wordvectors, t2ind, n_targets, upto=-1, ds='test', binoutvec=True) # train network rng = numpy.random.RandomState(23455) if usetypecosine: print 'using cosine(e,t) as another input feature' typevecmatrix = utils.buildtypevecmatrix( t2ind, wordvectors, vectorsize) # a matrix with size: 102 * dim e2simmatrix_test = utils.buildcosinematrix(input_matrix_test, typevecmatrix) input_matrix_test = utils.extend_in_matrix(input_matrix_test, e2simmatrix_test) dt = theano.config.floatX # @UndefinedVariable index = T.lscalar() # index to a [mini]batch x = T.matrix('x') # the data is presented as rasterized images y = T.imatrix('y') # the labels are presented as 1D vector of # [int] labels ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' rng = numpy.random.RandomState(23455) layer1 = layers.HiddenLayer(rng, input=x, n_in=input_matrix_test.shape[1],
use_tanh_out = False outputtype = config['outtype'] #hinge or softmax usetypecosine = False if 'typecosine' in config: usetypecosine = utils.str_to_bool(config['typecosine']) (t2ind, n_targets, wordvectors, vectorsize, typefreq_traindev) = utils.loadTypesAndVectors(targetTypesFile, vectorFile) (rvt, input_matrix_test, iet,resvectstnall, ntrn) = utils.fillOnlyEntityData(testfile,vectorsize, wordvectors, t2ind, n_targets, upto=-1, ds='test', binoutvec=True) # train network rng = numpy.random.RandomState(23455) if usetypecosine: print 'using cosine(e,t) as another input feature' typevecmatrix = utils.buildtypevecmatrix(t2ind, wordvectors, vectorsize) # a matrix with size: 102 * dim e2simmatrix_test = utils.buildcosinematrix(input_matrix_test, typevecmatrix) input_matrix_test = utils.extend_in_matrix(input_matrix_test, e2simmatrix_test) dt = theano.config.floatX # @UndefinedVariable index = T.lscalar() # index to a [mini]batch x = T.matrix('x') # the data is presented as rasterized images y = T.imatrix('y') # the labels are presented as 1D vector of # [int] labels ###################### # BUILD ACTUAL MODEL # ###################### print '... building the model' rng = numpy.random.RandomState(23455) layer1 = layers.HiddenLayer(rng, input=x, n_in=input_matrix_test.shape[1],n_out=num_of_hidden_units, activation=T.tanh) outlayers = []
upto = -1 (t2ind, n_targets, wordvectors, vectorsize, typefreq_traindev) = utils.loadTypesAndVectors(targetTypesFile, vectorFile, upto=upto) (rvt, input_matrix_train, iet,resvectrnall, ntrn) = utils.fillOnlyEntityData(trainfile,vectorsize, wordvectors, t2ind, n_targets, upto=upto, binoutvec=True) print "number of training examples:" + str(len(iet)) (rvd, input_matrix_dev, ied,resvecdevall, ntdev) = utils.fillOnlyEntityData(devfile,vectorsize, wordvectors, t2ind, n_targets, upto=upto, binoutvec=True) print "number of validation examples:" + str(len(ied)) if usetypecosine: print 'using cosine(e,t) as another input feature' typevecmatrix = utils.buildtypevecmatrix(t2ind, wordvectors, vectorsize) # a matrix with size: 102 * dim e2simmatrix_train = utils.buildcosinematrix(input_matrix_train, typevecmatrix) e2simmatrix_dev = utils.buildcosinematrix(input_matrix_dev, typevecmatrix) input_matrix_train = utils.extend_in_matrix(input_matrix_train, e2simmatrix_train) input_matrix_dev = utils.extend_in_matrix(input_matrix_dev, e2simmatrix_dev) rng = numpy.random.RandomState(23455) dt = theano.config.floatX # @UndefinedVariable train_set_x = theano.shared(numpy.matrix(input_matrix_train, dtype=dt)) # @UndefinedVariable valid_set_x = theano.shared(numpy.matrix(input_matrix_dev, dtype=dt)) train_set_y = theano.shared(numpy.matrix(resvectrnall, dtype=numpy.dtype(numpy.int32))) valid_set_y = theano.shared(numpy.matrix(resvecdevall, dtype=numpy.dtype(numpy.int32))) n_train_batches = train_set_x.get_value(borrow=True).shape[0] n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] n_train_batches /= batch_size n_valid_batches /= batch_size