Python Tensorflow - tf.keras.Conv2D() Function

Introduction

In deep learning, computer vision is one of the most important fields which is used for many complex and advanced tasks related to image datasets. It is used for image analysis, object detection, segmentations, etc. This is mainly achieved with the combination of TensorFlow and Keras, which offers several inbuilt functions which automate and make the process of model training very easy.

The Conv2D is also one of the most useful and powerful functions in the Keras library, which is used for applying convolutional operations to the image. In this article, we will discuss the Conv2D function in Keras, what it is, how we can use it, and several other discussions related to the same.

So before directly jumping into the function, let us discuss a bit about the convolutional operations.

What are Convolutional Operations?

Convolutional operations are the type of operations used in convolution neural networks to extract the information or the features from the input image data.

Here convolutional layers are used, which apply these operations to the input image and consist of different filters, their size, padding, input shape, and activation function.

Here in this convolutional layer, the input image will be passed, to which all the filters will be applied with specified size, and the padding and activations function will also be applied. This layer converts the image into a different form, and then this converted image is passed into the next layer or the next convolutional layer.

These operations performed in the convolutional layers are known as convolutional operations, which are necessary to process and extract the features from the data.

Now let us discuss the tf.keras.Conv2D() Function, it;'s meaning, and the parameters of the same.

tf.keras.Conv2D() Function

As we discussed above that, convolutional operations are the operations that are performed in the convolutional layers, which has its certain parameters defined, and different operations are performed in the image in order to extract the features from the image. To perform this task in Keras, the Conv2D function is used.

The tf.keras.Conv2D is a function that helps create the convolutional layers in neural networks. With the help of this function, we can create a very new convolutional layer by specifying the parameters of the same.

This function takes several parameters as input; let us discuss them one by one.

Number of Filters: As it is a convolutional operation, we will have a kernel or a filter that will be applied to the image or an input image to transform the input. The first parameter is the number of filters which specifies how many filters we want to apply to the image. This should be selected carefully as it influences the performance and complexity of the model.

Kernel Size: The kernel size is the measure of filter size which specify the size of the filter that we are going to apply to the image. The kernel size should be selected carefully in order to have a better-performing model.

Activation Function: The activation function is also one of the most important parameters of the convolutional layer, where the activation function to be used is defined for each convolutional layer. Each convolution layer can have a different activation function, and it can be changed in order to tune the model for better performance.

Padding: As we know that padding is an extra layer of pixels that is added to the image for many purposes. The convolutional operations also involve the padding parameters, which are used to specify the padding which is to be applied to an image in a specific image. Here we can choose either valid or the same value for the padding. The valid padding means that the input image size will remain the same and no padding will be applied, which is the default parameter, and the same padding means that the p[adding will be applied in such a way as to maintain the size of the image same.

Strides: The strides are also defined while creating the convolutional layers, which is basically a measure of the step size of the kernel movement on the image. The strides can then be modified to tune the model and enhance the performance of the same.

Now let us apply the same concepts with code examples of the tf.keras.Conv2D() Function.

tf.keras.Conv2D() Function: Code Example

To apply the tf.keras.Conv2D function to any image, first we will need to install the required libraries, which are TensorFlow and Keras.

To install the required libraries, we can use the above code:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

Now as we have installed the required libraries, the next step is to generate an image or load an existing image. To do this, use the above code:

image = np.random.rand(1, 224, 224, 3).astype(np.float32)

Now as we have our images and libraries ready, we will use the tf.keras.Conv2D() function to create a convolution layer.

conv_layer1 = tf.keras.layers.Conv2D(16, (3, 3), 
activation='relu', input_shape=(224, 224, 3))
conv_layer2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')

As we can see in the above code, that we have created two different convolutional layers with different numbers of filters and the same kernel size. Next, we will send out input images to the convolutional layers in order to extract the efeature4s from the same.

output1 = conv_layer1(image)
output2 = conv_layer2(output1)

As we can see, the input image is sent to the convolution layer one, and once it is preprocessed and operations are finished, the same output image from convolutional layer one will be sent to the convolutional layer two as an input image.

Conclusion

In this article, we discussed the tf.keras.conv2D() Function, the convolution operations and their significance in convolutional neural networks, and the code example showing the use case of the same. This article will help one to understand the Conv2D function better and will help one to apply the same wherever necessary.