def __init__(self,
cropsize,
batch_size,
nkerns=[10, 10, 10],
filters=[11, 6, 4]):
self.X_batch, self.y_batch = T.tensor4('x'), T.matrix('y')
self.layers, self.params = [], []
rng = np.random.RandomState(23455)
layer0 = LeNetConvPoolLayer(rng,
input=self.X_batch,
image_shape=(batch_size, 1, cropsize,
cropsize),
filter_shape=(nkerns[0], 1, filters[0],
filters[0]),
poolsize=(2, 2))
self.layers += [layer0]
self.params += layer0.params
# 400 - 11 + 1 = 390 / 2 = 195
map_size = (cropsize - filters[0] + 1) / 2
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0],
map_size, map_size),
filter_shape=(nkerns[1], nkerns[0],
filters[1], filters[1]),
poolsize=(2, 2))
self.layers += [layer1]
self.params += layer1.params
# 195 - 6 + 1 = 190 / 2 = 95
map_size = (map_size - filters[1] + 1) / 2
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(batch_size, nkerns[1],
map_size, map_size),
filter_shape=(nkerns[2], nkerns[1],
filters[2], filters[2]),
poolsize=(2, 2))
self.layers += [layer2]
self.params += layer2.params
# 95 - 4 + 1 = 92 / 2 = 46
map_size = (map_size - filters[2] + 1) / 2
layer3_input = layer2.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=nkerns[2] * map_size * map_size,
n_out=1,
activation=None)
self.layers += [layer3]
self.params += layer3.params
nparams = np.sum(
[p.get_value().flatten().shape[0] for p in self.params])
print "model contains %i parameters" % nparams
self.output = self.layers[-1].output
def __init__(self, nkerns=[48], miniBatchSize=200):
rng = numpy.random.RandomState(23455)
nClasses = 2
nMaxPool = 2
nHidden = 200
self.p = 65
#self.x = T.tensor3('x') # membrane data set
self.x = T.tensor4('x') # membrane mini-batch
self.y = T.ivector('y') # 1D vector of [int] labels
self.miniBatchSize = miniBatchSize
# Reshape matrix of rasterized images # to a 4D tensor,
# compatible with the LeNetConvPoolLayer
#layer0_input = self.x.reshape((self.miniBatchSize, 1, self.p, self.p))
layer0_input = self.x
#--------------------------------------------------
# LAYER 0
# layer0 convolution+max pool reduces image dimensions by:
# 65 -> 62 -> 31
#--------------------------------------------------
fs0 = 4 # filter size, layer 0
os0 = (self.p - fs0 + 1) / nMaxPool # image out size 0
assert (os0 == 31)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(self.miniBatchSize, 1, self.p,
self.p),
filter_shape=(nkerns[0], 1, fs0, fs0),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 1
# Fully connected sigmoidal layer, goes from
# X -> 200
#--------------------------------------------------
layer1_input = layer0.output.flatten(2)
layer1 = HiddenLayer(rng,
input=layer1_input,
n_in=nkerns[0] * os0 * os0,
n_out=nHidden,
activation=T.tanh)
#--------------------------------------------------
# LAYER 2
# Classification via a logistic regression layer
# 200 -> 2
#--------------------------------------------------
# classify the values of the fully-connected sigmoidal layer
layer2 = LogisticRegression(input=layer1.output,
n_in=nHidden,
n_out=nClasses)
self.layers = (layer0, layer1, layer2)
def __init__(self, nkerns=[48, 48, 48, 48], miniBatchSize=200):
rng = numpy.random.RandomState(23455)
nClasses = 2
nMaxPool = 2
nHidden = 200
self.p = 95
#self.x = T.tensor3('x') # membrane data set
self.x = T.tensor4('x') # membrane mini-batch
self.y = T.ivector('y') # labels := 1D vector of [int] labels
self.miniBatchSize = miniBatchSize
# Reshape matrix of rasterized images # to a 4D tensor,
# compatible with the LeNetConvPoolLayer
#layer0_input = self.x.reshape((self.miniBatchSize, 1, self.p, self.p))
layer0_input = self.x
#--------------------------------------------------
# LAYER 0
# layer0 convolution+max pool reduces image dimensions by:
# 95 -> 92 -> 46
#--------------------------------------------------
fs0 = 4 # filter size, layer 0
os0 = (self.p - fs0 + 1) / nMaxPool # image out size 0
assert (os0 == 46)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(self.miniBatchSize, 1, self.p,
self.p),
filter_shape=(nkerns[0], 1, fs0, fs0),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 1
# layer1 convolution+max pool reduces image dimensions by:
# 46 -> 42 -> 21
#--------------------------------------------------
fs1 = 5 # filter size, layer 1
os1 = (os0 - fs1 + 1) / nMaxPool # image out size 1
assert (os1 == 21)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(self.miniBatchSize, nkerns[0],
os0, os0),
filter_shape=(nkerns[1], nkerns[0], fs1,
fs1),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 2
# layer2 convolution+max pool reduces image dimensions by:
# 21 -> 18 -> 9
#--------------------------------------------------
fs2 = 4
os2 = (os1 - fs2 + 1) / nMaxPool
assert (os2 == 9)
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(self.miniBatchSize, nkerns[0],
os1, os1),
filter_shape=(nkerns[2], nkerns[1], fs2,
fs2),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 3
# layer3 convolution+max pool reduces image dimensions by:
# 9 -> 6 -> 3
#--------------------------------------------------
fs3 = 4
os3 = (os2 - fs3 + 1) / nMaxPool
assert (os3 == 3)
layer3 = LeNetConvPoolLayer(rng,
input=layer2.output,
image_shape=(self.miniBatchSize, nkerns[0],
os2, os2),
filter_shape=(nkerns[3], nkerns[2], fs3,
fs3),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 4
# Fully connected sigmoidal layer, goes from
# 3*3*48 ~ 450 -> 200
#--------------------------------------------------
layer4_input = layer3.output.flatten(2)
layer4 = HiddenLayer(rng,
input=layer4_input,
n_in=nkerns[3] * os3 * os3,
n_out=nHidden,
activation=T.tanh)
#--------------------------------------------------
# LAYER 5
# Classification via a logistic regression layer
# 200 -> 2
#--------------------------------------------------
# classify the values of the fully-connected sigmoidal layer
layer5 = LogisticRegression(input=layer4.output,
n_in=nHidden,
n_out=nClasses)
self.layers = (layer0, layer1, layer2, layer3, layer4, layer5)
def cifar_fast_net(batch_size=128,n_epochs=300,test_frequency=13, learning_rate=0.001):
rng1 = numpy.random.RandomState(23455)
rng2 = numpy.random.RandomState(12423)
rng3 = numpy.random.RandomState(23245)
rng4 = numpy.random.RandomState(12123)
rng5 = numpy.random.RandomState(25365)
rng6 = numpy.random.RandomState(15323)
train_set_x, train_set_y = load_cifar_data(['data_batch_1','data_batch_2','data_batch_3','data_batch_4'])
valid_set_x, valid_set_y = load_cifar_data(['data_batch_5'],WHICHSET='valid')
test_set_x, test_set_y = load_cifar_data(['test_batch'],WHICHSET='test')
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_test_batches = test_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
img_input = x.reshape((batch_size,3,32,32))
img_input = img_input.dimshuffle(1,2,3,0)
####define the layers:
conv_pool1 = LeNetConvPoolLayer(rng=rng1,input=img_input,
filter_shape=(3,5,5,32),
image_shape=(3,32,32,batch_size),
activation='vshape',
poolsize=(3,3),poolstride=2,pad=2,
convstride=1,initW=0.0001,initB=0,partial_sum=4,
pooling='max',
epsW=0.001,
epsB=0.002,
momW=0.9,
momB=0.9,
wc=0.004
)
conv_pool2 = LeNetConvPoolLayer(rng=rng2,input=conv_pool1.output,
filter_shape=(32,5,5,32),
image_shape=(32,16,16,batch_size),
activation='vshape',
poolsize=(3,3),poolstride=2,pad=2,
convstride=1,initW=0.01,initB=0,partial_sum=4,
pooling='average',
epsW=0.001,
epsB=0.002,
momW=0.9,
momB=0.9,
wc=0.004)
conv_pool3 = LeNetConvPoolLayer(rng=rng3,input=conv_pool2.output,
filter_shape=(32,5,5,64),
image_shape=(32,8,8,batch_size),
activation='vshape',
poolsize=(3,3),poolstride=2,pad=2,
convstride=1,initW=0.01,initB=0,partial_sum=4,
pooling='average',
epsW=0.001,
epsB=0.002,
momW=0.9,
momB=0.9,
wc=0.004)
layer4_input = conv_pool3.output.dimshuffle(3,0,1,2).flatten(2)
#fc_64 = HiddenLayer(rng=rng4,input=layer4_input,n_in=64*4*4,n_out=64,initW=0.1,initB=0)
fc_64 = HiddenLayer(rng=rng4,input=layer4_input,n_in=64*4*4,n_out=64,initW=0.1,initB=0,
epsW=0.001,
epsB=0.002,
momW=0.9,
momB=0.9,
wc=0.03)
fc_10 = LogisticRegression(input=fc_64.output,rng=rng5,n_in=64,n_out=10,initW=0.1,
epsW=0.001,
epsB=0.002,
momW=0.9,
momB=0.9,
wc=0.03)
####build the models:
cost = fc_10.negative_log_likelihood(y)
test_model = theano.function([index], fc_10.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]})
validate_model = theano.function([index], fc_10.errors(y),
givens={
x: valid_set_x[index * batch_size: (index + 1) * batch_size],
y: valid_set_y[index * batch_size: (index + 1) * batch_size]})
Ws = [conv_pool1.W, conv_pool2.W, conv_pool3.W, fc_64.W, fc_10.W]
pgradWs = [conv_pool1.grad_W, conv_pool2.grad_W, conv_pool3.grad_W, fc_64.grad_W, fc_10.grad_W]
bs = [conv_pool1.b, conv_pool2.b, conv_pool3.b, fc_64.b, fc_10.b]
pgradbs = [conv_pool1.grad_b, conv_pool2.grad_b, conv_pool3.grad_b, fc_64.grad_b, fc_10.grad_b]
momWs = [conv_pool1.momW, conv_pool2.momW, conv_pool3.momW, fc_64.momW, fc_10.momW]
momBs = [conv_pool1.momB, conv_pool2.momB, conv_pool3.momB, fc_64.momB, fc_10.momB]
wcs = [conv_pool1.wc, conv_pool2.wc, conv_pool3.wc, fc_64.wc, fc_10.wc]
epsWs = [conv_pool1.epsW, conv_pool2.epsW, conv_pool3.epsW, fc_64.epsW, fc_10.epsW]
epsBs = [conv_pool1.epsB, conv_pool2.epsB, conv_pool3.epsB, fc_64.epsB, fc_10.epsB]
gradWs = T.grad(cost, Ws)
gradbs = T.grad(cost, bs)
updates = []
for W_i, gradW_i, momW_i, wc_i, epsW_i, pgW_i in zip(Ws,gradWs,momWs,wcs, epsWs,pgradWs):
grad_i = - epsW_i*gradW_i - wc_i*epsW_i*W_i + momW_i*pgW_i
updates.append((W_i, W_i+grad_i))
updates.append((pgW_i,grad_i))
for b_i, gradb_i, momB_i, epsB_i, pgB_i in zip(bs,gradbs,momBs, epsBs,pgradbs):
grad_i = - epsB_i*gradb_i + momB_i*pgB_i
updates.append((b_i, b_i+grad_i))
updates.append((pgB_i,grad_i))
train_model = theano.function([index], cost, updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
###############
# TRAIN MODEL #
###############
print '... training'
# early-stopping parameters
patience = 10000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is
# found
improvement_threshold = 0.995 # a relative improvement of this much is
# considered significant
validation_frequency = min(n_train_batches, patience / 2)
# go through this many
# minibatche before checking the network
# on the validation set; in this case we
# check every epoch
best_params = None
best_validation_loss = numpy.inf
best_iter = 0
test_score = 0.
