devFeatures = np.zeros((nDev, dimVectors))
devLabels = np.zeros((nDev,), dtype=np.int32)
for i in xrange(nDev):
words, devLabels[i] = devset[i]
devFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Try our regularization parameters
results = []
for regularization in REGULARIZATION:
random.seed(3141)
np.random.seed(59265)
weights = np.random.randn(dimVectors, 5)
print "Training for reg=%f" % regularization
# We will do batch optimization
weights = sgd(lambda weights: softmax_wrapper(trainFeatures, trainLabels,
weights, regularization), weights, 3.0, 10000, PRINT_EVERY=100)
# Test on train set
_, _, pred = softmaxRegression(trainFeatures, trainLabels, weights)
trainAccuracy = accuracy(trainLabels, pred)
print "Train accuracy (%%): %f" % trainAccuracy
# Test on dev set
_, _, pred = softmaxRegression(devFeatures, devLabels, weights)
devAccuracy = accuracy(devLabels, pred)
print "Dev accuracy (%%): %f" % devAccuracy
# Save the results and weights
results.append({
"reg" : regularization,
"weights" : weights,
devFeatures = np.zeros((nDev, dimVectors))
devLabels = np.zeros((nDev, ), dtype=np.int32)
for i in range(nDev):
words, devLabels[i] = devset[i]
devFeatures[i, :] = getSentenceFeature(tokens, wordVectors, words)
# Try our regularization parameters
results = []
for regularization in REGULARIZATION:
random.seed(3141)
np.random.seed(59265)
weights = np.random.randn(dimVectors, 5)
print("Training for reg=%f" % regularization)
# We will do batch optimization
weights = sgd(lambda weights: softmax_wrapper(trainFeatures, trainLabels,
weights, regularization),
weights,
3.0,
10000,
PRINT_EVERY=100)
# Test on train set
_, _, pred = softmaxRegression(trainFeatures, trainLabels, weights)
trainAccuracy = accuracy(trainLabels, pred)
print("Train accuracy (%%): %f" % trainAccuracy)
# Test on dev set
_, _, pred = softmaxRegression(devFeatures, devLabels, weights)
devAccuracy = accuracy(devLabels, pred)
print("Dev accuracy (%%): %f" % devAccuracy)