editedbook
Publication: IIP Proceedings Year: 2022, Month: November Page No: 127-132, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023A372B2F0E79824A.pdf
DEEP LEARNING BASED PREDICTING IMAGES USING CONVNET
Area/Stream: Computing Technologies and Data Sciences, Authors: Chandra Sekhar Koppireddy, Veeravalli Sohan Venkata Satvik Keywords: convolutional layer, pooling layer, fully connected layers Book Name /series: DEEP LEARNING BASED PREDICTING IMAGES USING CONVNETPublication: IIP Proceedings Year: 2022, Month: November Page No: 127-132, ISSN/ISBN: 978-81-959356-3-5, DOI/Link: https://rsquarel.org/assets/docupload/rsl2023A372B2F0E79824A.pdf
Abstract:
In this paper, we will be learn about how to predict cats and dogs using convolutional neural networks (CNN) model. In this project we will be using tensorflow and keras python modules. Import Image Generator from the keras module. Along with these there will be need of the dataset which contains dogs and cats which must be created on the requirement of user. Firstly, import the required libraries which are mentioned above along with matplotlib library which will be useful in plotting the loss, accuracy, val_loss, val_accuracy. Secondly, build a CNN model in which the building of CNN model contain initialization, convolution, pooling, flatten, full connection, output layer. After completion of building the model, compile the model, and train the model. Fourthly, write the code to make the prediction. And the model’s accuracy will be depended on the number of epochs provided while training the model. Lastly plot the accuracy provided by the epochs.
Cite this: Chandra Sekhar Koppireddy, Veeravalli Sohan Venkata Satvik,"DEEP LEARNING BASED PREDICTING IMAGES USING CONVNET", DEEP LEARNING BASED PREDICTING IMAGES USING CONVNET, November, 2022, 127-132, 978-81-959356-3-5, https://rsquarel.org/assets/docupload/rsl2023A372B2F0E79824A.pdf
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