start_time = time.clock()
epoch = 0
done_looping = False
while (epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
#below is the code for reduce learning_rate
###########################################
if epoch == 50:
epsWs = [k/10.0 for k in epsWs]
epsBs = [k/10.0 for k in epsBs]
print 'reduce eps by a factor of 10'
updates = []
for W_i, gradW_i, momW_i, wc_i, epsW_i, pgW_i in zip(Ws,gradWs,momWs,wcs, epsWs,pgradWs):
grad_i = - epsW_i*gradW_i - wc_i*epsW_i*W_i + momW_i*pgW_i
updates.append((W_i, W_i+grad_i))
updates.append((pgW_i,grad_i))
for b_i, gradb_i, momB_i, epsB_i, pgB_i in zip(bs,gradbs,momBs, epsBs,pgradbs):
grad_i = - epsB_i*gradb_i + momB_i*pgB_i
updates.append((b_i, b_i+grad_i))
updates.append((pgB_i,grad_i))
train_model = theano.function([index], cost, updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
##############################################
for minibatch_index in xrange(n_train_batches):
iter = (epoch - 1) * n_train_batches + minibatch_index
if iter % 100 == 0:
print 'training @ iter = ', iter
cost_ij = train_model(minibatch_index)
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i
in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f %%' % \
(epoch, minibatch_index + 1, n_train_batches, \
this_validation_loss * 100.))
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * \
improvement_threshold:
patience = max(patience, iter * patience_increase)
# save best validation score and iteration number
best_validation_loss = this_validation_loss
best_iter = iter
conv_pool1.bestW = conv_pool1.W.get_value().copy()
conv_pool1.bestB = conv_pool1.b.get_value().copy()
conv_pool2.bestW = conv_pool2.W.get_value().copy()
conv_pool2.bestB = conv_pool2.b.get_value().copy()
conv_pool3.bestW = conv_pool3.W.get_value().copy()
conv_pool3.bestB = conv_pool3.b.get_value().copy()
fc_64.bestW = fc_64.W.get_value().copy()
fc_64.bestB = fc_64.b.get_value().copy()
fc_10.bestW = fc_10.W.get_value().copy()
fc_10.bestB = fc_10.b.get_value().copy()
##saving current best
print 'saving current best params..'
current_params = (conv_pool1.bestW,conv_pool1.bestB,conv_pool2.bestW,
conv_pool2.bestB,conv_pool3.bestW,conv_pool3.bestB,fc_64.bestW,fc_64.bestB,
fc_10.bestW,fc_10.bestB,momWs,momBs,epsWs,epsBs,wcs)
outfile = file('current_best_params.pkl','wb')
cPickle.dump(current_params,outfile)
outfile.close()
# test it on the test set
test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print((' epoch %i, minibatch %i/%i, test error of best '
'model %f %%') %
(epoch, minibatch_index + 1, n_train_batches,
test_score * 100.))
if patience <= iter:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print('Best validation score of %f %% obtained at iteration %i,'\
'with test performance %f %%' %
(best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr, ('The code for file ' +
os.path.split(__file__)[1] +
' ran for %.2fm' % ((end_time - start_time) / 60.))
embeddings = theano.shared(numpy.array(wordvectors,
dtype=theano.config.floatX)).dimshuffle(
1, 0)
batchsizeVar = numSamples.shape[0]
y_resh = y.reshape((batchsizeVar, )) # rel:e1->e2
y1ET_resh = y1ET.reshape((batchsizeVar, ))
y2ET_resh = y2ET.reshape((batchsizeVar, ))
numSamples_resh = numSamples.reshape((batchsizeVar, ))
layers = []
cnnContext = LeNetConvPoolLayer(rng=rng,
filter_shape=(nkernsContext, 1,
representationsize,
filtersizeContext),
poolsize=(1, kmaxContext))
layers.append(cnnContext)
if "middleContext" in config:
hidden_in = nkernsContext * kmaxContext
else:
cnnEntities = LeNetConvPoolLayer(rng=rng,
filter_shape=(nkernsEntities, 1,
representationsize,
filtersizeEntities),
poolsize=(1, kmaxEntities))
layers.append(cnnEntities)
hidden_in = 2 * (2 * nkernsContext * kmaxContext +
nkernsEntities * kmaxEntities)
hiddenLayer = HiddenLayer(rng=rng, n_in=hidden_in, n_out=hiddenUnits)
in_height = 50
# filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape,
poolsize=(2, 2),
)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, flt_channels, layer1_w, layer1_h),
filter_shape=(60, flt_channels, 3, 3),
poolsize=(2, 2),
)
layer2 = LeNetConvPoolLayer(
rng,
input=layer1.output,
def prepare_network():
rng = numpy.random.RandomState(23455)
print('Preparing Theano model...')
mydatasets = load_initial_test_data()
test_set_x, test_set_y, shared_test_set_y, valid_ds = mydatasets
n_test_batches = test_set_x.get_value(borrow=True).shape[0]
# allocate symbolic variables for the data
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
# image size
layer0_w = 50
layer0_h = 50
layer1_w = (layer0_w - 4) // 2
layer1_h = (layer0_h - 4) // 2
layer2_w = (layer1_w - 2) // 2
layer2_h = (layer1_h - 2) // 2
layer3_w = (layer2_w - 2) // 2
layer3_h = (layer2_h - 2) // 2
######################
# BUILD NETWORK #
######################
# image sizes
batchsize = 1
in_channels = 20
in_width = 50
in_height = 50
#filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape,
poolsize=(2, 2))
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batchsize, flt_channels, layer1_w,
layer1_h),
filter_shape=(60, flt_channels, 3, 3),
poolsize=(2, 2))
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(batchsize, 60, layer2_w,
layer2_h),
filter_shape=(90, 60, 3, 3),
poolsize=(2, 2))
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=90 * layer3_w * layer3_h,
n_out=500,
activation=T.tanh)
layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=8)
cost = layer4.negative_log_likelihood(y)
classify = theano.function(
[index],
outputs=layer4.get_output_labels(y),
givens={
x: test_set_x[index * batchsize:(index + 1) * batchsize],
y: test_set_y[index * batchsize:(index + 1) * batchsize]
})
print('Loading network weights...')
weightFile = '../live_count/weights.save'
f = open(weightFile, 'rb')
loaded_objects = []
for i in range(5):
loaded_objects.append(pickle.load(f))
f.close()
layer0.__setstate__(loaded_objects[0])
layer1.__setstate__(loaded_objects[1])
layer2.__setstate__(loaded_objects[2])
layer3.__setstate__(loaded_objects[3])
layer4.__setstate__(loaded_objects[4])
return test_set_x, test_set_y, shared_test_set_y, valid_ds, classify, batchsize
def __init__(self, configfile, train=False):
self.slotList = [
"N", "per:age", "per:alternate_names", "per:children",
"per:cause_of_death", "per:date_of_birth", "per:date_of_death",
"per:employee_or_member_of", "per:location_of_birth",
"per:location_of_death", "per:locations_of_residence",
"per:origin", "per:schools_attended", "per:siblings", "per:spouse",
"per:title", "org:alternate_names", "org:date_founded",
"org:founded_by", "org:location_of_headquarters", "org:members",
"org:parents", "org:top_members_employees"
]
typeList = [
"O", "PERSON", "LOCATION", "ORGANIZATION", "DATE", "NUMBER"
]
self.config = readConfig(configfile)
self.addInputSize = 1
logger.info("additional mlp input")
wordvectorfile = self.config["wordvectors"]
logger.info("wordvectorfile " + wordvectorfile)
networkfile = self.config["net"]
logger.info("networkfile " + networkfile)
hiddenunits = int(self.config["hidden"])
logger.info("hidden units " + str(hiddenunits))
hiddenunitsNer = hiddenunits
if "hiddenunitsNER" in self.config:
hiddenunitsNer = int(self.config["hiddenunitsNER"])
representationsizeNER = 50
if "representationsizeNER" in self.config:
representationsizeNER = int(self.config["representationsizeNER"])
learning_rate = float(self.config["lrate"])
logger.info("learning rate " + str(learning_rate))
if train:
self.batch_size = int(self.config["batchsize"])
else:
self.batch_size = 1
logger.info("batch size " + str(self.batch_size))
self.filtersize = [1, int(self.config["filtersize"])]
nkerns = [int(self.config["nkerns"])]
logger.info("nkerns " + str(nkerns))
pool = [1, int(self.config["kmax"])]
self.contextsize = int(self.config["contextsize"])
logger.info("contextsize " + str(self.contextsize))
if self.contextsize < self.filtersize[1]:
logger.info("setting filtersize to " + str(self.contextsize))
self.filtersize[1] = self.contextsize
logger.info("filtersize " + str(self.filtersize))
sizeAfterConv = self.contextsize - self.filtersize[1] + 1
sizeAfterPooling = -1
if sizeAfterConv < pool[1]:
logger.info("setting poolsize to " + str(sizeAfterConv))
pool[1] = sizeAfterConv
sizeAfterPooling = pool[1]
logger.info("kmax pooling: k = " + str(pool[1]))
# reading word vectors
self.wordvectors, self.vectorsize = readWordvectors(wordvectorfile)
self.representationsize = self.vectorsize + 1
rng = numpy.random.RandomState(
23455
) # not relevant, parameters will be overwritten by stored model anyways
if train:
seed = rng.get_state()[1][0]
logger.info("seed: " + str(seed))
numSFclasses = 23
numNERclasses = 6
# allocate symbolic variables for the data
self.index = T.lscalar() # index to a [mini]batch
self.xa = T.matrix('xa') # left context
self.xb = T.matrix('xb') # middle context
self.xc = T.matrix('xc') # right context
self.y = T.imatrix('y') # label (only present in training)
self.yNER1 = T.imatrix(
'yNER1') # label for first entity (only present in training)
self.yNER2 = T.imatrix(
'yNER2') # label for second entity (only present in training)
ishape = [self.representationsize,
self.contextsize] # this is the size of context matrizes
######################
# BUILD ACTUAL MODEL #
######################
logger.info('... building the model')
# Reshape input matrix to be compatible with LeNetConvPoolLayer
layer0a_input = self.xa.reshape(
(self.batch_size, 1, ishape[0], ishape[1]))
layer0b_input = self.xb.reshape(
(self.batch_size, 1, ishape[0], ishape[1]))
layer0c_input = self.xc.reshape(
(self.batch_size, 1, ishape[0], ishape[1]))
y_reshaped = self.y.reshape((self.batch_size, 1))
yNER1reshaped = self.yNER1.reshape((self.batch_size, 1))
yNER2reshaped = self.yNER2.reshape((self.batch_size, 1))
# Construct convolutional pooling layer:
filter_shape = (nkerns[0], 1, self.representationsize,
self.filtersize[1])
poolsize = (pool[0], pool[1])
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
numpy.prod(poolsize))
