1 files changed, 91 insertions, 0 deletions
diff --git a/nerv/layer/lstmp.lua b/nerv/layer/lstmp.lua
new file mode 100644
index 0000000..bbb2091
--- /dev/null
+++ b/nerv/layer/lstmp.lua
@@ -0,0 +1,91 @@
+local LSTMPLayer = nerv.class('nerv.LSTMPLayer', 'nerv.GraphLayer')
+
+function LSTMPLayer:__init(id, global_conf, layer_conf)
+    nerv.Layer.__init(self, id, global_conf, layer_conf)
+    self:check_dim_len(1, 1)
+
+    local din = layer_conf.dim_in[1]
+    local dcell = layer_conf.cell_dim
+    local dout = layer_conf.dim_out[1]
+
+    local pr = layer_conf.pr
+    if pr == nil then
+        pr = nerv.ParamRepo({}, self.loc_type)
+    end
+
+    local layers = {
+        ['nerv.CombinerLayer'] = {
+            mainCombine = {dim_in = {dcell, dcell}, dim_out = {dcell}, lambda = {1, 1}},
+        },
+        ['nerv.DuplicateLayer'] = {
+            inputDup = {dim_in = {din}, dim_out = {din, din, din, din}},
+            outputDup = {dim_in = {dout}, dim_out = {dout, dout, dout, dout, dout}},
+            cellDup = {dim_in = {dcell}, dim_out = {dcell, dcell, dcell, dcell, dcell}},
+        },
+        ['nerv.AffineLayer'] = {
+            mainAffine = {dim_in = {din, dout}, dim_out = {dcell}, pr = pr},
+        },
+        ['nerv.TanhLayer'] = {
+            mainTanh = {dim_in = {dcell}, dim_out = {dcell}},
+            outputTanh = {dim_in = {dcell}, dim_out = {dcell}},
+        },
+        ['nerv.LSTMGateLayer'] = {
+            forgetGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr},
+            inputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr},
+            outputGate = {dim_in = {din, dout, dcell}, dim_out = {dcell}, param_type = {'N', 'N', 'D'}, pr = pr},
+        },
+        ['nerv.ElemMulLayer'] = {
+            inputGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}},
+            forgetGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}},
+            outputGateMul = {dim_in = {dcell, dcell}, dim_out = {dcell}},
+        },
+        ['nerv.ProjectionLayer'] = {
+            projection = {dim_in = {dcell}, dim_out = {dout}, pr = pr},
+        },
+    }
+    
+    local connections = {
+        -- lstm input
+        {'<input>[1]', 'inputDup[1]', 0},
+
+        -- input gate
+        {'inputDup[1]', 'inputGate[1]', 0},
+        {'outputDup[1]', 'inputGate[2]', 1},
+        {'cellDup[1]', 'inputGate[3]', 1},
+
+        -- forget gate
+        {'inputDup[2]', 'forgetGate[1]', 0},
+        {'outputDup[2]', 'forgetGate[2]', 1},
+        {'cellDup[2]', 'forgetGate[3]', 1},
+
+        -- lstm cell
+        {'forgetGate[1]', 'forgetGateMul[1]', 0},
+        {'cellDup[3]', 'forgetGateMul[2]', 1},
+        {'inputDup[3]', 'mainAffine[1]', 0},
+        {'outputDup[3]', 'mainAffine[2]', 1},
+        {'mainAffine[1]', 'mainTanh[1]', 0},
+        {'inputGate[1]', 'inputGateMul[1]', 0},
+        {'mainTanh[1]', 'inputGateMul[2]', 0},
+        {'inputGateMul[1]', 'mainCombine[1]', 0},
+        {'forgetGateMul[1]', 'mainCombine[2]', 0},
+        {'mainCombine[1]', 'cellDup[1]', 0},
+
+        -- forget gate
+        {'inputDup[4]', 'outputGate[1]', 0},
+        {'outputDup[4]', 'outputGate[2]', 1},
+        {'cellDup[4]', 'outputGate[3]', 0},
+
+        -- lstm output
+        {'cellDup[5]', 'outputTanh[1]', 0},
+        {'outputGate[1]', 'outputGateMul[1]', 0},
+        {'outputTanh[1]', 'outputGateMul[2]', 0},
+        {'outputGateMul[1]', 'projection[1]', 0},
+        {'projection[1]', 'outputDup[1]', 0},
+        {'outputDup[5]', '<output>[1]', 0},
+    }
+
+    self:add_prefix(layers, connections)
+    local layer_repo = nerv.LayerRepo(layers, pr, global_conf)
+    self.lrepo = layer_repo
+    self:graph_init(layer_repo, connections)
+end