for nerClass in ['PER', 'LOC', 'ORG', 'OTH']: for subtype in ['', 'deriv', 'part']: label2Idx[bioTag + nerClass + subtype] = idx idx += 1 #Inverse label mapping idx2Label = {v: k for k, v in label2Idx.items()} # Read in data print "Read in data and create matrices" train_sentences = GermEvalReader.readFile(trainFile) dev_sentences = GermEvalReader.readFile(devFile) test_sentences = GermEvalReader.readFile(testFile) # Create numpy arrays train_x, train_y = GermEvalReader.createNumpyArray(train_sentences, windowSize, word2Idx, label2Idx) dev_x, dev_y = GermEvalReader.createNumpyArray(dev_sentences, windowSize, word2Idx, label2Idx) test_x, test_y = GermEvalReader.createNumpyArray(test_sentences, windowSize, word2Idx, label2Idx) ##################################### # # Create the Lasagne Network # ##################################### def build_network(input_var, n_in, n_hidden, n_out, embedding_matrix): ### -----> Put your code here to build the network <------- # l_out: Your network (see MNIST example from last week)
label2Idx[bioTag+nerClass+subtype] = idx idx += 1 #Inverse label mapping idx2Label = {v: k for k, v in label2Idx.items()} # Read in data print "Read in data and create matrices" train_sentences = GermEvalReader.readFile(trainFile) dev_sentences = GermEvalReader.readFile(devFile) test_sentences = GermEvalReader.readFile(testFile) # Create numpy arrays train_x, train_y = GermEvalReader.createNumpyArray(train_sentences, windowSize, word2Idx, label2Idx) dev_x, dev_y = GermEvalReader.createNumpyArray(dev_sentences, windowSize, word2Idx, label2Idx) test_x, test_y = GermEvalReader.createNumpyArray(test_sentences, windowSize, word2Idx, label2Idx) ##################################### # # Create the Lasagne Network # ##################################### def build_network(input_var, n_in, n_hidden, n_out, embedding_matrix): ### -----> Put your code here to build the network <------- # l_out: Your network (see MNIST example from last week) # params: The parameters you would like to train