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
# the convolution weight matrix
convW = theano.shared(numpy.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
convB = theano.shared(value=b_values, borrow=True)
self.layer0a = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0a_input,
image_shape=(self.batch_size, 1,
ishape[0], ishape[1]),
filter_shape=filter_shape,
poolsize=poolsize)
self.layer0b = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0b_input,
image_shape=(self.batch_size, 1,
ishape[0], ishape[1]),
filter_shape=filter_shape,
poolsize=poolsize)
self.layer0c = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0c_input,
image_shape=(self.batch_size, 1,
ishape[0], ishape[1]),
filter_shape=filter_shape,
poolsize=poolsize)
layer0aflattened = self.layer0a.output.flatten(2).reshape(
(self.batch_size, nkerns[0] * sizeAfterPooling))
layer0bflattened = self.layer0b.output.flatten(2).reshape(
(self.batch_size, nkerns[0] * sizeAfterPooling))
layer0cflattened = self.layer0c.output.flatten(2).reshape(
(self.batch_size, nkerns[0] * sizeAfterPooling))
layer0outputSF = T.concatenate(
[layer0aflattened, layer0bflattened, layer0cflattened], axis=1)
layer0outputSFsize = 3 * (nkerns[0] * sizeAfterPooling)
layer0outputNER1 = T.concatenate([layer0aflattened, layer0bflattened],
axis=1)
layer0outputNER2 = T.concatenate([layer0bflattened, layer0cflattened],
axis=1)
layer0outputNERsize = 2 * (nkerns[0] * sizeAfterPooling)
layer2ner1 = HiddenLayer(rng,
input=layer0outputNER1,
n_in=layer0outputNERsize,
n_out=hiddenunitsNer,
activation=T.tanh)
layer2ner2 = HiddenLayer(rng,
input=layer0outputNER2,
n_in=layer0outputNERsize,
n_out=hiddenunitsNer,
activation=T.tanh,
W=layer2ner1.W,
b=layer2ner1.b)
# concatenate additional features to sentence representation
self.additionalFeatures = T.matrix('additionalFeatures')
self.additionalFeatsShaped = self.additionalFeatures.reshape(
(self.batch_size, 1))
layer2SFinput = T.concatenate(
[layer0outputSF, self.additionalFeatsShaped], axis=1)
layer2SFinputSize = layer0outputSFsize + self.addInputSize
layer2SF = HiddenLayer(rng,
input=layer2SFinput,
n_in=layer2SFinputSize,
n_out=hiddenunits,
activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
layer3rel = LogisticRegression(input=layer2SF.output,
n_in=hiddenunits,
n_out=numSFclasses)
layer3et = LogisticRegression(input=layer2ner1.output,
n_in=hiddenunitsNer,
n_out=numNERclasses)
scoresForR1 = layer3rel.getScores(layer2SF.output)
scoresForE1 = layer3et.getScores(layer2ner1.output)
scoresForE2 = layer3et.getScores(layer2ner2.output)
self.crfLayer = CRF(numClasses=numSFclasses + numNERclasses,
rng=rng,
batchsizeVar=self.batch_size,
sequenceLength=3)
scores = T.zeros((self.batch_size, 3, numSFclasses + numNERclasses))
scores = T.set_subtensor(scores[:, 0, numSFclasses:], scoresForE1)
scores = T.set_subtensor(scores[:, 1, :numSFclasses], scoresForR1)
scores = T.set_subtensor(scores[:, 2, numSFclasses:], scoresForE2)
self.scores = scores
self.y_conc = T.concatenate([
yNER1reshaped + numSFclasses, y_reshaped,
yNER2reshaped + numSFclasses
],
axis=1)
# create a list of all model parameters
self.paramList = [
self.crfLayer.params, layer3rel.params, layer3et.params,
layer2SF.params, layer2ner1.params, self.layer0a.params
]
self.params = []
for p in self.paramList:
self.params += p
logger.info(p)
if not train:
self.gotNetwork = 1
# load parameters
if not os.path.isfile(networkfile):
logger.error("network file does not exist")
self.gotNetwork = 0
else:
save_file = open(networkfile, 'rb')
for p in self.params:
p.set_value(cPickle.load(save_file), borrow=False)
save_file.close()
self.relation_scores_global = self.crfLayer.getProbForClass(
self.scores, numSFclasses)
self.predictions_global = self.crfLayer.getPrediction(self.scores)
def train_rep(
learning_rate=0.002,
L1_reg=0.0002,
L2_reg=0.005,
n_epochs=200,
nkerns=[20, 50],
batch_size=25,
):
rng = numpy.random.RandomState(23455)
train_dir = "../out/h5/"
valid_dir = "../out/h5/"
weights_dir = "./weights/"
print("... load input data")
filename = train_dir + "rep_train_data_1.gzip.h5"
datasets = load_initial_data(filename)
train_set_x, train_set_y, shared_train_set_y = datasets
filename = valid_dir + "rep_valid_data_1.gzip.h5"
datasets = load_initial_data(filename)
valid_set_x, valid_set_y, shared_valid_set_y = datasets
mydatasets = load_initial_test_data()
test_set_x, test_set_y, shared_test_set_y, valid_ds = mydatasets
# compute number of minibatches for training, validation and testing
n_all_train_batches = 30000
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_all_train_batches /= batch_size
n_train_batches /= batch_size
n_valid_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x = T.matrix("x") # the data is presented as rasterized images
y = T.ivector("y") # the labels are presented as 1D vector of
# [int] labels
# image size
layer0_w = 50
layer0_h = 50
layer1_w = (layer0_w - 4) / 2
layer1_h = (layer0_h - 4) / 2
layer2_w = (layer1_w - 2) / 2
layer2_h = (layer1_h - 2) / 2
layer3_w = (layer2_w - 2) / 2
layer3_h = (layer2_h - 2) / 2
######################
# BUILD ACTUAL MODEL #
######################
print("... building the model")
# image sizes
batchsize = batch_size
in_channels = 20
in_width = 50
in_height = 50
# filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape,
poolsize=(2, 2),
)
# TODO: incase of flt_time < in_time the output dimension will be different
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, flt_channels, layer1_w, layer1_h),
filter_shape=(60, flt_channels, 3, 3),
poolsize=(2, 2),
)
layer2 = LeNetConvPoolLayer(
rng,
input=layer1.output,
image_shape=(batch_size, 60, layer2_w, layer2_h),
filter_shape=(90, 60, 3, 3),
poolsize=(2, 2),
)
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(
rng,
input=layer3_input,
n_in=90 * layer3_w * layer3_h,
n_out=500,
activation=T.tanh,
)
layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=8)
classify = theano.function(
[index],
outputs=layer4.get_output_labels(y),
givens={
x: test_set_x[index * batch_size : (index + 1) * batch_size],
y: test_set_y[index * batch_size : (index + 1) * batch_size],
},
)
validate_model = theano.function(
[index],
layer4.errors(y),
givens={
x: valid_set_x[index * batch_size : (index + 1) * batch_size],
y: valid_set_y[index * batch_size : (index + 1) * batch_size],
},
)
# create a list of all model parameters to be fit by gradient descent
params = (
layer4.params + layer3.params + layer2.params + layer1.params + layer0.params
)
# symbolic Theano variable that represents the L1 regularization term
L1 = (
T.sum(abs(layer4.params[0]))
+ T.sum(abs(layer3.params[0]))
+ T.sum(abs(layer2.params[0]))
+ T.sum(abs(layer1.params[0]))
+ T.sum(abs(layer0.params[0]))
)
# symbolic Theano variable that represents the squared L2 term
L2_sqr = (
T.sum(layer4.params[0] ** 2)
+ T.sum(layer3.params[0] ** 2)
+ T.sum(layer2.params[0] ** 2)
+ T.sum(layer1.params[0] ** 2)
+ T.sum(layer0.params[0] ** 2)
)
# the loss
cost = layer4.negative_log_likelihood(y) + L1_reg * L1 + L2_reg * L2_sqr
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
updates = []
for param_i, grad_i in zip(params, grads):
updates.append((param_i, param_i - learning_rate * grad_i))
train_model = theano.function(
[index],
cost,
updates=updates,
givens={
x: train_set_x[index * batch_size : (index + 1) * batch_size],
y: train_set_y[index * batch_size : (index + 1) * batch_size],
},
)
###############
# TRAIN MODEL #
###############
print("... training")
start_time = time.clock()
epoch = 0
done_looping = False
cost_ij = 0
train_files_num = 600
val_files_num = 100
startc = time.clock()
while (epoch < n_epochs) and (not done_looping):
endc = time.clock()
print(("epoch %i, took %.2f minutes" % (epoch, (endc - startc) / 60.0)))
startc = time.clock()
epoch = epoch + 1
for nTrainSet in range(1, train_files_num + 1):
# load next train data
if nTrainSet % 50 == 0:
print("training @ nTrainSet = ", nTrainSet, ", cost = ", cost_ij)
filename = train_dir + "rep_train_data_" + str(nTrainSet) + ".gzip.h5"
datasets = load_next_data(filename)
ns_train_set_x, ns_train_set_y = datasets
train_set_x.set_value(ns_train_set_x, borrow=True)
shared_train_set_y.set_value(
numpy.asarray(ns_train_set_y, dtype=theano.config.floatX), borrow=True
)
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
# train
for minibatch_index in range(n_train_batches):
# training itself
# --------------------------------------
cost_ij = train_model(minibatch_index)
# -------------------------
# at the end of each epoch run validation
this_validation_loss = 0
for nValSet in range(1, val_files_num + 1):
filename = valid_dir + "rep_valid_data_" + str(nValSet) + ".gzip.h5"
datasets = load_next_data(filename)
ns_valid_set_x, ns_valid_set_y = datasets
valid_set_x.set_value(ns_valid_set_x, borrow=True)
shared_valid_set_y.set_value(
numpy.asarray(ns_valid_set_y, dtype=theano.config.floatX), borrow=True
)
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
n_valid_batches /= batch_size
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i in range(n_valid_batches)]
this_validation_loss += numpy.mean(validation_losses)
this_validation_loss /= val_files_num
print((
"epoch %i, minibatch %i/%i, validation error %f %%"
% (
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.0,
)
))
# save snapshots
print("saving weights state, epoch = ", epoch)
f = file(weights_dir + "weights_epoch" + str(epoch) + ".save", "wb")
state_L0 = layer0.__getstate__()
pickle.dump(state_L0, f, protocol=pickle.HIGHEST_PROTOCOL)
state_L1 = layer1.__getstate__()
pickle.dump(state_L1, f, protocol=pickle.HIGHEST_PROTOCOL)
state_L2 = layer2.__getstate__()
pickle.dump(state_L2, f, protocol=pickle.HIGHEST_PROTOCOL)
state_L3 = layer3.__getstate__()
pickle.dump(state_L3, f, protocol=pickle.HIGHEST_PROTOCOL)
state_L4 = layer4.__getstate__()
pickle.dump(state_L4, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
end_time = time.clock()
print ("Optimization complete.")
