Now, let's implement one final class that will combine multiple dense layer and softmax layer objects into a single coherent feed-forward sequential neural network. This will be implemented as another class, which will subsume the other classes. Let's first start by writing the constructor—we will be able to set the max batch size here, which will affect how much memory is allocated for the use of this network – we'll store some allocated memory used for weights and input/output for each layer in the list variable, network_mem. We will also store the DenseLayer and SoftmaxLayer objects in the list network, and information about each layer in the NN in network_summary. Notice how we can also set up some training parameters here, including the delta, how many streams to use for gradient descent (we'll see this...
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You're reading from Hands-On GPU Programming with Python and CUDA
Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Read more about Dr. Brian Tuomanen
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Dr. Brian Tuomanen has been working with CUDA and General-Purpose GPU Programming since 2014. He received his Bachelor of Science in Electrical Engineering from the University of Washington in Seattle, and briefly worked as a Software Engineer before switching to Mathematics for Graduate School. He completed his Ph.D. in Mathematics at the University of Missouri in Columbia, where he first encountered GPU programming as a means for studying scientific problems. Dr. Tuomanen has spoken at the US Army Research Lab about General Purpose GPU programming, and has recently lead GPU integration and development at a Maryland based start-up company. He currently lives and works in the Seattle area.
Read more about Dr. Brian Tuomanen