By Sanshodhana LP
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Extra resources for mACHINE lEARNING eXPERIMENTS
The gradientdescent learning rate is included in the sum(de/dx) calculation and this is where we have a get the boundary for local minimum. The stochastic version of this is left to the reader but since this is linear function not much change is expected in this scenario. run(b), '\n') The figure below shows the problem space and the regression line in the second scatter graph in the figure below. This is not something which is part of a bigger solution but can be considered for layout analysis.
The first dimension being the batch size which will be specified later. What the convolution does to come up with the 32 or 64 outputs is left to the reader to investigate. Let’s finish by going over the following 4 lines the entire code base is on the code site. The softmax_cross_entropy_with_logits takes the output of the prediction tensor which has a size of 10 and the actual value of the training sample which is also a tensor of size 10 (no of classes) and comes out with a probability value whose mean is then calculated for digits 0,1,2,3,4,5,6,7,8,9 separately by minimizing the error in the entropy for all dimensions which are the 10 dimensions of the scores for each 0, 1, 2, 3,4,5,6,7,8,9.
There is a python simulation of gradient descent in the additional materials folder on Google cloud storage. There are other variations of the Gradient descent algorithm available one most popular is Stochastic gradient descent where we calculate the minima at each point using a shuffled set of data points there by reducing the risk of getting stuck in a local minima. run command executes the Tensor flow directed graph, and the cost node is passed in along with the training values. The gradientdescent learning rate is included in the sum(de/dx) calculation and this is where we have a get the boundary for local minimum.