print((
"The code for file "
+ os.path.split(__file__)[1]
+ " ran for %.2fm" % ((end_time - start_time) / 60.0)
), file=sys.stderr)
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
# the convolution weight matrix
convW = theano.shared(numpy.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
name='conv_W',
borrow=True)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
convB = theano.shared(value=b_values, name='conv_b', borrow=True)
layer0 = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0_input,
filter_shape=filter_shape,
poolsize=poolsize)
layer0flattened = layer0.output.flatten(2).reshape(
(batch_size_var, nkerns[0] * sizeAfterPooling))
layer0outputsize = nkerns[0] * sizeAfterPooling
if "internalOnH" in attentionMethod:
layer1 = AttentionLayer(rng,
thisInput=layer0.conv_out_tanh,
batchsize=batch_size_var,
dim1=nkerns[0],
dim2=sizeAfterConv,
method=attentionMethod,
k=kattention)
def main(args):
# initial parameters
embedding_size = args.embedding_size
mention_context_size = args.mention_context_size
type_context_size = args.type_context_size
embedding_file = args.embedding_path
hidden_units = args.hidden_units
learning_rate = args.learning_rate
margin = args.margin
batch_size = args.batch_size
n_epochs = args.num_epochs
relation_size = 82
nkerns = [500]
filter_size = [1, 1]
pool = [1, 1]
l1 = 0.000001
l2 = 0.000002
newbob = False
network_file = args.model_path
test_file = args.test
test_result_file = args.test_result
label_file = args.ontology_path
label_file_norm = args.norm_ontology_path
relation_file = args.relation_path
train_type_flag = args.seen_types
tup_representation_size = embedding_size * 2
# load word vectors
word_vectors, vector_size = load_word_vec(embedding_file)
# read train and dev file
print("start loading train and dev file ... ")
doc_id_list_test, type_list_test, trigger_list_test, left_word_list_test, relation_list_test, \
right_word_list_test = load_training_data(test_file)
print("start loading arg and relation files ... ")
all_type_list, all_type_structures = load_types_1(label_file_norm)
rel_index, index_rel = read_relation_index(relation_file)
type_size = len(all_type_list)
# using a matrix to represent each relation
relation_matrix = random_init_rel_vec_factor(
relation_file, tup_representation_size * tup_representation_size)
train_types = get_types_for_train(train_type_flag, label_file)
# prepare data structure
print("start preparing data structures ... ")
curSeed = 23455
rng = numpy.random.RandomState(curSeed)
seed = rng.get_state()[1][0]
print("seed: ", seed)
result_index_test_matrix, result_vector_test_matrix, input_context_test_matrix, input_trigger_test_matrix, \
relation_binary_test_matrix, pos_neg_test_matrix = input_matrix_1_test(
type_list_test, trigger_list_test, left_word_list_test, relation_list_test, right_word_list_test,
embedding_size, mention_context_size, relation_size, label_file, word_vectors, rel_index, train_type_flag)
input_type_matrix, input_type_structure_matrix = type_matrix(
all_type_list, all_type_structures, embedding_file, type_context_size)
time1 = time.time()
dt = theano.config.floatX
test_set_content = theano.shared(
numpy.matrix(input_context_test_matrix, dtype=dt))
test_set_trigger = theano.shared(
numpy.matrix(input_trigger_test_matrix, dtype=dt))
test_set_relation_binary = theano.shared(
numpy.matrix(relation_binary_test_matrix, dtype=dt))
test_set_posneg = theano.shared(numpy.matrix(pos_neg_test_matrix,
dtype=dt))
test_set_y = theano.shared(
numpy.array(result_index_test_matrix, dtype=numpy.dtype(numpy.int32)))
test_set_y_vector = theano.shared(
numpy.matrix(result_vector_test_matrix, dtype=dt))
train_set_type = theano.shared(numpy.matrix(input_type_matrix, dtype=dt))
train_set_type_structure = theano.shared(
numpy.matrix(input_type_structure_matrix, dtype=dt))
train_types = theano.shared(numpy.matrix(train_types, dtype=dt))
# compute number of minibatches for training, validation and testing
n_test_batches = input_trigger_test_matrix.shape[0]
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x_content = T.matrix(
'x_content') # the data is presented as rasterized images
x_trigger = T.matrix(
'x_trigger') # the data is presented as rasterized images
x_relation_binary = T.matrix('x_relation_binary')
x_pos_neg_flag = T.matrix('x_pos_neg_flag')
x_type = T.matrix('x_type')
x_type_structure = T.matrix('x_type_structure')
y = T.ivector('y') # the labels are presented as 1D vector of
y_vector = T.matrix('y_vector') # the labels are presented as 1D vector of
x_train_types = T.matrix('x_train_types')
# [int] labels
i_shape = [tup_representation_size,
mention_context_size] # this is the size of context matrizes
time2 = time.time()
print("time for preparing data structures: ", time2 - time1)
# build actual model
print('start building the model ... ')
time1 = time.time()
rel_w = theano.shared(value=relation_matrix, borrow=True) ## 26*400
# Construct the mention structure input Layer
layer0_input = x_content.reshape((batch_size, 1, i_shape[0], i_shape[1]))
layer0_input_binary_relation = x_relation_binary.reshape(
(batch_size, 1, relation_size, i_shape[1])) ## 100*1*26*5
# compose amr relation matrix to each tuple
compose_layer = ComposeLayerMatrix(
input=layer0_input,
input_binary_relation=layer0_input_binary_relation,
rel_w=rel_w,
rel_vec_size=tup_representation_size)
layer1_input = compose_layer.output
# initialize the convolution weight matrix
filter_shape = (nkerns[0], 1, tup_representation_size, filter_size[1])
pool_size = (pool[0], pool[1])
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
numpy.prod(pool_size))
w_bound = numpy.sqrt(6. / (fan_in + fan_out))
conv_w = theano.shared(numpy.asarray(rng.uniform(low=-w_bound,
high=w_bound,
size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
conv_b = theano.shared(value=b_values, borrow=True)
# conv with pool layer
layer1_conv = LeNetConvPoolLayer(rng,
W=conv_w,
b=conv_b,
input=layer1_input,
image_shape=(batch_size, 1, i_shape[0],
i_shape[1]),
filter_shape=filter_shape,
poolsize=pool_size)
layer1_output = layer1_conv.output
layer1_flattened = layer1_output.flatten(2)
trigger_features_shaped = x_trigger.reshape((batch_size, embedding_size))
layer2_input = T.concatenate([layer1_flattened, trigger_features_shaped],
axis=1)
# Construct the type structure input Layer
layer_type_input = x_type_structure.reshape(
(type_size, 1, tup_representation_size, type_context_size))
filter_shape_type = (nkerns[0], 1, tup_representation_size, filter_size[1])
pool_size_type = (pool[0], pool[1])
# initialize the implicit relation tensor
type_tensor_shape = (tup_representation_size, tup_representation_size,
tup_representation_size)
type_tensor_w = theano.shared(numpy.asarray(rng.uniform(
low=-w_bound, high=w_bound, size=type_tensor_shape),
dtype=theano.config.floatX),
borrow=True)
# compose relation tensor to each tuple
compose_type_layer = ComposeLayerTensor(input=layer_type_input,
tensor=type_tensor_w)
layer_type_input1 = compose_type_layer.output
# conv with pool layer
layer1_conv_type = LeNetConvPoolLayer(rng,
W=conv_w,
b=conv_b,
input=layer_type_input1,
image_shape=(type_size, 1,
tup_representation_size,
type_context_size),
filter_shape=filter_shape_type,
poolsize=pool_size_type)
layer1_type_output = layer1_conv_type.output
layer1_type_flattened = layer1_type_output.flatten(2)
types_shaped = x_type.reshape((type_size, embedding_size))
layer2_type_input = T.concatenate([layer1_type_flattened, types_shaped],
axis=1)
layer2_type_input_size = nkerns[0]**pool[1] + embedding_size
# ranking based max margin loss layer
train_types_signal = x_train_types.reshape((type_size, 1))
pos_neg_flag = x_pos_neg_flag.reshape((batch_size, 1))
layer3 = MaxRankingMarginCosine1(rng=rng,
input=layer2_input,
input_label=layer2_type_input,
true_label=y_vector,
n_in=layer2_type_input_size,
margin=margin,
batch_size=batch_size,
type_size=type_size,
train_type_signal=train_types_signal,
pos_neg_flag=pos_neg_flag)
cost = layer3.loss
# create a list of all model parameters to be fit by gradient descent
param_list = [
compose_layer.params, layer1_conv.params, compose_type_layer.params
]
params = []
for p in param_list:
params += p
# the cost we minimize during training is the NLL of the model
lambd1 = T.scalar('lambda1', dt)
lambd2 = T.scalar('lambda2', dt)
# L1 and L2 regularization possible
reg2 = 0
reg1 = 0
for p in param_list:
reg2 += T.sum(p[0]**2)
reg1 += T.sum(abs(p[0]))
cost += lambd2 * reg2
cost += lambd1 * reg1
lr = T.scalar('lr', dt)
start = index * batch_size
end = (index + 1) * batch_size
testVariables = {}
testVariables[x_content] = test_set_content[start:end]
testVariables[x_trigger] = test_set_trigger[start:end]
testVariables[x_relation_binary] = test_set_relation_binary[start:end]
testVariables[x_type] = train_set_type
testVariables[x_type_structure] = train_set_type_structure
testVariables[y] = test_set_y[start:end]
testVariables[y_vector] = test_set_y_vector[start:end]
testVariables[x_train_types] = train_types
testVariables[x_pos_neg_flag] = test_set_posneg[start:end]
print("length of train variables ", len(testVariables))
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by SGD Since this model has many parameters,
