testfile = sys.argv[2]
outf = sys.argv[3]
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)
vectorFile=config['ent_vectors']
learning_rate = float(config['lrate'])
batch_size = 1#int(config['batchsize'])
n_epochs = int(config['nepochs'])
num_neg = int(config['numneg'])
testfile=sys.argv[2]
outf=sys.argv[3]
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
if 'loss_reg' in config:
l_reg = config['loss_reg']
l_weight = float(config['loss_weight'])
use_tanh_out = False
if 'tanh' in config:
use_tanh_out = True
outputtype = config['outtype'] #hinge or softmax
usetypecosine = False
if 'typecosine' in config:
usetypecosine = utils.str_to_bool(config['typecosine'])
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)
