*4, out_features=120)
        self.fc2 = nn.Linear(in_features=120, out_features=60)
        self.out = nn.Linear(in_features=60, out_features=10)
        
    def forward(self, t):
        t = F.relu(self.conv1(t))
        t = F.max_pool2d(t, kernel_size=2, stride=2)

        t = F.relu(self.conv2(t))
        t = F.max_pool2d(t, kernel_size=2, stride=2)

        t = F.relu(self.fc1(t.reshape(-1, 12*4*4)))
        t = F.relu(self.fc2(t))
        t = self.out(t)
        return t

def get_num_correct(preds, labels):
    return preds.argmax(dim=1).eq(labels).sum().item()

transform = transforms.ToTensor()
train_set = datasets.FashionMNIST(
    root='./data/FashionMNIST'
    ,train=True
    ,download=True
    ,transform = transform)

test_set = datasets.FashionMNIST(
    root='./data/FashionMNIST'
    ,train=False
    ,download=True
    ,transform = transform)
train_sampler = SubsetRandomSampler(list(range(48000)))
valid_sampler = SubsetRandomSampler(list(range(12000)))

parameters = dict(
    lr = [0.001], #0.01, 0.001
    batch_size = [20] #10, 100, 1000
)

param_values = [v for v in parameters.values()]
model = Network()

for lr, batch_size in product(*param_values):
    train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, sampler=train_sampler)
    valid_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, sampler=valid_sampler)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    for epoch in range(10):
        total_loss, total_correct, valid_loss, valid_correct = 0, 0, 0, 0

        for batch in train_loader: #Get Batch
            images, labels = batch

            preds = model(images) #Pass Batch
            loss = F.cross_entropy(preds, labels) #Calculating Loss

            optimizer.zero_grad()
            loss.backward() #Calculating the Gradients
            optimizer.step() #Updating the weights

            total_loss += loss.item()
            total_correct += get_num_correct(preds, labels)
        
        for batch in valid_loader:
            images, labels = batch

            preds = model(images)
            loss = F.cross_entropy(preds, labels)

            valid_loss += loss.item()
            valid_correct += get_num_correct(preds, labels)

        print("epoch:", epoch, "train_correct:", total_correct, "train_loss:", total_loss,
                'valid_correct', valid_correct, 'valid_loss:', valid_loss)

    print('Lr: ', lr, 'batch size:', batch_size)

all_preds = []
targets = []
test_loader = torch.utils.data.DataLoader(test_set, batch_size=1000)
for batch in test_loader:
    images, labels = batch

    preds = model(images)
    loss = F.cross_entropy(preds, labels)

    all_preds.append(torch.max(preds, dim=1).indices)
    targets.append(labels.data)
all_preds = torch.cat(all_preds)
targets = torch.cat(targets)
cm = confusion_matrix(targets, all_preds)
accuracy = accuracy_score(targets, all_preds)

print("Confusion_matrix: \n", cm)
print("Overall accuracy on test set: ", accuracy)

#save the model
PATH = 'yourpath/model1.pth'
torch.save(model, PATH)

#Load model
#model = TheModelClass(*args, **kwargs)
#model.load_state_dict(torch.load(PATH))
#model.eval()