def testloadData(self):
nExamples = 15
train_data = '/home/bhanu/Downloads/relationClassification/dataCamera/allDataTrain.mat'
pre_trained_weights = '/home/bhanu/Downloads/relationClassification/dataCamera/pretrainedWeights.mat'
rnnData = RNNDataCorpus()
rnnData.load_data(load_file=train_data, nExamples=nExamples)
params = Params(data=rnnData, wordSize=50, rankWo=3)
# n = params.wordSize; fanIn = params.fanIn; nWords = params.nWords; nLabels = params.categories; rank=params.rankWo
# theta = np.random.normal(loc=0.0, scale=np.math.pow(10,-5)**2, size = n*(2*n+1) + n*2*n + nLabels*fanIn + n*nWords + (2*n*rank+n)*nWords)
#
# #init respective parameters with prior values
# W, WO, Wcat, Wv, Wo = unroll_theta(theta, params)
# Wo[:n,:] = np.ones((n,Wo.shape[1])) #init word matrices with Identity matrices + epsilon
#load pre-trained weights here
mats = sio.loadmat(pre_trained_weights)
Wv = mats.get('Wv')
W = mats.get('W')
WO = mats.get('WO')
Wo = np.ndarray(
shape=(2 * params.wordSize * params.rankWo + params.wordSize) *
params.nWords,
dtype='float64')
sentencesIdx = np.arange(nExamples)
[allSNum_batch, allSNN_batch, Wv_batch, Wo_batch,
allWordInds] = getRelevantWords(rnnData, sentencesIdx, Wv, Wo, params)
print
def testloadData(self):
nExamples = 15
train_data = '/home/bhanu/Downloads/relationClassification/dataCamera/allDataTrain.mat'
pre_trained_weights = '/home/bhanu/Downloads/relationClassification/dataCamera/pretrainedWeights.mat'
rnnData = RNNDataCorpus()
rnnData.load_data(load_file=train_data, nExamples=nExamples)
params = Params(data=rnnData, wordSize=50, rankWo=3)
# n = params.wordSize; fanIn = params.fanIn; nWords = params.nWords; nLabels = params.categories; rank=params.rankWo
# theta = np.random.normal(loc=0.0, scale=np.math.pow(10,-5)**2, size = n*(2*n+1) + n*2*n + nLabels*fanIn + n*nWords + (2*n*rank+n)*nWords)
#
# #init respective parameters with prior values
# W, WO, Wcat, Wv, Wo = unroll_theta(theta, params)
# Wo[:n,:] = np.ones((n,Wo.shape[1])) #init word matrices with Identity matrices + epsilon
#load pre-trained weights here
mats = sio.loadmat(pre_trained_weights)
Wv = mats.get('Wv'); W = mats.get('W'); WO = mats.get('WO')
Wo = np.ndarray(shape=(2*params.wordSize*params.rankWo + params.wordSize)*params.nWords, dtype='float64')
sentencesIdx = np.arange(nExamples)
[allSNum_batch, allSNN_batch, Wv_batch, Wo_batch, allWordInds] = getRelevantWords(rnnData, sentencesIdx, Wv,Wo,params)
print
def train():
np.random.seed(131742)
#get sentences, trees and labels
nExamples = -1
print "loading data.."
