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