# it would be tedious to manually create an update rule for each model parameter. We thus create the updates
# list by automatically looping over all (params[i],grads[i]) pairs.
updates = []
for param_i, grad_i in zip(params, grads):
updates.append((param_i, param_i - lr * grad_i))
test_model_confidence = theano.function([index],
layer3.results(y),
on_unused_input='ignore',
givens=testVariables)
time2 = time.time()
print("time for building the model: ", time2 - time1)
print("loading saved network")
netfile = open(network_file)
relW = cPickle.load(netfile)
compose_layer.params[0].set_value(relW, borrow=True)
convolW = cPickle.load(netfile)
convolB = cPickle.load(netfile)
layer1_conv.params[0].set_value(convolW, borrow=True)
layer1_conv.params[1].set_value(convolB, borrow=True)
layer1_conv_type.params[0].set_value(convolW, borrow=True)
layer1_conv_type.params[1].set_value(convolB, borrow=True)
typeW = cPickle.load(netfile)
compose_type_layer.params[0].set_value(typeW, borrow=True)
netfile.close()
print("finish loading network")
test_batch_size = 100
all_batches = len(result_index_test_matrix) / test_batch_size
confidence_prob = []
confidence_value = []
confidence_list = []
confidence = [test_model_confidence(i) for i in xrange(all_batches)]
for r in range(0, len(confidence)):
for r1 in range(0, test_batch_size):
hypo_result = confidence[r][0].item(r1)
confidence_prob.append(confidence[r][2][r1])
confidence_value.append(confidence[r][1][r1])
confidence_list.append(hypo_result)
y_pred = confidence_list
f = open(test_result_file, "w")
for i in range(0, len(y_pred)):
f.write(str(y_pred[i]) + "\t" + str(confidence_value[i]) + "\t")
for j in range(0, type_size):
f.write(str(confidence_prob[i][j]) + " ")
f.write("\n")
f.close()
def build_model(self, flag_preserve_params=False):
###################
# build the model #
logging.info('... building the model')
# allocate symbolic variables for the data
self.index = T.lscalar() # index to a [mini]batch
self.x = T.matrix('x') # the data is presented as rasterized images
# self.y = T.ivector('y')
# the labels are presented as 1D vector of
# [int] labels, used to represent labels given by
# data
# the y as features, used for taking in intermediate layer "y" values
self.y = T.matrix('y')
# Reshape matrix of rasterized images of shape (batch_size,28*28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
self.layer0_input = self.x.reshape((self.batch_size, self.img_dim, self.img_size, self.img_size))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1,28-5+1)=(24,24)
# maxpooling reduces this further to (24/2,24/2) = (12,12)
# 4D output tensor is thus of shape (batch_size,nkerns[0],12,12)
self.layer0 = LeNetConvPoolLayer(self.rng, input=self.layer0_input,
image_shape=(self.batch_size, self.img_dim, self.img_size, self.img_size),
filter_shape=(self.nkerns[0], self.img_dim,
self.filtersize[0], self.filtersize[0]),
poolsize=(self.poolsize[0], self.poolsize[0]),
activation=self.conv_activation)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1,12-5+1)=(8,8)
# maxpooling reduces this further to (8/2,8/2) = (4,4)
# 4D output tensor is thus of shape (nkerns[0],nkerns[1],4,4)
self.img_size1 = (self.img_size - self.filtersize[0] + 1) / self.poolsize[0]
self.layer1 = LeNetConvPoolLayer(self.rng, input=self.layer0.output,
image_shape=(self.batch_size, self.nkerns[0],
self.img_size1, self.img_size1),
filter_shape=(self.nkerns[1], self.nkerns[0],
self.filtersize[1], self.filtersize[1]),
poolsize=(self.poolsize[1], self.poolsize[1]),
activation=self.conv_activation)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size,num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (20,32*4*4) = (20,512)
self.layer2_input = self.layer1.output.flatten(2)
self.img_size2 = (self.img_size1 - self.filtersize[1] + 1) / self.poolsize[1]
# construct a fully-connected sigmoidal layer
self.layer2 = HiddenLayer(self.rng, input=self.layer2_input,
n_in=self.nkerns[1] * self.img_size2 * self.img_size2,
n_out=self.num_hidden,
activation=self.hidden_activation)
# classify the values of the fully-connected sigmoidal layer
self.layer3 = LogisticRegression(input=self.layer2.output,
n_in=self.num_hidden,
n_out=self.num_class,
activation=self.logreg_activation)
# regularization term
self.decay_hidden = self.alpha_l1 * abs(self.layer2.W).sum() + \
self.alpha_l2 * (self.layer2.W ** 2).sum()
self.decay_softmax = self.alpha_l1 * abs(self.layer3.W).sum() + \
self.alpha_l2 * (self.layer3.W ** 2).sum()
# there's different choices of cost models
if self.cost_type == 'nll_softmax':
# the cost we minimize during training is the NLL of the model
self.y = T.ivector('y') # index involved so has to use integer
self.cost = self.layer3.negative_log_likelihood(self.y) + \
self.decay_hidden + self.decay_softmax + \
self.alpha_entropy * self.layer3.p_y_entropy
elif self.cost_type == 'ssd_softmax':
self.cost = T.mean((self.layer3.p_y_given_x - self.y) ** 2) + \
self.decay_hidden + self.decay_softmax
elif self.cost_type == 'ssd_hidden':
self.cost = T.mean((self.layer2.output - self.y) ** 2) + \
self.decay_hidden
elif self.cost_type == 'ssd_conv':
self.cost = T.mean((self.layer2_input - self.y) ** 2)
# create a list of all model parameters to be fit by gradient descent
# preserve parameters if the exist, used for keep parameter while
# changing
# some of the theano functions
# but the user need to be aware that if the parameters should be kept
# only if the network structure doesn't change
if flag_preserve_params and hasattr(self, 'params'):
pass
params_temp = copy.deepcopy(self.params)
else:
params_temp = None
self.params = self.layer3.params + self.layer2.params + self.layer1.params + self.layer0.params
# if needed, assign old parameters
if flag_preserve_params and (params_temp is not None):
for ind in range(len(params_temp)):
self.params[ind].set_value(params_temp[ind].get_value(), borrow=True)
# create a list of gradients for all model parameters
self.grads = T.grad(self.cost, self.params, disconnected_inputs='warn')
# error function from the last layer logistic regression
self.errors = self.layer3.errors
def build_lenet(config):
rng = np.random.RandomState(23455)
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector
image_width = config.image_width
batch_size = config.batch_size
image_size = image_width**2
x_shared = T.cast(theano.shared(np.random.rand(batch_size, image_size),
borrow=True), theano.config.floatX)
y_shared = T.cast(theano.shared(np.random.randint(config.ydim,
size=batch_size),
borrow=True), 'int32')
layer0_input = x.reshape((batch_size, 1, image_width, image_width))
# construct the first convolutional pooling layer
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, image_width, image_width),
filter_shape=(config.num_kerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# construct the second convolutional pooling layer
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, config.num_kerns[0], 12, 12),
filter_shape=(config.num_kerns[1], config.num_kerns[0], 5, 5),
poolsize=(2, 2)
)
layer2_input = layer1.output.flatten(2)
# construct a fully-connected layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=config.num_kerns[1] * 4 * 4,
n_out=500,
activation=relu
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500,
n_out=config.ydim)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
# create a list of all model parameters to be fit by gradient descent
params_W = [layer3.W, layer2.W, layer1.W, layer0.W]
params_b = [layer3.b, layer2.b, layer1.b, layer0.b]
params = params_W + params_b
shared_cost = theano.shared(np.float32(0.0))
grads_temp = T.grad(cost, params)
start_compilation = time.time()
forward_step = theano.function([], [], updates=[(shared_cost, cost)],
givens={x: x_shared, y: y_shared})
forward_backward_step = theano.function([], grads_temp,
givens={x: x_shared, y: y_shared})
print 'compilation time: %.4f s' % (time.time() - start_compilation)
return forward_step, forward_backward_step
# the convolution weight matrix
convW = theano.shared(numpy.asarray(rng.uniform(low=-W_bound,
high=W_bound,
size=filter_shape),
dtype=theano.config.floatX),
name='conv_W',
borrow=True)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
convB = theano.shared(value=b_values, name='conv_b', borrow=True)
layer0a = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0a_input,
image_shape=(xa.shape[0], 1, ishape[0],
ishape[1]),
filter_shape=filter_shape,
poolsize=poolsize)
layer0b = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0b_input,
image_shape=(xb.shape[0], 1, ishape[0],
ishape[1]),
filter_shape=filter_shape,
poolsize=poolsize)
layer0c = LeNetConvPoolLayer(rng,
W=convW,
b=convB,
input=layer0c_input,
def main(args):
# initial parameters
embedding_size = args.embedding_size
arg_context_size = args.arg_context_size
role_context_size = args.role_context_size
embedding_file = args.embedding_path
hidden_units = args.hidden_units
learning_rate = args.learning_rate
margin = args.margin
batch_size = args.batch_size
n_epochs = args.num_epochs
relation_size = 82
nkerns = [500]
filter_size = [1, 1]
pool = [1, 1]
l1 = 0.001
l2 = 0.002
newbob = False
arg_network_file = args.model_path
arg_train_file = args.train
arg_dev_file = args.dev
arg_test_file = args.test
arg_label_file = args.ontology_path
arg_label_file_norm = args.norm_ontology_path
relation_file = args.relation_path
train_role_flag = args.seen_args
arg_path_file_merge = args.arg_path_file
arg_path_file_universal = args.arg_path_file_universal
trigger_role_matrix_file = args.trigger_role_matrix
tup_representation_size = embedding_size * 2
# load word vectors
word_vectors, vector_size = load_word_vec(embedding_file)
# read train and dev file
print("start loading train and dev file ... ")
arg_trigger_list_train, arg_trigger_type_list_train, arg_list_train, arg_path_left_list_train, \
arg_path_rel_list_train, arg_path_right_list_train, arg_role_list_train = load_arg_data(arg_train_file)
arg_trigger_list_dev, arg_trigger_type_list_dev, arg_list_dev, arg_path_left_list_dev, \
arg_path_rel_list_dev, arg_path_right_list_dev, arg_role_list_dev = load_arg_data(arg_dev_file)
arg_trigger_list_test, arg_trigger_type_list_test, arg_list_test, arg_path_left_list_test, \
arg_path_rel_list_test, arg_path_right_list_test, arg_role_list_test = load_arg_data(arg_test_file)
num_examples_per_epoch = len(arg_trigger_list_train)
print("start loading arg and relation files ... ")
all_type_list, all_type_structures = load_types(arg_label_file_norm)
type_size = len(all_type_list)
all_arg_role_list, all_type_role_structures, index_2_role, trigger_role_2_index, index_2_norm_role, \
trigger_norm_role_2_index = load_roles_1(arg_path_file_merge)
role_size = len(all_arg_role_list)
trigger_role_matrix = get_trigger_arg_matrix(trigger_role_matrix_file,
type_size, role_size)
train_roles = get_roles_for_train_1(train_role_flag, arg_path_file_merge)
rel_2_index, index_2_rel = read_relation_index(relation_file)
relation_matrix = random_init_rel_vec_factor(
relation_file, tup_representation_size * tup_representation_size)
print("start preparing data structures ... ")
curSeed = 23455
rng = numpy.random.RandomState(curSeed)
seed = rng.get_state()[1][0]
print("seed: ", seed)
# arg data matrix
role_index_train_matrix, role_vector_train_matrix, input_arg_context_train_matrix, input_arg_train_matrix, \
