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Rectified Linear Unit (ReLU)
This layer is the next after the convolutional layer. It takes the features maps of the convolutional layer and
generates the activation map by discarding all the negative numbers of the feature maps.It means all positive
numbers will go as it to the system but all negative numbers will go as zero which makes the feature map appear
as Non-linear graph with all positive values. This enhances the activation layer and provides better features to
the input image for further processing by the next layer in the CNN.
Pooling Layer
This layer reduces the dimensions of the input image while still retaining the important features. This will help
in making the input image more resistant to small transformations, distortions and translations. All this is done
to reduce the number of parameters and computation in the network thus making it more manageable and
improving the efficiency of the whole system.
For example if an image of an animal is given as an input to the CNN then by just retaining the shape of the
eyes, ears and face it is easy to identify an animal. Keeping all the features will just increase the processing time
of the image and the complexity of the code for calculating the maximum probability.
There are two types of pooling:
• Max Pooling: Max Pooling is the most commonly used method that selects the maximum value of the
current image view and helps preserve the maximum detected features.
• Average Pooling: Average Pooling finds out the average value of the current image view and thus
downsamples the feature map.
Fully Connected Layer
This is the last and the final layer of the Convolutional Neural Network. After the features of the input image
are extracted by the convolution layers and downsampled by the pooling layers,their output is a 3-dimensional
matrix which is flattened into a vector of values. These values of the single vector represent a specific feature of a
specific label and are redirected to fully connected layers to predict the final outputs of the network. This helps in
classifying an image into a specific label based on the probability of the input being in a specific class. For example
the input image with a beak belongs to the category of a bird.
bird p bird
sunset p sunset
dog p dog
... cat p cat
convolution + nonlinearity max pooling vec ...
convolution + pooling layers fully connected layers Nx binary classification
Image Classification Using CNN
Computer Vision 269

