CNN stands for Convolutional Neural Network. It is a type of artificial neural network that revolves around for processing of structured grid-like data and many more. Moreover, It is also very small in tasks which include images, audio, videos and sequential data like time-series.
At the core of CNN, there are convolutional layers that perform convolutions in technical terms and filters in simple terms to input data. Furthermore, these filters detect several features within the data, like textures, edges and many more complex patterns that are available in the data. Then the neural network learns of merging these features across multiple layers by extracting the input in hierarchical representations.
Components of CNN:
A CNN consists of various components or aspects that work together to extract features from grid-like data as mentioned above.
Input Layer:
It is the initial stage of the neural network where it receives and processes the input data. Because the provided information is given raw, this layer’s responsibility is to make that information compatible for the network to analyze and learn from.
Convolution Layer:
As explained earlier the convolution layers apply filters on input data. So that the network can learn patterns, edges and textures within the data.
Pooling Layer:
Pooling Layers are also known as Subsampling Layers. The main function of these pooling layers is to retain only the important features while leaving out all the non-important data. Common pooling methods are max pooling and average pooling, which resultantly downsizes the input data and helps in computational efficiency.
Fully Connected Layer:
Fully Connected Layers are found at the end of the neural network. These Layers perform the functionality of combining the learned features and do tasks like classification and regression. Adding to that, each neuron in this layer is always connected to activation functions in previous layers as well.
Implementation of CNN:
There are several steps in the implementation of CNN:
Data Preparation:
Gather and preprocessing of the dataset. For example in image tasks, there is a need to resize and normalise pixel values and also split those images in training and testing datasets. It is recommended that 80 per cent of the whole dataset should be in training and the remaining 20 per cent should be in the testing dataset. Furthermore, It is very important to know that data in training and testing datasets should be different. In addition to that, make sure the size of data in all the classes remains equal so that we don’t face any problems related to over-fitting or less accuracy.
Building the Model:
This step involves creating the CNN architecture using a deep learning framework like TensorFlow or PyTorch. Then we will start by defining the input shape, convolutional layers, activation functions, pooling layers, fully connected layers, and an output layer based on the expected result.
Compile the Model:
In this phase, we will identify loss functions, optimizers and metrics(accuracy) to monitor during training.
Training:
After all these previous steps, there comes the most important step which is training. Provide the input data to the model and also adjust the weights and biases back and forth to minimize loss function using backpropagation. Also, monitor the performance on the validation set or the ground truth to prevent overfitting.
Then the remaining steps are evaluation, fine-tuning, optimization and deployment.
Now let’s see the code of the logic I provided you earlier.
import tensorflow as tf
from tensorflow.keras import layers, models
model=models.Sequential()
#where h=height, w=width, c=channels
model.add(layers.Conv2D(32,(3,3)), activation="relu", input_shape=(h,w,c))
#max pooling in order to retain only important information
model.add(layers.MaxPooling2D(2,2))
#making the output suitable for fully connected layers
model.add(layers.Flatten())
model.add(layers.Dense(128, activation="relu"))
#num classes means the expected result within those classes
model.add(layers.Dense(num_classes, activation="softmax")
model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
model.fit(train_images, train_labels, epochs=5, validation_data=(val_images, val_labels))
test_loss, test_acc=model.evaluate(test_images, test_labels)
#printing the accuracy to know how much accurate is our neural network
print('Test accuracy', test_acc)Importance of CNNs:
CNNs hold immense significance due to their qualities and their use in various domains. Below are a few examples:
- Image Processing
- Computer Vision Advancements
- Medical Imaging
- Natural Language Processing
- Autonomous Vehicles
- Industrial Automation
- Entertainment and Gaming
Pros of CNN:
- CNN can learn complex relationships within data.
- It uses parameter sharing which makes it computationally more efficient.
- CNN is robust because it can identify features irrespective of their position in the image.
- Pre-trained CNN models like ImageNet, VGG16 and many more can be fine-tuned for specific tasks, leveraging the learned features for new applications and requiring less data for training.
Cons of CNN:
- Computational Intensity due to the high resolution of images can require significant computational resources.
- CNNs need large amounts of labelled data for effective training. Insufficient or unbalanced data might lead to overfitting or inaccurate predictions.
- Understanding how CNNs make decisions is quite challenging due to their complex architecture.
FAQs
What is CNN in Python?
A CNN in Python is a type of deep learning model used in tasks involving image processing, audio recognition, and sequential data.
How does CNN work?
CNN works by processing the input data through specific layers designed to extract specific features and also learn a hierarchical representation of features.
What are the Types of CNN?
The CNN come in different architectures. Each is suitable for a specific task. Some famous ones are;
- LeNet
- AlexNet
- VGGNet
- Inception
- Xception
- ResNet
- DenseNet
- MobileNet
- EfficientNet
Conclusion
Convolution Neural Networks stand as a cornerstone in deep learning due to their ability to learn and extract hierarchical representations of features from raw data, particularly images and sequential data
which has driven advancements in Image recognition, object detection medical imaging and more.
