# 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