arg_relation_binary_train_matrix, pos_neg_role_train_matrix, limited_roles_train_matrix = \
input_arg_matrix(arg_trigger_list_train, arg_trigger_type_list_train, arg_list_train, arg_path_left_list_train,
arg_path_rel_list_train, arg_path_right_list_train, arg_role_list_train, word_vectors,
all_arg_role_list, trigger_role_2_index, vector_size, arg_context_size, relation_size,
rel_2_index, train_roles, trigger_role_matrix, arg_label_file)
role_index_dev_matrix, role_vector_dev_matrix, input_arg_context_dev_matrix, input_arg_dev_matrix, \
arg_relation_binary_dev_matrix, pos_neg_role_dev_matrix, limited_roles_dev_matrix = \
input_arg_matrix_test(arg_trigger_list_dev, arg_trigger_type_list_dev, arg_list_dev, arg_path_left_list_dev,
arg_path_rel_list_dev,arg_path_right_list_dev, arg_role_list_dev, word_vectors,
all_arg_role_list, trigger_role_2_index, vector_size, arg_context_size, relation_size,
rel_2_index, train_roles, trigger_role_matrix, arg_label_file)
role_index_test_matrix, role_vector_test_matrix, input_arg_context_test_matrix, input_arg_test_matrix, \
arg_relation_binary_test_matrix, pos_neg_role_test_matrix, limited_roles_test_matrix = \
input_arg_matrix_test(arg_trigger_list_test, arg_trigger_type_list_test, arg_list_test, arg_path_left_list_test,
arg_path_rel_list_test, arg_path_right_list_test, arg_role_list_test, word_vectors,
all_arg_role_list, trigger_role_2_index, vector_size, arg_context_size, relation_size,
rel_2_index, train_roles, trigger_role_matrix, arg_label_file)
input_role_matrix, input_role_structure_matrix = role_matrix_1(
all_arg_role_list, all_type_role_structures, embedding_file,
role_context_size)
time1 = time.time()
dt = theano.config.floatX
## arg data
train_set_content_arg = theano.shared(
numpy.matrix(input_arg_context_train_matrix, dtype=dt))
valid_set_content_arg = theano.shared(
numpy.matrix(input_arg_context_dev_matrix, dtype=dt))
test_set_content_arg = theano.shared(
numpy.matrix(input_arg_context_test_matrix, dtype=dt))
train_set_arg = theano.shared(
numpy.matrix(input_arg_train_matrix, dtype=dt))
valid_set_arg = theano.shared(numpy.matrix(input_arg_dev_matrix, dtype=dt))
test_set_arg = theano.shared(numpy.matrix(input_arg_test_matrix, dtype=dt))
train_set_relation_binary_arg = theano.shared(
numpy.matrix(arg_relation_binary_train_matrix, dtype=dt))
valid_set_relation_binary_arg = theano.shared(
numpy.matrix(arg_relation_binary_dev_matrix, dtype=dt))
test_set_relation_binary_arg = theano.shared(
numpy.matrix(arg_relation_binary_test_matrix, dtype=dt))
train_set_posneg_arg = theano.shared(
numpy.matrix(pos_neg_role_train_matrix, dtype=dt))
valid_set_posneg_arg = theano.shared(
numpy.matrix(pos_neg_role_dev_matrix, dtype=dt))
test_set_posneg_arg = theano.shared(
numpy.matrix(pos_neg_role_test_matrix, dtype=dt))
train_set_arg_y = theano.shared(
numpy.array(role_index_train_matrix, dtype=numpy.dtype(numpy.int32)))
valid_set_arg_y = theano.shared(
numpy.array(role_index_dev_matrix, dtype=numpy.dtype(numpy.int32)))
test_set_arg_y = theano.shared(
numpy.array(role_index_test_matrix, dtype=numpy.dtype(numpy.int32)))
train_set_arg_y_vector = theano.shared(
numpy.matrix(role_vector_train_matrix, dtype=dt))
valid_set_arg_y_vector = theano.shared(
numpy.matrix(role_vector_dev_matrix, dtype=dt))
test_set_arg_y_vector = theano.shared(
numpy.matrix(role_vector_test_matrix, dtype=dt))
train_set_arg_limited_role = theano.shared(
numpy.matrix(limited_roles_train_matrix, dtype=dt))
valid_set_arg_limited_role = theano.shared(
numpy.matrix(limited_roles_dev_matrix, dtype=dt))
test_set_arg_limited_role = theano.shared(
numpy.matrix(limited_roles_test_matrix, dtype=dt))
train_set_role = theano.shared(numpy.matrix(input_role_matrix, dtype=dt))
train_set_role_structure = theano.shared(
numpy.matrix(input_role_structure_matrix, dtype=dt))
train_roles = theano.shared(numpy.matrix(train_roles, dtype=dt))
# compute number of minibatches for training, validation and testing
n_train_batches = input_arg_train_matrix.shape[0]
n_valid_batches = input_arg_dev_matrix.shape[0]
n_test_batches = input_arg_test_matrix.shape[0]
n_train_batches /= batch_size
n_valid_batches /= batch_size
n_test_batches /= batch_size
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
x_content_arg = T.matrix('x_content_arg')
x_arg = T.matrix('x_arg')
x_relation_binary_arg = T.matrix('x_relation_binary_arg')
x_pos_neg_flag_arg = T.matrix('x_pos_neg_flag_arg')
x_role = T.matrix('x_role')
x_role_structure = T.matrix('x_role_structure')
x_train_roles = T.matrix('x_train_roles')
arg_y = T.ivector('arg_y')
arg_y_vector = T.matrix('arg_y_vector')
arg_limited_role = T.matrix('arg_limited_role')
# [int] labels
ishape = [tup_representation_size,
arg_context_size] # this is the size of context matrizes
time2 = time.time()
print("time for preparing data structures: ", time2 - time1)
# build the actual model
print('start building the model ... ')
time1 = time.time()
# argument representation layer
layer0_arg_input = x_content_arg.reshape(
(batch_size, 1, ishape[0], ishape[1]))
layer0_input_binary_relation = x_relation_binary_arg.reshape(
(batch_size, 1, relation_size, ishape[1])) ## 100*1*26*5
# compose amr relation matrix to each tuple
rel_w = theano.shared(value=relation_matrix, borrow=True) ## 26*400
compose_layer = ComposeLayerMatrix(
input=layer0_arg_input,
input_binary_relation=layer0_input_binary_relation,
rel_w=rel_w,
rel_vec_size=tup_representation_size)
layer1_input = compose_layer.output
filter_shape = (nkerns[0], 1, tup_representation_size, filter_size[1])
pool_size = (pool[0], pool[1])
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
numpy.prod(pool_size))
w_bound = numpy.sqrt(6. / (fan_in + fan_out))
conv_w = theano.shared(numpy.asarray(rng.uniform(low=-w_bound,
high=w_bound,
size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
b_values = numpy.zeros((filter_shape[0], ), dtype=theano.config.floatX)
conv_b = theano.shared(value=b_values, borrow=True)
layer1_arg_conv = LeNetConvPoolLayer(rng,
W=conv_w,
b=conv_b,
input=layer1_input,
image_shape=(batch_size, 1, ishape[0],
arg_context_size),
filter_shape=filter_shape,
poolsize=pool_size)
layer1_arg_output = layer1_arg_conv.output
layer1_arg_flattened = layer1_arg_output.flatten(2)
arg_features_shaped = x_arg.reshape((batch_size, embedding_size))
layer2_arg_input = T.concatenate(
[layer1_arg_flattened, arg_features_shaped], axis=1)
layer2_arg_input_size = nkerns[0] * pool[1] + embedding_size
# arg role representation layer
layer_role_input = x_role_structure.reshape(
(role_size, 1, tup_representation_size, role_context_size))
filter_shape_role = (nkerns[0], 1, tup_representation_size, filter_size[1])
pool_size_role = (pool[0], pool[1])
# initialize the implicit relation tensor
type_tensor_shape = (tup_representation_size, tup_representation_size,
tup_representation_size)
type_tensor_w = theano.shared(numpy.asarray(rng.uniform(
low=-w_bound, high=w_bound, size=type_tensor_shape),
dtype=theano.config.floatX),
borrow=True)
# compose relation tensor to each tuple
compose_type_layer = ComposeLayerTensor(input=layer_role_input,
tensor=type_tensor_w)
layer_type_input1 = compose_type_layer.output
layer1_conv_role = LeNetConvPoolLayer(rng,
W=conv_w,
b=conv_b,
input=layer_type_input1,
image_shape=(role_size, 1,
tup_representation_size,
role_context_size),
filter_shape=filter_shape_role,
poolsize=pool_size_role)
layer1_role_output = layer1_conv_role.output
layer1_role_flattened = layer1_role_output.flatten(2)
role_shaped = x_role.reshape((role_size, embedding_size))
layer2_role_input = T.concatenate([layer1_role_flattened, role_shaped],
axis=1)
layer2_role_input_size = nkerns[0]**pool[1] + embedding_size
# ranking based max margin loss layer
train_roles_signal = x_train_roles.reshape((role_size, 1))
pos_neg_flag_arg = x_pos_neg_flag_arg.reshape((batch_size, 1))
limited_role = arg_limited_role.reshape((batch_size, role_size))
layer3 = MaxRankingMarginCosine1Arg1(rng=rng,
input=layer2_arg_input,
input_label=layer2_role_input,
true_label=arg_y_vector,
n_in=layer2_arg_input_size,
n_in2=layer2_role_input_size,
margin=margin,
batch_size=batch_size,
type_size=role_size,
train_type_signal=train_roles_signal,
pos_neg_flag=pos_neg_flag_arg,
limited_role=limited_role)
# cost and parameters update
cost = layer3.loss
# create a list of all model parameters to be fit by gradient descent
param_list = [
layer1_arg_conv.params, compose_layer.params, compose_type_layer.params
]
params = []
for p in param_list:
params += p
# the cost we minimize during training is the NLL of the model
lambd1 = T.scalar('lambda1', dt)
lambd2 = T.scalar('lambda2', dt)
# L1 and L2 regularization possible
reg2 = 0
reg1 = 0
for p in param_list:
reg2 += T.sum(p[0]**2)
reg1 += T.sum(abs(p[0]))
print("reg1 ", reg1)
print("reg2 ", reg2)
cost += lambd2 * reg2
cost += lambd1 * reg1
lr = T.scalar('lr', dt)
start = index * batch_size
end = (index + 1) * batch_size
validVariables = {}
validVariables[x_content_arg] = valid_set_content_arg[start:end]
validVariables[x_arg] = valid_set_arg[start:end]
validVariables[x_role] = train_set_role
validVariables[x_role_structure] = train_set_role_structure
validVariables[x_relation_binary_arg] = valid_set_relation_binary_arg[
start:end]
validVariables[arg_y] = valid_set_arg_y[start:end]
validVariables[arg_y_vector] = valid_set_arg_y_vector[start:end]
validVariables[x_train_roles] = train_roles
validVariables[x_pos_neg_flag_arg] = valid_set_posneg_arg[start:end]
validVariables[arg_limited_role] = valid_set_arg_limited_role[start:end]
testVariables = {}
testVariables[x_content_arg] = test_set_content_arg[start:end]
testVariables[x_arg] = test_set_arg[start:end]
testVariables[x_role] = train_set_role
testVariables[x_role_structure] = train_set_role_structure
testVariables[x_relation_binary_arg] = test_set_relation_binary_arg[
start:end]
testVariables[arg_y] = test_set_arg_y[start:end]
testVariables[arg_y_vector] = test_set_arg_y_vector[start:end]
testVariables[x_train_roles] = train_roles
testVariables[x_pos_neg_flag_arg] = test_set_posneg_arg[start:end]
testVariables[arg_limited_role] = test_set_arg_limited_role[start:end]
trainVariables = {}
trainVariables[x_content_arg] = train_set_content_arg[start:end]
trainVariables[x_arg] = train_set_arg[start:end]
trainVariables[x_role] = train_set_role
trainVariables[x_role_structure] = train_set_role_structure
trainVariables[x_relation_binary_arg] = train_set_relation_binary_arg[
start:end]
trainVariables[arg_y] = train_set_arg_y[start:end]
trainVariables[arg_y_vector] = train_set_arg_y_vector[start:end]
trainVariables[x_train_roles] = train_roles
trainVariables[x_pos_neg_flag_arg] = train_set_posneg_arg[start:end]
trainVariables[arg_limited_role] = train_set_arg_limited_role[start:end]
print("length of train variables ", trainVariables)
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by SGD Since this model has many parameters,
# it would be tedious to manually create an update rule for each model parameter. We thus create the updates
# list by automatically looping over all (params[i],grads[i]) pairs.
updates = []
rho = 0.9
epsilon = 1e-6
# for param_i, grad_i in zip(params, grads):
# updates.append((param_i, param_i - lr * grad_i))
for p, g in zip(params, grads):
acc = theano.shared(p.get_value() * 0.)
