A Convolutional Neural Network (CNN) is a kind of deep learning model widely used in computer vision. Computer vision is a branch of Artificial Intelligence (AI) that helps computers analyze and understand images or other visual content.

In machine learning, Artificial Neural Networks are powerful tools. They work well with different types of data, such as images, audio, and text. Depending on the task, specific types of neural networks are more suitable. For example, if we need to predict the next word in a sentence, we often use Recurrent Neural Networks (RNNs) or their advanced version, LSTMs. On the other hand, for tasks like image classification, CNNs are the go-to choice.

Neural Networks: Layers and How They Work

Neural networks are made up of three main types of layers, each with its own role:

1. Input Layer:
   This is where we provide data to the model. For example, in the case of an image, the number of neurons in the input layer equals the number of pixels in the image.
2. Hidden Layers:
   The input from the input layer moves into the hidden layers. A network can have many hidden layers, depending on the complexity of the task. Each hidden layer contains neurons, usually more than the number of features in the input.

The output of each hidden layer is calculated through a process:

Multiply the output of the previous layer by weights (learned during training).

Add biases (also learned during training).

Apply an activation function, which introduces non-linearity and allows the network to learn complex patterns.

1. Output Layer:
   The final layer takes the results from the hidden layers and applies a function like sigmoid or softmax. This converts the output into probabilities for each class.

When we feed data into the model, it goes through these layers in a process called feedforward. After the output is generated, we compare it to the correct answer using an error function (like cross-entropy or squared error). This tells us how far off the predictions are.

To improve the model, we use backpropagation. This involves calculating derivatives to adjust the weights and biases in the network, reducing the error and making the model better over time. This entire process is what allows neural networks to learn and improve.

Convolutional Neural Network (CNN)

A Convolutional Neural Network (CNN) is an advanced type of Artificial Neural Network (ANN) designed to handle grid-like data, such as images or videos. It is specifically built to identify patterns and extract important features from this kind of data.

CNN Architecture

A Convolutional Neural Network (CNN) is made up of several key layers that work together to analyze data, like images. These layers include:

1. Input Layer
2. Convolutional Layer
3. Activation Layer
4. Pooling Layer
5. Flattening Layer
6. Dense Layer
7. Output

How Convolutional Layers Work?

Convolutional Neural Networks (CNNs) use shared parameters to process images. Think of an image as a cuboid with length and width (dimensions) and height (representing color channels like red, green, and blue).

Now, consider taking a small patch of the image and running a small neural network (a filter or kernel) over it, generating K outputs arranged vertically. By sliding this filter across the entire image, we get a new image with reduced width and height, but with more channels. This operation, known as convolution, uses fewer weights due to the small patch, unlike a full image that would require a regular neural network.

Mathematical Overview of Convolution

Let’s break down the mathematical process involved in convolution.

• Convolution layers are composed of a set of learnable filters (or kernels), which have small dimensions compared to the input volume, but the same depth. For an image input, the depth would be 3 (corresponding to the RGB channels).
• For instance, if we apply convolution to an image of size 34x34x3, the filters we use could have dimensions like axax3, where ‘a’ could be values such as 3, 5, or 7. These filter sizes are smaller than the image dimensions.
• During the forward pass, we slide each filter over the entire input volume, one step at a time. This sliding movement is known as the stride, and it can be set to values like 2, 3, or even 4 for larger images. At each position, we compute the dot product between the filter weights and the corresponding patch of the input volume.
• As we slide the filters over the input, we generate a 2D output for each filter. These outputs are then stacked together to form the final output volume, which will have a depth equal to the number of filters used. The network learns the values of these filters during the training process.

Layers Used to Build Convolutional Neural Networks (ConvNets)

A Convolutional Neural Network (CNN), or ConvNet, is built by stacking multiple layers, each transforming the input volume into another volume using differentiable functions.

Example with Image Size

Let’s consider running a ConvNet on an image with dimensions 32x32x3.