rnnData = RNNDataCorpus()
rnnData.load_data(load_file=config.train_data, nExamples=nExamples)
#initialize params
print "initializing params"
params = Params(data=rnnData, wordSize=50, rankWo=2)
#define theta
#one vector for all the parameters of mvrnn model: W, Wm, Wlabel, L, Lm
n = params.wordSize
fanIn = params.fanIn
nWords = params.nWords
nLabels = params.categories
rank = params.rankWo
Wo = 0.01 * np.random.randn(n + 2 * n * rank, nWords) #Lm, as in paper
Wo[:n, :] = np.ones((n, Wo.shape[1])) #Lm, as in paper
Wcat = 0.005 * np.random.randn(nLabels, fanIn) #Wlabel, as in paper
# Wv = 0.01*np.random.randn(n, nWords)
# WO = 0.01*np.random.randn(n, 2*n)
# W = 0.01*np.random.randn(n, 2*n+1)
#load pre-trained weights here
mats = sio.loadmat(config.pre_trained_weights)
Wv = mats.get('Wv') #L, as in paper
W = mats.get('W') #W, as in paper
WO = mats.get('WO') #Wm, as in paper
sentencesIdx = np.arange(rnnData.ndoc())
np.random.shuffle(sentencesIdx)
nTrain = 4 * len(sentencesIdx) / 5
trainSentIdx = sentencesIdx[0:nTrain]
testSentIdx = sentencesIdx[nTrain:]
batchSize = 5
nBatches = len(trainSentIdx) / batchSize
evalFreq = 5 #evaluate after every 5 minibatches
nTestSentEval = 50 #number of test sentences to be evaluated
rnnData_train = RNNDataCorpus()
rnnData.copy_into_minibatch(rnnData_train, trainSentIdx)
rnnData_test = RNNDataCorpus()
if (len(testSentIdx) > nTestSentEval):
# np.random.shuffle(testSentIdx) #choose random test examples
thisTestSentIdx = testSentIdx[:nTestSentEval]
else:
thisTestSentIdx = testSentIdx
rnnData.copy_into_minibatch(rnnData_test, thisTestSentIdx)
# [Wv_test, Wo_test, _] = getRelevantWords(rnnData_test, Wv,Wo,params)
[Wv_trainTest, Wo_trainTest, all_train_idx
] = getRelevantWords(rnnData, Wv, Wo,
params) #sets nWords_reduced, returns new arrays
theta = np.concatenate((W.flatten(), WO.flatten(), Wcat.flatten(),
Wv_trainTest.flatten(), Wo_trainTest.flatten()))
#optimize
print "starting training..."
nIter = 100
rnnData_minibatch = RNNDataCorpus()
for i in range(nIter):
#train in minibatches
# ftrain = np.zeros(nBatches)
# for ibatch in range(nBatches):
# set_minibatch(rnnData, rnnData_minibatch, ibatch, nBatches, trainSentIdx)
# print 'Iteration: ', i, ' minibatch: ', ibatch
tunedTheta, fbatch_train, _ = lbfgsb.fmin_l_bfgs_b(
func=costFn,
x0=theta,
fprime=None,
args=(rnnData_train, params),
approx_grad=0,
bounds=None,
m=5,
factr=1000000000000000.0,
pgtol=1.0000000000000001e-5,
epsilon=1e-08,
iprint=3,
maxfun=1,
disp=0)
#map parameters back
W[:, :], WO[:, :], Wcat[:, :], Wv_trainTest, Wo_trainTest = unroll_theta(
tunedTheta, params)
Wv[:, all_train_idx] = Wv_trainTest
Wo[:, all_train_idx] = Wo_trainTest
# ftrain[ibatch] = fbatch_train
theta = tunedTheta #for next iteration
print "========================================"
print "XXXXXXIteration ", i,
print "Average cost: ", np.average(fbatch_train)
evaluate(Wv, Wo, W, WO, Wcat, params, rnnData_test)
print "========================================"
#save weights
save_dict = {'Wv': Wv, 'Wo': Wo, 'Wcat': Wcat, 'W': W, 'WO': WO}
sio.savemat(config.saved_params_file + '_lbfgs_iter' + str(i),
mdict=save_dict)
print "saved tuned theta. "
def train():
np.random.seed(131742)
#get sentences, trees and labels
nExamples = -1
print "loading data.."
