textSimilarityConvNet/trainMSRVID.lua at master · hohoCode/textSimilarityConvNet · GitHub

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--[[

Author: Hua He

Usage: th trainMSRVID.lua

Training script for semantic relatedness prediction on the MSRVID dataset.

--]]

require('torch')

require('nn')

require('nngraph')

require('optim')

require('xlua')

require('sys')

require('lfs')

similarityMeasure = {}

include('util/read_data.lua')

include('util/Vocab.lua')

include('Conv.lua')

include('CsDis.lua')

--include('PaddingReshape.lua')

printf = utils.printf

-- global paths (modify if desired)

similarityMeasure.data_dir = 'data'

similarityMeasure.models_dir = 'trained_models'

similarityMeasure.predictions_dir = 'predictions'

function header(s)

print(string.rep('-', 80))

print(s)

print(string.rep('-', 80))

end

-- Pearson correlation

function pearson(x, y)

x = x - x:mean()

y = y - y:mean()

return x:dot(y) / (x:norm() * y:norm())

end

-- read command line arguments

local args = lapp [[

Training script for semantic relatedness prediction on the SICK dataset.

-m,--model (default dependency) Model architecture: [dependency, lstm, bilstm]

-l,--layers (default 1) Number of layers (ignored for Tree-LSTM)

-d,--dim (default 150) LSTM memory dimension

]]

local model_name, model_class, model_structure

model_name = 'convOnly'

model_class = similarityMeasure.Conv

model_structure = model_name

torch.seed()

--torch.manualSeed(123)

print('<torch> using the specified seed: ' .. torch.initialSeed())

-- directory containing dataset files

local data_dir = 'data/msrvid/'

-- load vocab

local vocab = similarityMeasure.Vocab(data_dir .. 'vocab-cased.txt')

-- load embeddings

print('loading word embeddings')

local emb_dir = 'data/glove/'

local emb_prefix = emb_dir .. 'glove.840B'

local emb_vocab, emb_vecs = similarityMeasure.read_embedding(emb_prefix .. '.vocab', emb_prefix .. '.300d.th')

local emb_dim = emb_vecs:size(2)

-- use only vectors in vocabulary (not necessary, but gives faster training)

local num_unk = 0

local vecs = torch.Tensor(vocab.size, emb_dim)

for i = 1, vocab.size do

local w = vocab:token(i)

if emb_vocab:contains(w) then

vecs[i] = emb_vecs[emb_vocab:index(w)]

else

num_unk = num_unk + 1

vecs[i]:uniform(-0.05, 0.05)

end

print('unk count = ' .. num_unk)

emb_vocab = nil

emb_vecs = nil

collectgarbage()

local taskD = 'vid'

-- load datasets

print('loading datasets')

local train_dir = data_dir .. 'train/'

local dev_dir = data_dir .. 'dev/'

local test_dir = data_dir .. 'test/'

local train_dataset = similarityMeasure.read_relatedness_dataset(train_dir, vocab, taskD)

local dev_dataset = similarityMeasure.read_relatedness_dataset(dev_dir, vocab, taskD)

printf('num train = %d\n', train_dataset.size)

printf('num dev = %d\n', dev_dataset.size)

-- initialize model

local model = model_class{

emb_vecs = vecs,

structure = model_structure,

mem_dim = 150,

task = taskD,

}

-- number of epochs to train

local num_epochs = 35

-- print information

header('model configuration')

printf('max epochs = %d\n', num_epochs)

model:print_config()

if lfs.attributes(similarityMeasure.predictions_dir) == nil then

lfs.mkdir(similarityMeasure.predictions_dir)

end

-- train

local train_start = sys.clock()

local best_dev_score = -1.0

local best_dev_model = model

-- threads

--torch.setnumthreads(4)

--print('<torch> number of threads in used: ' .. torch.getnumthreads())

header('Training model')

local id = 2007

print("Id: " .. id)

for i = 1, num_epochs do

local start = sys.clock()

print('--------------- EPOCH ' .. i .. '--- -------------')

model:trainCombineOnly(train_dataset)

print('Finished epoch in ' .. ( sys.clock() - start) )

local dev_predictions = model:predict_dataset(dev_dataset)

local dev_score = pearson(dev_predictions, dev_dataset.labels)

printf('-- score: %.5f\n', dev_score)

end

print('finished training in ' .. (sys.clock() - train_start))

textSimilarityConvNet/trainMSRVID.lua at master · hohoCode/textSimilarityConvNet · GitHub

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