def run_denotation(self, lines, dataset='denotation'):
"""
Runs stochastic gradient descent with model as objective.
This method is about training with a very large dataset.
"""
m = len(lines)
# randomly shuffle data
#random.shuffle(trees)
for i in xrange(0, m-self.minibatch+1, self.minibatch):
self.it += 1
# Get data as much as it's specified by minibatch num
# Load&parse trees beforehand
# Get data as much as it's specified by minibatch num
# Parse&load tree data beforehand
trees = list(tr.inputarray(lines[i:i+self.minibatch], dataset))
# map word indices to loaded trees
tr.map_words_to_trees(trees, dataset)
mb_data = trees#mb_data = trees[i:i+self.minibatch]
# Get the gradient?
cost,grad = self.model.costAndGrad(mb_data)
# compute exponentially weighted cost
if np.isfinite(cost):
if self.it > 1:
self.expcost.append(.01*cost + .99*self.expcost[-1])
else:
self.expcost.append(cost)
if self.optimizer == 'sgd':
update = grad
scale = -self.alpha
elif self.optimizer == 'adagrad':
# trace = trace+grad.^2
self.gradt[1:] = [gt+g**2
for gt,g in zip(self.gradt[1:],grad[1:])]
# update = grad.*trace.^(-1/2)
update = [g*(1./np.sqrt(gt))
for gt,g in zip(self.gradt[1:],grad[1:])]
# handle dictionary separately
dL = grad[0]
dLt = self.gradt[0]
for j in dL.iterkeys():
dLt[:,j] = dLt[:,j] + dL[j]**2
dL[j] = dL[j] * (1./np.sqrt(dLt[:,j]))
update = [dL] + update
scale = -self.alpha
# update params
self.model.updateParams(scale,update,log=False)
self.costt.append(cost)
if self.it % 1 == 0:
print "Iter %d : Cost=%.4f, ExpCost=%.4f."%(self.it,cost,self.expcost[-1])
def test_denotation(netFile, data, dataset):
# trees, vocab = tr.loadTrees(dataset,data)
assert netFile is not None, "Must give model to test"
with open(netFile, "r") as fid:
opts = pickle.load(fid)
_ = pickle.load(fid)
x = pickle.load(open("mr_%s.p" % dataset, "rb"))
W = x[0]
W2 = 0.01 * np.random.randn(opts.wvecDim, opts.numWords)
rnn = nnet_rte.RNNRTE(opts.wvecDim, opts.outputDim, 200, opts.numWords, opts.minibatch)
rnn.initParams(W)
rnn.fromFile(fid)
lines = tr.get_lines(opts.dataset, opts.data)
m = len(lines)
CHUNK_SIZE = 10000
minibatch = opts.minibatch
print "Testing..."
cost = correct = total = 0
for i in xrange(0, m - CHUNK_SIZE + 1, CHUNK_SIZE):
# Get data as much as it's specified by minibatch num
# Load&parse trees beforehand
# Get data as much as it's specified by minibatch num
# Parse&load tree data beforehand
trees = list(tr.inputarray(lines[i : i + CHUNK_SIZE], dataset))
# map word indices to loaded trees
tr.map_words_to_trees(trees, dataset)
c, cor, tot = rnn.costAndGrad(trees, test=True)
cost += c
correct += cor
total += tot
if i % CHUNK_SIZE == 0:
print "tested: %d" % i
print "Cost %f, Correct %d/%d, Acc %f" % (cost, correct, total, correct / float(total))
