Updated March 14, 2023
Introduction to Keras conv2D
Keras conv2D which stands for convolution layer in a 2-dimensional pattern is responsible for generating the kernel of convolution which is then amalgamated with the other input layers of the Keras model so that the final resultant output will contain a tensor. In this article, we will dive into the topic of Keras conv2D and will try to understand what is Keras conv2D, how to use Keras conv2D, Keras conv2D framework, Keras conv2D model, Keras conv2D model configuration, and a conclusion about the same.
What is Keras conv2D?
Keras conv2D is the layer of convolution that helps us generate the kernel of convolution so that when it is joined with the input layers of the Keras model, the model results in the output containing tensor. The kernel produced is a mask or matrix of convolution that is further used for the edge detection, sharpening, blurring, embossing which is carried out by performing the convolution of images and the kernel.
How to use Keras conv2D?
We can simply use the model after its compiled, fitted, and evaluated for further usage. We need to make use of the class mentioned below for referring to its syntax. Below are the steps using which you can create and use the Keras conv2D model –
• Importing the necessary libraries and classes at the top of the code.
• Declaring the configurations of the model containing all the attributes mentioned in the configuration section of this article.
• Loading the data.
• Defining the shape of the input data.
• Parsing the input values if in numbers to float.
• Scaling the data for training and testing purposes.
• Model creation and addition of the required conv2D and other layers to it.
• Model compilation.
• Fitting the input data for training purposes into the model.
• Generation of metrics for generalization and setting the verbose value or printing the results to the output screen.
Keras conv2D framework
When dealing with the images, etc that are two-dimensional inputs we can make the use of Keras conv2D for the representation of these layers. The perfect way in which they are represented is as shown below –
The class of Keras conv2D is as defined below which shows all the parameters which are required to pass –
Keras.layers.Conv2D (kernel initializer = glorot uniform, data format = None, strides = (1,1), dilation rate = (1,1), activity regularizer = None, bias regularizer = None, use bias = true, padding = valid, kernel size, filters, bias initializer = zeros, kernel regularizer = None, bias constraint = None, kernel constraint = None, activation = None)
Various parameters used here that act as the attributes are described in the below table –
|A number of pixels that accommodate the width and height are summarized as the 2-dimensional filter of specified width and height.
|A number of filters that are to be learned by the layers of convolution are represented here. Each and every individual filter slides inside the input so that the feature map is created in the resultant.
|It helps us represent the way in which the jumping of kernel over the image that is passed as input happens. For example, when the value of stride is one then the kernel moves through the input image pixel by pixel, if it is 2 then the kernel jumps by one pixel in the image that is passed as input.
|When the filters of the kernel don’t fit than the guidance is provided by padding. One of the scenarios is when the specified height and width of the image is not matching with the size of the kernel and the specified value of stride.
|Its value can be either channel first or channel last. when we pass the images as input each of them corresponds to the specific channels such as RGB red green blue or alike. These channels are present in the last or first is represented by data format. The latest version of Keras doesn’t need this as they support TensorFlow.
|When the convolutions being used are of dilated type then the specification of the rate of dilation can be done in this attribute.
|It takes the input of conv2D layer in linear format so that it can convert it to non-linear.
|Specified for each layer for scaling vertically the functionality that is learned. The default value when not specified is set to enabled.
|The configuration of constraints, biases, and regularizers can be done by using the initializers.
Keras conv2D model
For creating the conv2D model in Keras we need to have the latest version of Keras as it supports conv2D layers. It is convenient to have the TensorFlow version 2 or higher installed on your system. You can easily found the resources which will guide you about the installation process of Keras or TensorFlow once you google it or search in educba site itself. One more additional thing is we will need the latest version of python on your system such as the one which is installed by using Anaconda.
There are certain principles followed while creating the conv2D model in Keras which are –
- The images of input should be transformed by the layers of conv2D into an abstract representation.
- Densely connected layers can be used for the generation of the classification in the representation of images.
- Note that dense layers are capable of handling only one-dimensional data. In case we have multi-dimensional data, we will first need to convert it into a single dimension by the last conv2D layer or by using the flatten layer.
Keras conv2D model configuration
The following configurations need to be done for the model –
- The batch size should be set. For example, if it is set to 20 then we need to pass 20 samples to the model for each and every individual step.
- Specification of a number of channels, width, height, or image should be done.
- The values of the optimizer and loss function should be set.
- Set the value of no_classes. For example, if the dataset of your model has 15 classes then no_classes should be set to 15.
- We will need to specify the number of iterations that is the value of epochs which can be any integer value as per our requirement. This is the number of times that we want to iterate the training of our model.
- Check whether the model is overfitting by using the data of training for validation which includes checking the performance of the model after each and every iteration.
- In order to display the output on the screen, we can set the value of mode verbosity to true or equivalent value such as 1.
The below image shows the sample configurations of one of the Keras conv2D models that we have created –
Keras conv2D are the layers of convolution in a 2-dimensional pattern and generate kernel of convolution which is then bound with the other input layers of the Keras model to get an output of tensor.
This is a guide to Keras conv2D. Here we discuss the introduction, What is Keras conv2D, How to use Keras conv2D, examples, and attributes. You may also have a look at the following articles to learn more –