acc_new = rho * acc + (1 - rho) * g**2
gradient_scaling = T.sqrt(acc_new + epsilon)
g = g / gradient_scaling
updates.append((acc, acc_new))
updates.append((p, p - lr * g))
test_model_confidence = theano.function([index],
layer3.results(arg_y),
on_unused_input='ignore',
givens=testVariables)
eval_model_confidence = theano.function([index],
layer3.results(arg_y),
on_unused_input='ignore',
givens=validVariables)
train_model = theano.function([index, lr, lambd1, lambd2],
[cost, layer3.loss],
updates=updates,
on_unused_input='ignore',
givens=trainVariables)
time2 = time.time()
print("time for building the model: ", time2 - time1)
# train the model
print('start training ... ')
time1 = time.time()
validation_frequency = num_examples_per_epoch / batch_size # validate after each epoch
best_params = []
best_fscore = -1
last_fscore = -1
best_fscore_m1 = -1
best_iter = 0
best_fscoreEval = -1
best_fscore_m1Eval = -1
best_iterEval = 0
start_time = time.clock()
epoch = 0
done_looping = False
maxNoImprovement = 5
noImprovement = 0
while (epoch < n_epochs) and (not done_looping):
print('epoch = ', epoch)
epoch = epoch + 1
this_n_train_batches = num_examples_per_epoch / batch_size
for minibatch_index in xrange(this_n_train_batches):
iter = (epoch - 1) * this_n_train_batches + minibatch_index
if iter % 100 == 0:
print('training @ iter = ', iter)
cost_ij, loss = train_model(minibatch_index, learning_rate, l1, l2)
print("cost: ", cost_ij)
print("loss1: ", loss)
if (iter + 1) % validation_frequency == 0:
# test data
confidence_eval = [
test_model_confidence(i) for i in xrange(n_test_batches)
]
confidence_list_eval = []
for r in range(0, len(confidence_eval)):
for r1 in range(0, batch_size):
hypo_result_eval = confidence_eval[r][0].item(r1)
confidence_list_eval.append(hypo_result_eval)
y_pred_eval = confidence_list_eval
y_true_eval = role_index_test_matrix[:n_test_batches *
batch_size]
y_true_eval_2 = []
for i in range(len(y_true_eval)):
y_true_eval_2.append(int(y_true_eval[i]))
labels1 = [13, 14, 15, 16, 17]
this_fscore_eval = f1_score(y_true_eval_2,
y_pred_eval,
labels=labels1,
average='micro')
this_fscore_macro_eval = f1_score(y_true_eval_2,
y_pred_eval,
labels=labels1,
average='macro')
print(
'EVAL: *** epoch %i, best_validation %f, best_validation_m1 %f, learning_rate %f, '
'minibatch %i/%i, validation fscore %f %%' %
(epoch, best_fscoreEval * 100., best_fscore_m1Eval * 100,
learning_rate, minibatch_index + 1, this_n_train_batches,
this_fscore_eval * 100.))
if this_fscore_eval > best_fscoreEval:
best_fscoreEval = this_fscore_eval
best_fscore_m1Eval = this_fscore_macro_eval
best_iterEval = iter
# dev data
confidence = [
eval_model_confidence(i) for i in xrange(n_valid_batches)
]
confidence_list = []
for r in range(0, len(confidence)):
for r1 in range(0, batch_size):
hypo_result = confidence[r][0].item(r1)
confidence_list.append(hypo_result)
y_pred = confidence_list
y_true = role_index_dev_matrix[:n_valid_batches * batch_size]
y_true_2 = []
for i in range(len(y_true)):
y_true_2.append(int(y_true[i]))
labels = []
for i in range(1, role_size):
labels.append(i)
this_fscore = f1_score(y_true_2,
y_pred,
labels=labels,
average='micro')
this_fscore_macro = f1_score(y_true_2,
y_pred,
labels=labels,
average='macro')
print(
'epoch %i, best_validation %f, best_validation_m1 %f, learning_rate %f, minibatch %i/%i, '
'validation fscore %f %%' %
(epoch, best_fscore * 100., best_fscore_m1 * 100,
learning_rate, minibatch_index + 1, this_n_train_batches,
this_fscore * 100.))
# if we got the best validation score until now
if this_fscore > best_fscore:
best_fscore = this_fscore
best_fscore_m1 = this_fscore_macro
best_iter = iter
best_params = []
for p in param_list:
p_param = []
for part in p:
p_param.append(part.get_value(borrow=False))
best_params.append(p_param)
noImprovement = 0
else:
if this_fscore > last_fscore:
noImprovement -= 1
noImprovement = max(noImprovement, 0)
else:
noImprovement += 1
updatestep = minibatch_index + this_n_train_batches * (
epoch - 1)
if newbob: # learning rate schedule depending on dev result
learning_rate /= 1.2
print("reducing learning rate to ", learning_rate)
last_fscore = this_fscore
if newbob: # learning rate schedule depending on dev result
if noImprovement > maxNoImprovement or learning_rate < 0.0000001:
done_looping = True
break
if not newbob:
if epoch + 1 > 10:
learning_rate /= 1.2
print("reducing learning rate to ", learning_rate)
if epoch + 1 > 50:
done_looping = True
break
end_time = time.clock()
print('Optimization complete.')
print(
'Best validation score of %f %% obtained for c=%i, nk=%i, f=%i, h=%i at iteration %i,'
% (best_fscore * 100., arg_context_size, nkerns[0], filter_size[1],
hidden_units, best_iter + 1))
time2 = time.time()
print("time for training: ", time2 - time1)
print('Saving net.')
save_file = open(arg_network_file, 'wb')
for p in best_params:
for p_part in p:
cPickle.dump(p_part, save_file, -1)
save_file.close()
def __init__(self,
nkerns=[48, 48, 48],
miniBatchSize=200,
nHidden=200,
nClasses=2,
nMaxPool=2,
nChannels=1):
"""
nClasses : the number of target classes (e.g. 2 for binary classification)
nMaxPool : number of pixels to max pool
nChannels : number of input channels (e.g. 1 for single grayscale channel)
"""
rng = numpy.random.RandomState(23455)
self.p = 65
self.miniBatchSize = miniBatchSize
# Note: self.x and self.y will be re-bound to a subset of the
# training/validation/test data dynamically by the update
# stage of the appropriate function.
self.x = T.tensor4('x') # membrane mini-batch
self.y = T.ivector('y') # 1D vector of [int] labels
# We now assume the input will already be reshaped to the
# proper size (i.e. we don't need a theano resize op here).
layer0_input = self.x
#--------------------------------------------------
# LAYER 0
# layer0 convolution+max pool reduces image dimensions by:
# 65 -> 62 -> 31
#--------------------------------------------------
fs0 = 4 # conv. filter size, layer 0
os0 = (self.p - fs0 + 1) / nMaxPool # image out size 0
assert (os0 == 31)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=(self.miniBatchSize, nChannels,
self.p, self.p),
filter_shape=(nkerns[0], nChannels, fs0,
fs0),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 1
# layer1 convolution+max pool reduces image dimensions by:
# 31 -> 28 -> 14
#--------------------------------------------------
fs1 = 4 # filter size, layer 1
os1 = (os0 - fs1 + 1) / nMaxPool # image out size 1
assert (os1 == 14)
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(self.miniBatchSize, nkerns[0],
os0, os0),
filter_shape=(nkerns[1], nkerns[0], fs1,
fs1),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 2
# layer2 convolution+max pool reduces image dimensions by:
# 14 -> 10 -> 5
#--------------------------------------------------
fs2 = 5
os2 = (os1 - fs2 + 1) / nMaxPool
assert (os2 == 5)
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(self.miniBatchSize, nkerns[1],
os1, os1),
filter_shape=(nkerns[2], nkerns[1], fs2,
fs2),
poolsize=(nMaxPool, nMaxPool))
#--------------------------------------------------
# LAYER 3
# Fully connected sigmoidal layer, goes from
# 5*5*48 -> 200
#--------------------------------------------------
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=nkerns[2] * os2 * os2,
n_out=nHidden,
activation=T.tanh)
#--------------------------------------------------
# LAYER 4
# Classification via a logistic regression layer
# 200 -> 2
#--------------------------------------------------
# classify the values of the fully-connected sigmoidal layer
layer4 = LogisticRegression(input=layer3.output,
n_in=nHidden,
n_out=nClasses)
self.layers = (layer0, layer1, layer2, layer3, layer4)
def start(inputfile):
global in_time, out_time, cooldown_in_time, cooldown_out_time, classify
global global_counter, winner_stride, cur_state, in_frame_num, actions_counter
global test_set_x, test_set_y, shared_test_set_y
rng = numpy.random.RandomState(23455)
# ####################### build start ########################
# create an empty shared variables to be filled later
data_x = numpy.zeros([1, 20 * 50 * 50])
data_y = numpy.zeros(20)
train_set = (data_x, data_y)
(test_set_x, test_set_y, shared_test_set_y) = \
shared_dataset(train_set)
print 'building ... '
batch_size = 1
# allocate symbolic variables for the data
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
# image size
layer0_w = 50
layer0_h = 50
layer1_w = (layer0_w - 4) / 2
layer1_h = (layer0_h - 4) / 2
layer2_w = (layer1_w - 2) / 2
layer2_h = (layer1_h - 2) / 2
layer3_w = (layer2_w - 2) / 2
layer3_h = (layer2_h - 2) / 2
# #####################
# BUILD ACTUAL MODEL #
# #####################
# image sizes
batchsize = batch_size
in_channels = 20
in_width = 50
in_height = 50
# filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(rng,
input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape,
poolsize=(2, 2))
layer1 = LeNetConvPoolLayer(rng,
input=layer0.output,
image_shape=(batch_size, flt_channels,
layer1_w, layer1_h),
filter_shape=(60, flt_channels, 3, 3),
poolsize=(2, 2))
layer2 = LeNetConvPoolLayer(rng,
input=layer1.output,
image_shape=(batch_size, 60, layer2_w,
layer2_h),
filter_shape=(90, 60, 3, 3),
poolsize=(2, 2))
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(rng,
input=layer3_input,
n_in=90 * layer3_w * layer3_h,
n_out=500,
activation=T.tanh)
layer4 = LogisticRegression(input=layer3.output, n_in=500, n_out=8)
cost = layer4.negative_log_likelihood(y)
classify = theano.function(
[index],
outputs=layer4.get_output_labels(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
})
# load weights
print 'loading weights state'
f = file('weights.save', 'rb')
loaded_objects = []
for i in range(5):
loaded_objects.append(cPickle.load(f))
f.close()
layer0.__setstate__(loaded_objects[0])
layer1.__setstate__(loaded_objects[1])
layer2.__setstate__(loaded_objects[2])
layer3.__setstate__(loaded_objects[3])
layer4.__setstate__(loaded_objects[4])
# ####################### build done ########################
fromCam = False
if fromCam:
print 'using camera input'
cap = cv2.VideoCapture(0)
else:
print 'using input file: ', inputfile
cap = cv2.VideoCapture(inputfile)
# my timing
frame_rate = 5
frame_interval_ms = 1000 / frame_rate
fourcc = cv2.VideoWriter_fourcc(*'XVID')
video_writer = cv2.VideoWriter('../out/live_out.avi', fourcc, frame_rate,
(640, 480))
frame_counter = 0
(ret, frame) = cap.read()
proFrame = process_single_frame(frame)
# init detectors
st_a_det = RepDetector(proFrame, detector_strides[0])
st_b_det = RepDetector(proFrame, detector_strides[1])
st_c_det = RepDetector(proFrame, detector_strides[2])
frame_wise_counts = []
while True:
in_frame_num += 1
if in_frame_num % 2 == 1:
continue
(ret, frame) = cap.read()
if ret == 0:
print 'unable to read frame'
break
proFrame = process_single_frame(frame)