1. Input Layer: The input layer is where the model receives the data. In CNNs, this input is typically an image (or a sequence of images). For this example, the image has a width of 32, height of 32, and a depth of 3 (representing the RGB color channels).
2. Convolutional Layer: This layer is responsible for extracting features from the input image. It applies a set of learnable filters, also called kernels, to the input image. These filters are smaller matrices, typically 2×2, 3×3, or 5×5 in size. The filter slides over the input image and computes the dot product between the kernel weights and the corresponding patches of the input image. The result is a feature map that highlights important patterns in the image. For instance, using 12 filters in this layer would yield an output volume of dimension 32 x 32 x 12.
3. Activation Layer: After applying the convolutional layer, an activation function is used to introduce non-linearity into the network. This layer applies an element-wise activation function, such as ReLU (Rectified Linear Unit) or Tanh, to the output of the previous layer. This ensures that the model can learn complex patterns. The dimensions of the output volume remain unchanged, so it remains 32 x 32 x 12.
4. Pooling Layer: Pooling layers are used periodically within CNNs to reduce the size of the volume, which helps speed up computation, reduce memory usage, and prevent overfitting. Two common types of pooling are max pooling and average pooling. For example, if we use a 2×2 max pooling filter with a stride of 2, the output volume will be reduced in size to 16 x 16 x 12.

5. Flattening Layer:
   After the convolution and pooling layers, the feature maps are flattened into a one-dimensional vector. This prepares the data for the fully connected layers, enabling it to be used for classification or regression tasks.
6. Fully Connected Layers:
   These layers take the flattened output and perform the final computations to determine the classification or regression result. They are responsible for mapping the learned features to the final output.
7. Output Layer:
   The output from the fully connected layers is passed through an activation function, like sigmoid or softmax, to convert the raw values into probability scores, representing the likelihood of each class in classification tasks.

Example: Applying CNN to an Image

Let’s walk through applying a CNN to an image, using the convolution, activation, and pooling layers to extract features. The steps are as follows:

• Import Libraries
• Set Parameters
• Define Kernel
• Load and Plot Image
• Reformat Image
• Apply Convolution Layer
• Apply Activation Layer
• Apply Pooling Layer

Convolutional Neural Networks in Action: Image Processing with TensorFlow

we’ll walk through the process of performing convolutional image processing using Python and TensorFlow. We will cover the steps of loading an image, applying a convolution filter, activation, and pooling layers—essential concepts in the world of Convolutional Neural Networks (CNNs). Let’s dive right in!

Step 1: Import Necessary Libraries

To start with, we need to import the necessary libraries. Here’s the code for that:

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from itertools import product

These libraries help us with mathematical operations, image processing, and plotting.

Step 2: Setting Parameters

We set some initial parameters for image display:

plt.rc('figure', autolayout=True)
plt.rc('image', cmap='magma')

This ensures that images are displayed with a color map that’s easy to understand and adjust for better visibility.

Step 3: Define the Kernel (Filter)

A kernel, or filter, is a small matrix that we use to scan over the image. This kernel helps us highlight important features like edges or corners.

kernel = tf.constant([[-1, -1, -1],
                      [-1, 8, -1],
                      [-1, -1, -1]])

This is a simple edge-detection kernel. It highlights areas where there are sharp transitions in pixel intensity.

Step 4: Load and Process the Image

Next, we load an image and convert it to grayscale for processing:

image = tf.io.read_file('Ganesh.jpg')
image = tf.io.decode_jpeg(image, channels=1)  # Convert to grayscale
image = tf.image.resize(image, size=[300, 300])  # Resize for uniformity

We’re using TensorFlow’s tf.io.read_file to load the image and resize it to 300×300 pixels.

Step 5: Display the Original Image

Let’s take a look at the original grayscale image:

img = tf.squeeze(image).numpy()
plt.figure(figsize=(5, 5))
plt.imshow(img, cmap='gray')
plt.axis('off')
plt.title('Original Gray Scale image')
plt.show()

This code displays the grayscale version of the image without any axes, making it easy to analyze.

Step 6: Reformat the Image for Convolution

To apply the convolution, we need to reshape and adjust the image format:

image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.expand_dims(image, axis=0)  # Add a batch dimension
kernel = tf.reshape(kernel, [*kernel.shape, 1, 1])
kernel = tf.cast(kernel, dtype=tf.float32)

The expand_dims method adds a batch dimension, which is required by TensorFlow’s convolution function.

