Salient edge map in keras data augmentation

SALIENT EDGE MAP IN KERAS DATA AUGMENTATION HOW TO

Similarly, in the third layer, this cascading effect results in the square marked 3 being obtained from a large region around the leg area.

Specifically, the whole area around the left ear of the cat is responsible for the value at the square marked 2. The numbers in this square are obtained from multiple regions from the input image.

In the second layer, the square marked 2 is obtained from the bigger square in Layer 1.

In the first layer, the square marked 1 is obtained from the area in the image where the leaves are painted.

The following observations are to be noted : In the above figure, the big squares indicate the region over which the convolution operation is performed and the small squares indicate the output of the operation which is just a number. So the number of weights while using 32 filters is simply 3x3x3x32 = 288 and the number of biases is 32. Note that all neurons associated with the same filter share the same weights and biases. Please refer to Figure below for a graphical view. If we use 32 filters we will have an activation map of size 30x30x32. Typically, we use more than 1 filter in one convolution layer. We can also slide the window by more than 1 pixel. In the above case, we slid the window by 1 pixel at a time.

Unlike traditional neural nets where weights and biases of neurons are independent of each other, in case of CNNs the neurons corresponding to one filter in a layer share the same weights and biases. In our example, there are 30Г—30 = 900 neurons because there are that many locations where the 3x3x3 filter can be applied. The activation map of one layer serves as the input to the next layer.

Then 30x30x1 outputs or activations of all neurons are called the activation maps. Activation MapsДЇor a 32x32x3 input image and filter size of 3x3x3, we have 30x30x1 locations and there is a neuron corresponding to each location.

This makes the CNNs Translation Invariant. The same filters are slid over the entire image to find the relevant features. So 1 pixel is stripped away from left, right, top and bottom of the image. We have to ensure the filter is always inside the image. This is because the convolution operation is not defined for a strip 2 pixels wide around the image. If we can perform the convolution operation by sliding the 3x3x3 filter over the entire 32x32x3 sized image, we will obtain an output image of size 30x30x1. ThatвЂ™s it! Note that is termed as the kernel size. We then multiply the values in this chunk element-by-element with the convolution filter (also sized x ) and then add them all to obtain a single output. In image processing, to calculate convolution at a particular location, we extract x sized chunk from the image centered at location. If they find the features they are looking for, they produce a high activation.Д¬onvolution can be thought of as a weighted sum between two signals ( in terms of signal processing jargon ) or functions ( in terms of mathematics ). The neurons in this layer look for specific features. The convolutional layer can be thought of as the eyes of the CNN. You can also have a sigmoid layer to give you a probability of the image being a cat. The output can be a softmax layer indicating whether there is a cat or something else. In the above diagram, the input is fed to the network of stacked Conv, Pool and Dense layers. To make it work for these images, either we have to train separate MLPs for different locations or we have to make sure that we have all these variations in the training set as well, which I would say is difficult, if not impossible. The classifier predicts it correctly for the centered image but fails in the other two cases. Given below is an example of the number 7 being pushed to the top-left and bottom-right. We want the network to be Translation-Invariant. If the images in the test set are off-center, then the MLP approach fails miserably. In our training dataset, all images are centered. But there was a problem with that approach. The performance was pretty good as we achieved 98.3% accuracy on test data. In our previous article on Image Classification, we used a Multilayer Perceptron on the MNIST digits dataset. We will use the MNIST and CIFAR10 datasets for illustrating various concepts. We discussed Feedforward Neural Networks, Activation Functions, and Basics of Keras in the previous tutorials. We will also see how data augmentation helps in improving the performance of the network.

SALIENT EDGE MAP IN KERAS DATA AUGMENTATION HOW TO

In this tutorial, we will learn the basics of Convolutional Neural Networks ( CNNs ) and how to use them for an Image Classification task.

Salient edge map in keras data augmentation