# handle stride A....
if frame_counter % st_a_det.stride_number == 0:
st_a_det.count(proFrame)
# handle stride B
if frame_counter % st_b_det.stride_number == 0:
st_b_det.count(proFrame)
# handle stride C
if frame_counter % st_c_det.stride_number == 0:
st_c_det.count(proFrame)
# display result on video................
blue_color = (130, 0, 0)
green_color = (0, 130, 0)
red_color = (0, 0, 130)
orange_color = (0, 140, 0xFF)
out_time = in_frame_num / 60
if cur_state == state.IN_REP and (out_time - in_time < 4
or global_counter < 5):
draw_str(frame, (20, 120),
' new hypothesis (%d) ' % global_counter, orange_color,
1.5)
if cur_state == state.IN_REP and out_time - in_time >= 4 \
and global_counter >= 5:
draw_str(
frame, (20, 120), 'action %d: counting... %d' %
(actions_counter, global_counter), green_color, 2)
if cur_state == state.COOLDOWN and global_counter >= 5:
draw_str(
frame, (20, 120), 'action %d: done. final counting: %d' %
(actions_counter, global_counter), blue_color, 2)
# print "pls", global_counter
frame_wise_counts.append(global_counter)
# print 'action %d: done. final counting: %d' % (actions_counter, global_counter)
print "Dhruv", frame_wise_counts, global_counter
return frame_wise_counts
def __init__(self, configfile, train = False):
self.config = readConfig(configfile)
self.addInputSize = 1
logger.info("additional mlp input")
wordvectorfile = self.config["wordvectors"]
logger.info("wordvectorfile " + str(wordvectorfile))
networkfile = self.config["net"]
logger.info("networkfile " + str(networkfile))
hiddenunits = int(self.config["hidden"])
logger.info("hidden units " + str(hiddenunits))
hiddenunitsNER = 50
if "hiddenunitsNER" in self.config:
hiddenunitsNER = int(self.config["hiddenunitsNER"])
logger.info("hidden units NER " + str(hiddenunitsNER))
learning_rate = float(self.config["lrate"])
logger.info("learning rate " + str(learning_rate))
if train:
self.batch_size = int(self.config["batchsize"])
else:
self.batch_size = 1
logger.info("batch size " + str(self.batch_size))
self.filtersize = [1,int(self.config["filtersize"])]
nkerns = [int(self.config["nkerns"])]
logger.info("nkerns " + str(nkerns))
pool = [1, int(self.config["kmax"])]
self.contextsize = int(self.config["contextsize"])
logger.info("contextsize " + str(self.contextsize))
if self.contextsize < self.filtersize[1]:
logger.info("setting filtersize to " + str(self.contextsize))
self.filtersize[1] = self.contextsize
logger.info("filtersize " + str(self.filtersize))
sizeAfterConv = self.contextsize - self.filtersize[1] + 1
sizeAfterPooling = -1
if sizeAfterConv < pool[1]:
logger.info("setting poolsize to " + str(sizeAfterConv))
pool[1] = sizeAfterConv
sizeAfterPooling = pool[1]
logger.info("kmax pooling: k = " + str(pool[1]))
# reading word vectors
self.wordvectors, self.vectorsize = readWordvectors(wordvectorfile)
self.representationsize = self.vectorsize + 1
rng = numpy.random.RandomState(23455)
if train:
seed = rng.get_state()[1][0]
logger.info("seed: " + str(seed))
# allocate symbolic variables for the data
self.index = T.lscalar() # index to a [mini]batch
self.xa = T.matrix('xa') # left context
self.xb = T.matrix('xb') # middle context
self.xc = T.matrix('xc') # right context
self.y = T.imatrix('y') # label (only present in training)
self.yNER1 = T.imatrix('yNER1') # label for first entity
self.yNER2 = T.imatrix('yNER2') # label for second entity
ishape = [self.representationsize, self.contextsize] # this is the size of context matrizes
######################
# BUILD ACTUAL MODEL #
######################
logger.info('... building the model')
# Reshape input matrix to be compatible with our LeNetConvPoolLayer
layer0a_input = self.xa.reshape((self.batch_size, 1, ishape[0], ishape[1]))
layer0b_input = self.xb.reshape((self.batch_size, 1, ishape[0], ishape[1]))
layer0c_input = self.xc.reshape((self.batch_size, 1, ishape[0], ishape[1]))
self.y_reshaped = self.y.reshape((self.batch_size, 1))
yNER1reshaped = self.yNER1.reshape((self.batch_size, 1))
yNER2reshaped = self.yNER2.reshape((self.batch_size, 1))
# Construct convolutional pooling layer:
filter_shape = (nkerns[0], 1, self.representationsize, self.filtersize[1])
poolsize=(pool[0], pool[1])
fan_in = numpy.prod(filter_shape[1:])
fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
numpy.prod(poolsize))
W_bound = numpy.sqrt(6. / (fan_in + fan_out))
# the convolution weight matrix
convW = theano.shared(numpy.asarray(
rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
dtype=theano.config.floatX),
borrow=True)
# the bias is a 1D tensor -- one bias per output feature map
b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
convB = theano.shared(value=b_values, borrow=True)
self.layer0a = LeNetConvPoolLayer(rng, W=convW, b=convB, input=layer0a_input,
image_shape=(self.batch_size, 1, ishape[0], ishape[1]),
filter_shape=filter_shape, poolsize=poolsize)
self.layer0b = LeNetConvPoolLayer(rng, W=convW, b=convB, input=layer0b_input,
image_shape=(self.batch_size, 1, ishape[0], ishape[1]),
filter_shape=filter_shape, poolsize=poolsize)
self.layer0c = LeNetConvPoolLayer(rng, W=convW, b=convB, input=layer0c_input,
image_shape=(self.batch_size, 1, ishape[0], ishape[1]),
filter_shape=filter_shape, poolsize=poolsize)
#layer0_output = T.concatenate([self.layer0a.output, self.layer0b.output, self.layer0c.output], axis = 3)
layer0aflattened = self.layer0a.output.flatten(2).reshape((self.batch_size, nkerns[0] * sizeAfterPooling))
layer0bflattened = self.layer0b.output.flatten(2).reshape((self.batch_size, nkerns[0] * sizeAfterPooling))
layer0cflattened = self.layer0c.output.flatten(2).reshape((self.batch_size, nkerns[0] * sizeAfterPooling))
layer0_output = T.concatenate([layer0aflattened, layer0bflattened, layer0cflattened], axis = 1)
self.layer1a = HiddenLayer(rng = rng, input = self.yNER1, n_in = 6, n_out = hiddenunitsNER, activation = T.tanh)
self.layer1b = HiddenLayer(rng = rng, input = self.yNER2, n_in = 6, n_out = hiddenunitsNER, activation = T.tanh, W = self.layer1a.W, b = self.layer1a.b)
layer2_input = T.concatenate([layer0_output, self.layer1a.output, self.layer1b.output], axis = 1)
layer2_inputSize = 3 * nkerns[0] * sizeAfterPooling + 2 * hiddenunitsNER
self.additionalFeatures = T.matrix('additionalFeatures')
additionalFeatsShaped = self.additionalFeatures.reshape((self.batch_size, 1))
layer2_input = T.concatenate([layer2_input, additionalFeatsShaped], axis = 1)
layer2_inputSize += self.addInputSize
self.layer2 = HiddenLayer(rng, input=layer2_input, n_in=layer2_inputSize,
n_out=hiddenunits, activation=T.tanh)
# classify the values of the fully-connected sigmoidal layer
self.layer3 = LogisticRegression(input=self.layer2.output, n_in=hiddenunits, n_out=23)
# create a list of all model parameters
self.paramList = [self.layer3.params, self.layer2.params, self.layer1a.params, self.layer0a.params]
self.params = []
for p in self.paramList:
self.params += p
logger.info(p)
if not train:
self.gotNetwork = 1
# load parameters
if not os.path.isfile(networkfile):
logger.error("network file does not exist")
self.gotNetwork = 0
else:
save_file = open(networkfile, 'rb')
for p in self.params:
p.set_value(cPickle.load(save_file), borrow=False)
save_file.close()
in_channels = 20
in_width = 50
in_height = 50
#filter sizes
flt_channels = 40
flt_time = 20
flt_width = 5
flt_height = 5
signals_shape = (batchsize, in_channels, in_height, in_width)
filters_shape = (flt_channels, in_channels, flt_height, flt_width)
layer0_input = x.reshape(signals_shape)
layer0 = LeNetConvPoolLayer(rng, input=layer0_input,
image_shape=signals_shape,
filter_shape=filters_shape, poolsize=(2, 2))
layer1 = LeNetConvPoolLayer(rng, input=layer0.output,
image_shape=(batch_size, flt_channels, layer1_w, layer1_h),
filter_shape=(60, flt_channels, 3, 3), poolsize=(2, 2))
layer2 = LeNetConvPoolLayer(rng, input=layer1.output,
image_shape=(batch_size, 60, layer2_w, layer2_h),
filter_shape=(90, 60, 3, 3), poolsize=(2, 2))
layer3_input = layer2.output.flatten(2)
layer3 = HiddenLayer(rng, input=layer3_input, n_in=90 * layer3_w * layer3_h ,
n_out=500, activation=T.tanh)