Step 7: Apply the Convolution Layer

Now, let’s apply the convolution operation to the image using our kernel. This highlights features like edges:

conv_fn = tf.nn.conv2d
image_filter = conv_fn(input=image, filters=kernel, strides=1, padding='SAME')

We apply the convolution with the SAME padding, which ensures the output image has the same dimensions as the input image.

Step 8: Display the Convolved Image

Let’s visualize the image after the convolution operation:

plt.figure(figsize=(15, 5))
plt.subplot(1, 3, 1)
plt.imshow(tf.squeeze(image_filter))
plt.axis('off')
plt.title('Convolution')

This will show the image after the convolution has been applied, emphasizing edges.

Step 9: Activation Layer (ReLU)

Next, we apply an activation function to introduce non-linearity. We’ll use the Rectified Linear Unit (ReLU) function here:

relu_fn = tf.nn.relu
image_detect = relu_fn(image_filter)

ReLU helps to remove negative values in the image, keeping only the features that are important for detection.

Step 10: Display the Activated Image

Let’s visualize the image after the activation layer:

plt.subplot(1, 3, 2)
plt.imshow(tf.squeeze(image_detect))
plt.axis('off')
plt.title('Activation')

This shows the image after ReLU activation, where negative values are set to zero.

Step 11: Apply Pooling (Downsampling)

We apply a pooling layer to reduce the image’s size and focus on the most important features:

pool = tf.nn.pool
image_condense = pool(input=image_detect,
                      window_shape=(2, 2),
                      pooling_type='MAX',
                      strides=(2, 2),
                      padding='SAME')

Pooling helps reduce computational complexity and emphasizes the most prominent features.

Step 12: Display the Pooled Image

Finally, let’s look at the image after the pooling operation:

plt.subplot(1, 3, 3)
plt.imshow(tf.squeeze(image_condense))
plt.axis('off')
plt.title('Pooling')
plt.show()

The pooled image shows the most important features, downsampled for further analysis.

Advantages and Disadvantages of CNNs 

Advantages of CNNs:

• Excellent at detecting patterns and features in images, videos, and audio signals.
• Invariant to translation, rotation, and scaling.
• Supports end-to-end training without the need for manual feature extraction.
• Capable of handling large datasets and achieving high accuracy.

Disadvantages of CNNs:

• Computationally intensive to train and require significant memory.
• Prone to overfitting if there is insufficient data or improper regularization.
• Depend on large amounts of labeled data.
• Limited interpretability, making it difficult to understand what the network has learned.

FAQs

What is a Convolutional Neural Network (CNN)?

A CNN is a deep learning model designed for visual data, like images and videos, using convolution and pooling layers to extract features for tasks like classification or object detection.

How do CNNs work?

CNNs apply convolution layers with filters to extract features from images, followed by pooling layers that downsample the data, making the network more efficient.

What is the difference between CNN and convolution?

CNN is the entire architecture that uses convolution layers to process and learn from data, while convolution is the mathematical operation that applies filters to extract features.

What is the basic principle of CNN?

The basic principle of CNN is to automatically learn and extract features from input data through convolution layers, enabling it to understand hierarchical patterns.

What is convolution and its types?

Convolution is the operation in CNNs that extracts features by applying filters to input data. Types include standard, depthwise, and dilated convolution, each varying in how filters are applied.

How many layers are in CNN?

The number of layers in a CNN varies depending on the architecture and task, with no fixed number.

What is the purpose of using multiple convolution layers in a CNN?

Multiple convolution layers allow the network to learn features at different levels of complexity, from simple patterns to complex shapes and objects.

What is the difference between a convolution layer and a pooling layer?

A convolution layer extracts features using filters, while a pooling layer reduces the size of the data, making the network more efficient by downsampling the output.

How CNN differ from traditional neural network?

CNNs differ from traditional neural networks by using convolution layers to automatically extract features from grid-like data, such as images. They utilize local receptive fields, weight sharing, and pooling layers to reduce the number of parameters. This makes CNNs more efficient and effective for tasks like image recognition, unlike fully connected traditional networks.