# Load the dev set (for tuning hyperparameters) docs = du.load_dataset('data/ner/dev') X_dev, y_dev = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=windowsize) # Load the test set (dummy labels only) docs = du.load_dataset('data/ner/test.masked') X_test, y_test = du.docs_to_windows(docs, word_to_num, tag_to_num, wsize=windowsize) from softmax_example import SoftmaxRegression sr = SoftmaxRegression(wv=zeros((10, 100)), dims=(100, 5)) ## # Automatic gradient checker! # this checks anything you add to self.grads or self.sgrads # using the method of Assignment 1 sr.grad_check(x=5, y=4) #from nerwindow import WindowMLP from nerwindow_msushkov import WindowMLP clf = WindowMLP(wv, windowsize=windowsize, dims=[None, 100, 5], reg=0.001, alpha=0.01) clf.grad_check(X_train[0], y_train[0]) # gradient check on single point