rnnData = RNNDataCorpus()
rnnData.load_data(load_file=config.train_data, nExamples=nExamples)
#initialize params
print "initializing params"
params = Params(data=rnnData, wordSize=50, rankWo=2)
#define theta
#one vector for all the parameters of mvrnn model: W, Wm, Wlabel, L, Lm
n = params.wordSize; fanIn = params.fanIn; nWords = params.nWords; nLabels = params.categories; rank=params.rankWo
Wo = 0.01*np.random.randn(n + 2*n*rank, nWords) #Lm, as in paper
Wo[:n,:] = np.ones((n,Wo.shape[1])) #Lm, as in paper
Wcat = 0.005*np.random.randn(nLabels, fanIn) #Wlabel, as in paper
# Wv = 0.01*np.random.randn(n, nWords)
# WO = 0.01*np.random.randn(n, 2*n)
# W = 0.01*np.random.randn(n, 2*n+1)
#load pre-trained weights here
mats = sio.loadmat(config.pre_trained_weights)
Wv = mats.get('Wv') #L, as in paper
W = mats.get('W') #W, as in paper
WO = mats.get('WO') #Wm, as in paper
sentencesIdx = np.arange(rnnData.ndoc())
np.random.shuffle(sentencesIdx)
nTrain = 4*len(sentencesIdx)/5
trainSentIdx = sentencesIdx[0:nTrain]
testSentIdx = sentencesIdx[nTrain:]
batchSize = 5
nBatches = len(trainSentIdx)/batchSize
evalFreq = 5 #evaluate after every 5 minibatches
nTestSentEval = 50 #number of test sentences to be evaluated
rnnData_train = RNNDataCorpus()
rnnData.copy_into_minibatch(rnnData_train, trainSentIdx)
rnnData_test = RNNDataCorpus()
if(len(testSentIdx) > nTestSentEval):
# np.random.shuffle(testSentIdx) #choose random test examples
thisTestSentIdx = testSentIdx[:nTestSentEval]
else:
thisTestSentIdx = testSentIdx
rnnData.copy_into_minibatch(rnnData_test, thisTestSentIdx)
# [Wv_test, Wo_test, _] = getRelevantWords(rnnData_test, Wv,Wo,params)
[Wv_trainTest, Wo_trainTest, all_train_idx] = getRelevantWords(rnnData, Wv,Wo,params) #sets nWords_reduced, returns new arrays
theta = np.concatenate((W.flatten(), WO.flatten(), Wcat.flatten(), Wv_trainTest.flatten(), Wo_trainTest.flatten()))
#optimize
print "starting training..."
nIter = 100
rnnData_minibatch = RNNDataCorpus()
for i in range(nIter):
#train in minibatches
# ftrain = np.zeros(nBatches)
# for ibatch in range(nBatches):
# set_minibatch(rnnData, rnnData_minibatch, ibatch, nBatches, trainSentIdx)
# print 'Iteration: ', i, ' minibatch: ', ibatch
tunedTheta, fbatch_train, _ = lbfgsb.fmin_l_bfgs_b(func=costFn, x0=theta, fprime=None, args=(rnnData_train, params), approx_grad=0, bounds=None, m=5,
factr=1000000000000000.0, pgtol=1.0000000000000001e-5, epsilon=1e-08,
iprint=3, maxfun=1, disp=0)
#map parameters back
W[:,:], WO[:,:], Wcat[:,:], Wv_trainTest, Wo_trainTest = unroll_theta(tunedTheta, params)
Wv[:,all_train_idx] = Wv_trainTest
Wo[:,all_train_idx] = Wo_trainTest
# ftrain[ibatch] = fbatch_train
theta = tunedTheta #for next iteration
print "========================================"
print "XXXXXXIteration ", i,
print "Average cost: ", np.average(fbatch_train)
evaluate(Wv,Wo,W,WO,Wcat,params, rnnData_test)
print "========================================"
#save weights
save_dict = {'Wv':Wv, 'Wo':Wo, 'Wcat':Wcat, 'W':W, 'WO':WO}
sio.savemat(config.saved_params_file+'_lbfgs_iter'+str(i), mdict=save_dict)
print "saved tuned theta. "