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Perfect layers vs layer guides Patch#

ceil rounds off the decimal to the closet higher integer. It is used to decrease the input image size considerably as after the convolution operation the size shrinks to ceil((n+f-1)/s) where ’n’ is input dimensions ‘f’ is filter size and ‘s’ is stride length. Stride: It is generally the number of pixels you wish to skip while traversing the input horizontally and vertically during convolution after each element-wise multiplication of the input weights with those in the filter.ceil rounds off the decimal to the closet higher integer, No padding occurs. Valid- Output size shrinks to ceil((n+f-1)/s) where ’n’ is input dimensions ‘f’ is filter size and ‘s’ is stride length. Parameters for the padding function in Keras are Same- output size is the same as input size by padding evenly left and right, but if the amount of columns to be added is odd, it will add the extra column to the right. Padding: Padding is generally used to add columns and rows of zeroes to keep the spatial sizes constant after convolution, doing this might improve performance as it retains the information at the borders.Note in general we use filters with odd sizes. If you think what differentiates objects are some small and local features you should use small filters (3x3 or 5x5). If you think that a big amount of pixels are necessary for the network to recognize the object you will use large filters (as 11x11 or 9x9). Smaller filters collect as much local information as possible, bigger filters represent more global, high-level and representative information. The weights in the filter matrix are derived while training the data.

A feature may be vertical edge or an arch,or any shape.

Perfect layers vs layer guides Patch#

The filter on convolution, provides a measure for how close a patch of input resembles a feature.

Kernel/Filter Size: A filter is a matrix of weights with which we convolve on the input.

Here we will speak about the additional parameters present in CNNs, please refer part-I(link at the start) to learn about hyper-parameters in dense layers as they also are part of the CNN architecture. So lets take our quest forward with convolutional networks and see how well could a deeper hyper-parameter optimized version of this do, but before that lets have a look at the additional hyper-parameters in a convolutional neural net. RNNs would require a lot of layers and hell lot of time to mimic the same as they can find only few sequences at a single layer. Whereas a CNN can have multiple kernels/filters in a layer enabling them to find many features and build upon that to form shapes every subsequent layer. RNNs on the other hand find sequences in data and an edge or a shape too can be thought of as a sequence of pixel values but the problem lies in the fact that they have only a single weight matrix which is used by all the recurrent units which does not help in finding many spatial features and shapes. This might easily fail if we can have objects anywhere in the image and not necessarily centered like in the MNIST or to a certain extent also in the Fashion-MNIST data. if pixel number 25 and 26 are greater than a certain value it might belong to a certain class and a few complex variations of the same. In Dense networks we try to find patterns in pixel values given as input for eg. Hence they can successfully boil down a given image into a highly abstracted representation which is easy for predicting.

These high number of filters essentially learn to capture spatial features from the image based on the learned weights through back propagation and stacked layers of filters can be used to detect complex spatial shapes from the spatial features at every subsequent level. The CNNs have several different filters/kernels consisting of trainable parameters which can convolve on a given image spatially to detect features like edges and shapes. In the current article we will continue from where we left off in part-I and would try to solve the same problem, the image classification task of the Fashion-MNIST data-set using Convolutional Neural Networks(CNN).