Flower Classification Using CNN Layers

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Flower Classification Using CNN Layers

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Abstract

In the era of advancement of technology, the growth of deep learning is increasing with the increase in the day to day activities around.Deep learning has been used to find several subjects such as face recognition, object detection, and face detection. Deep learning is a sub layer of AI and machine learning that is defined as the process of simulating the human brain. Neural networks are the foundation of deep learning.Deep learning is one of the sub layer of AI and ML,and is defined as the process of mimicing of human brain.Deep learning is based on neural networks .The concept of deep learning mimics how human neurons sends signals to particular organ according to the task.We are using CNN for this model because for training the images,CNN is the best suited model.CNNs are a category of neural network that works to processing data with a grid-like architecture, such as pixels.The three layers involved in CNN are: 1. The convolution Layer 2. The pooling layer 3.The fully Convoluted layer The first two layers i.e.,Convolution layer and pooling layer is used to identify the features in an image The last layer ,Fully convoluted layer is used to extract the features in an image. Our model is based on flower image classification.In this model,we used five different optimizers in calculating loss functions.The lower the loss function,gives the better model.

Keywords

Neural networks Convolution Neural network Pooling layer Fully convoluted layer Gradient Descent Adam AdaDelta AdaGrad Loss Functions Optimizers.

References

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