diff options
Diffstat (limited to 'nerv/examples/ptb/main.lua')
-rw-r--r-- | nerv/examples/ptb/main.lua | 118 |
1 files changed, 118 insertions, 0 deletions
diff --git a/nerv/examples/ptb/main.lua b/nerv/examples/ptb/main.lua new file mode 100644 index 0000000..688716b --- /dev/null +++ b/nerv/examples/ptb/main.lua @@ -0,0 +1,118 @@ +nerv.include('reader.lua') +nerv.include('select_linear.lua') + +gconf = { + chunk_size = 5, + dropout_rate = 0, + lrate = 1.5, + wcost = 1e-5, + max_iter = 3, + clip = 5, + momentum = 0, + batch_size = 200, + test = true, +} + +local hidden_size = 300 +local vocab_size = 10000 +local layer_num = 1 +local dropout_rate = 0.5 +local trainer = nerv.Trainer + +function trainer:make_layer_repo(param_repo) + local layers = { + ['nerv.LSTMLayer'] = {}, + ['nerv.DropoutLayer'] = {}, + ['nerv.SelectLinearLayer'] = { + ['select'] = {dim_in = {1}, dim_out = {hidden_size}, vocab = vocab_size, pr = param_repo}, + }, + ['nerv.AffineLayer'] = { + output = {dim_in = {hidden_size}, dim_out = {vocab_size}, pr = param_repo}, + }, + ['nerv.SoftmaxCELayer'] = { + softmax = {dim_in = {vocab_size, 1}, dim_out = {1}, compressed = true}, + }, + } + for i = 1, layer_num do + layers['nerv.LSTMLayer']['lstm' .. i] = {dim_in = {hidden_size}, dim_out = {hidden_size}, pr = param_repo} + layers['nerv.DropoutLayer']['dropout' .. i] = {dim_in = {hidden_size}, dim_out = {hidden_size}} + end + return nerv.LayerRepo(layers, param_repo, gconf) +end + +function trainer:get_network(layer_repo) + local connections = { + {'<input>[1]', 'select[1]', 0}, + {'select[1]', 'lstm1[1]', 0}, + {'dropout' .. layer_num .. '[1]', 'output[1]', 0}, + {'output[1]', 'softmax[1]', 0}, + {'<input>[2]', 'softmax[2]', 0}, + {'softmax[1]', '<output>[1]', 0}, + } + for i = 1, layer_num do + table.insert(connections, {'lstm' .. i .. '[1]', 'dropout' .. i .. '[1]', 0}) + if i < 1 then + table.insert(connections, {'dropout' .. (i - 1) .. '[1]', 'lstm' .. i .. '[1]', 0}) + end + end + return nerv.GraphLayer('graph', gconf, {dim_in = {1, 1}, dim_out = {1}, layer_repo = layer_repo, connections = connections}) +end + +function trainer:get_input_order() + return {'input', 'label'} +end + +function trainer:get_readers(dataset) + local data_path = 'nerv/nerv/examples/lmptb/PTBdata/' + local vocab_file = data_path .. 'vocab' + local train_file = data_path .. 'ptb.train.txt.adds' + local cv_file = data_path .. 'ptb.valid.txt.adds' + local test_file = data_path .. 'ptb.test.txt.adds' + local reader + if dataset == 'train' then + reader = nerv.Reader(vocab_file, train_file) + elseif dataset == 'validate' then + reader = nerv.Reader(vocab_file, cv_file) + elseif dataset == 'test' then + reader = nerv.Reader(vocab_file, test_file) + else + nerv.error('no such dataset') + end + return {{reader = reader, data = {input = 1, label = 1}}} +end + +local total_err +local total_frame + +function trainer:get_error() + return math.pow(10, -total_err / total_frame) +end + +function trainer:epoch_preprocess(dataset, do_train) + if dataset == 'train' then + gconf.dropout_rate = dropout_rate + nerv.info('set dropout rate to %f', dropout_rate) + end + if dataset == 'validate' then + gconf.dropout_rate = 0 + nerv.info('set dropout rate to 0') + end + if dataset == 'test' then + gconf.dropout_rate = 0 + nerv.info('set dropout rate to 0') + end + total_err = 0 + total_frame = 0 +end + +function trainer:mini_batch_middleprocess(cnt, info) + for t = 1, gconf.chunk_size do + local tmp = info.output[1][t]:new_to_host() + for i = 1, gconf.batch_size do + total_err = total_err + math.log10(math.exp(tmp[i - 1][0])) + end + end + for i = 1, gconf.batch_size do + total_frame = total_frame + info.seq_length[i] + end +end |