Assignment 2: Learning World representations.

machine_learning/course2/assignment2/load_data.m

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+function [train_input, train_target, valid_input, valid_target, test_input, test_target, vocab] = load_data(N)
+% This method loads the training, validation and test set.
+% It also divides the training set into mini-batches.
+% Inputs:
+%   N: Mini-batch size.
+% Outputs:
+%   train_input: An array of size D X N X M, where
+%                 D: number of input dimensions (in this case, 3).
+%                 N: size of each mini-batch (in this case, 100).
+%                 M: number of minibatches.
+%   train_target: An array of size 1 X N X M.
+%   valid_input: An array of size D X number of points in the validation set.
+%   test: An array of size D X number of points in the test set.
+%   vocab: Vocabulary containing index to word mapping.
+
+load data.mat;
+numdims = size(data.trainData, 1);
+D = numdims - 1;
+M = floor(size(data.trainData, 2) / N);
+train_input = reshape(data.trainData(1:D, 1:N * M), D, N, M);
+train_target = reshape(data.trainData(D + 1, 1:N * M), 1, N, M);
+valid_input = data.validData(1:D, :);
+valid_target = data.validData(D + 1, :);
+test_input = data.testData(1:D, :);
+test_target = data.testData(D + 1, :);
+vocab = data.vocab;
+end