mACHINE lEARNING eXPERIMENTS by Sanshodhana LP
By Sanshodhana LP
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And the update_centroid statement captures the inner summation of deduce the mean along the x axis for all 7 partitions. run([update_centroids,centroids,assignments]) The above statement runs the directed graph consisting of the 2 summations in the equation. The concat operation is illustrated below for reference: (The vectors or tensors are joined together using concat for dimension specified). We are ready to do some pattern matching with Neural Network here is a multi-layer perceptron and a convolution net.
Square(vectors_subtration), 2) The above 2 lines are important vector operations which basically determine the distance of centroids from the candidate vectors and also based on this calculate Euclidian distance over the vectors_subtraction vector which yields (x-c)^2 + (y-c)^2 calculation. dynamic_partition(vectors, assignments, num_clusters) The above statement for assignments calculates the smallest eculidean_distance of vectors (the argument 0 is for vectors). The below expression is captured elegantly by the dynamic partition statement above where the assignments vector captures the outer summation of vectors into k clusters.
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.