revised some code examples

ML/Pytorch/Basics/pytorch_simple_CNN.py

@@ -1,34 +1,33 @@
 """
-Example code of a simple CNN network training on MNIST dataset.
-The code is intended to show how to create a CNN network as well
-as how to initialize loss, optimizer, etc. in a simple way to get
-training to work with function that checks accuracy as well.
+A simple walkthrough of how to code a convolutional neural network (CNN)
+using the PyTorch library. For demonstration we train it on the very
+common MNIST dataset of handwritten digits. In this code we go through
+how to create the network as well as initialize a loss function, optimizer,
+check accuracy and more.
 
-Video explanation: https://youtu.be/wnK3uWv_WkU
-Got any questions leave a comment on youtube :)
-
-Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
-*    2020-04-08 Initial coding
+Programmed by Aladdin Persson
+* 2020-04-08: Initial coding
+* 2021-03-24: More detailed comments and small revision of the code
 
 """
 
 # Imports
 import torch
-import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
-import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.
-import torch.nn.functional as F  # All functions that don't have any parameters
-from torch.utils.data import (
-    DataLoader,
-)  # Gives easier dataset managment and creates mini batches
-import torchvision.datasets as datasets  # Has standard datasets we can import in a nice way
-import torchvision.transforms as transforms  # Transformations we can perform on our dataset
+import torchvision # torch package for vision related things
+import torch.nn.functional as F  # Parameterless functions, like (some) activation functions
+import torchvision.datasets as datasets  # Standard datasets
+import torchvision.transforms as transforms  # Transformations we can perform on our dataset for augmentation
+from torch import optim  # For optimizers like SGD, Adam, etc.
+from torch import nn  # All neural network modules
+from torch.utils.data import DataLoader  # Gives easier dataset managment by creating mini batches etc.
+from tqdm import tqdm  # For nice progress bar!
 
 # Simple CNN
 class CNN(nn.Module):
     def __init__(self, in_channels=1, num_classes=10):
         super(CNN, self).__init__()
         self.conv1 = nn.Conv2d(
-            in_channels=1,
+            in_channels=in_channels,
             out_channels=8,
             kernel_size=(3, 3),
             stride=(1, 1),
@@ -51,7 +50,6 @@ class CNN(nn.Module):
         x = self.pool(x)
         x = x.reshape(x.shape[0], -1)
         x = self.fc1(x)
-
         return x
 
 
@@ -59,24 +57,20 @@ class CNN(nn.Module):
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Hyperparameters
-in_channel = 1
+in_channels = 1
 num_classes = 10
 learning_rate = 0.001
 batch_size = 64
-num_epochs = 5
+num_epochs = 3
 
 # Load Data
-train_dataset = datasets.MNIST(
-    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
-)
+train_dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
+test_dataset = datasets.MNIST(root="dataset/", train=False, transform=transforms.ToTensor(), download=True)
 train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
-test_dataset = datasets.MNIST(
-    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
-)
 test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
 
 # Initialize network
-model = CNN().to(device)
+model = CNN(in_channels=in_channels, num_classes=num_classes).to(device)
 
 # Loss and optimizer
 criterion = nn.CrossEntropyLoss()
@@ -84,7 +78,7 @@ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
 
 # Train Network
 for epoch in range(num_epochs):
-    for batch_idx, (data, targets) in enumerate(train_loader):
+    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
         # Get data to cuda if possible
         data = data.to(device=device)
         targets = targets.to(device=device)
@@ -101,14 +95,7 @@ for epoch in range(num_epochs):
         optimizer.step()
 
 # Check accuracy on training & test to see how good our model
-
-
 def check_accuracy(loader, model):
-    if loader.dataset.train:
-        print("Checking accuracy on training data")
-    else:
-        print("Checking accuracy on test data")
-
     num_correct = 0
     num_samples = 0
     model.eval()
@@ -123,12 +110,10 @@ def check_accuracy(loader, model):
             num_correct += (predictions == y).sum()
             num_samples += predictions.size(0)
 
-        print(
-            f"Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}"
-        )
 
     model.train()
+    return num_correct/num_samples
 
 
-check_accuracy(train_loader, model)
-check_accuracy(test_loader, model)
+print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
+print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")