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Implementing CNNs for Image Classification in Python

Implementing CNNs for Image Classification in Python

Explore how to implement image classification using a CNN in Python programming.

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CNNs are commonly used in DL architectures for image recognition and analysis. The CNN architecture effectively identifies patterns in image data without requiring explicit feature engineering.

In the following steps, we're building a Convolutional Neural Network (CNN) for image recognition tasks, particularly utilizing the Keras library. The process involves importing necessary libraries, loading the MNIST dataset, reshaping images, normalizing data, building the CNN model, training the model, and evaluating its performance on test data. This sequence demonstrates how to construct and train a CNN using Keras for accurate image classification.

Step 1: Importing libraries

Keras is an open-source, high-level DL library with an easy-to-use interface for building neural networks. It allows developers to quickly prototype and experiment with different models, including CNNs. It also includes a collection of in-built layers, loss functions, activation functions, and optimization methods.

The following code illustrates how to import the necessary libraries using Keras for image classification:

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#Import necessary libraries
import keras
from keras.models import Sequential # importing labirary for sequential model
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.datasets import mnist #import MNIST digit dataset
from keras.optimizers import Adam
import numpy as np

  • Line 3: We import the Sequential function—a foundation class to build neural networks—from the Keras library. It allows us to build a sequential stack of layers in which each layer feeds into the next one.

  • Line 4: We import the following functions:

    • Conv2D: This function performs a convolution operation on two-dimensional input data, like images, in order to extract features from the input data. In the convolution operation, a filter or kernel is moved over the input data in a sliding window manner, and the dot product between the filter and the input data in that location is computed. The output of this computation is then stored in a new matrix called the output feature map.

    • MaxPooling2D: This is a pooling layer that operates on two-dimensional data, such as feature maps, and performs downsampling by reducing the spatial dimensions of the input feature maps. It accomplishes this by taking the maximum value inside a sliding window or kernel while retaining the most important features.

    • Flatten: This converts the input data into a one-dimensional array and is commonly used as an intermediary layer between the output of CNN layers and fully connected layers.

    • Dense: This is a fully connected layer in which each neuron of every layer is connected with all neurons of the next layer. It allows the machine to learn of complex nonlinear relationships between input and output data.

  • Line 5: We import the mnist dataset from the Keras library, a handwritten digit dataset, as shown in the figure below. It consists of 70,000 images of handwritten digits from 0 to 9, divided into 60,000 training images and 10,000 test images, each with 28x28 ...