A CNN is a kind of network architecture for deep learning algorithms and is specifically used for image recognition and tasks that involve the processing of pixel data. This repository contains the code for CNN with a categorical classification dataset.
The three types of layers usually present in a Convolutional Network are:
Convolutional Layers Pooling Layers Fully Connected Layers
The Convolution Layer: Convolution is an orderly procedure where two sources of information are intertwined; it’s an operation that changes a function into something else. Convolutions have been used for a long time typically in image processing to blur and sharpen images, but also to perform other operations.
The Pooling Layer: Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network.
The Fully Connected Layer: Fully-connected layers, also known as linear layers, connect every input neuron to every output neuron and are commonly used in neural networks. If you want to know more about filly connected layer check my artificail neural network repositorie.
About the Dataset: This is a categorical classification dataset. Which has a different categories of data. This dataset is from pytorch CIFAR-10. The CIFAR-10 dataset contains 60,000 32x32 color images, and each image is represented as a 3-D tensor. Each image will be a (32,32,3) tensor, where the dimensions are 32 (height) x 32 (weight) x 3 (R-G-B color channels). There are different types of classes are present. They are ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'].
This convolution neural network has Cross entropy loss function which is best suitable for the categorical dataset. This neural network has Adam optimizer which is the more suitable for this dataset.
Output: There are two output in this repositorie. In the first one the number of epochs used are 200 with a learing rate of 0.0001. The output has training loss of 0.02 and the testing accuracy of 72. The sample prediction by the neural network is in OUTPUT 1.txt. The sample data set is in Figure_1.png.The second one has the epochs of 500 and the learing rate of 0.00001.the output has training loss of 0.004 the test accuracy of 75. The sample prediction is in OUTPUT 2.txt. The sample prediction is in Figure_2.png.
Software used: Pycharm Community - 2023.1.4 cuda version - 12.2 torch - 2.0.1+cu118 torchaudio - 2.0.2 torchvision - 0.15.2+cu118 Jinja2 - 3.1.2 numpy - 1.25.0 matplotlib - 3.7.2