Deep Learning for Computer Vision

Building the model

In this section, we will use a few convolution layers followed by fully connected layers for training the preceding dataset. Construct a simple sequential model with two convolution layers followed by pooling, dropout, and dense layers.  A sequential model has the add method to stack layers one above another. The first layer has 64 filters, and the second layers have 128 filters. The kernel size is 3 for all the filters. Apply the max pooling after the convolution layers. The output of the convolution layers is flattened connecting to a couple of fully connected layers with dropout connections.

The last layer is connected to softmax as this is a multiclass classification problem. The following code shows how to define the ...