1 files changed, 71 insertions, 0 deletions
diff --git a/nerv/examples/lmptb/rnn/layers/gate_fff.lua b/nerv/examples/lmptb/rnn/layers/gate_fff.lua
new file mode 100644
index 0000000..751dde1
--- /dev/null
+++ b/nerv/examples/lmptb/rnn/layers/gate_fff.lua
@@ -0,0 +1,71 @@
+local GateFFFLayer = nerv.class('nerv.GateFFFLayer', 'nerv.Layer')
+
+function GateFFFLayer:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self.gconf = global_conf
+ 
+    self.ltp1 = self:find_param("ltp1", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[1], self.dim_out[1]}) --layer_conf.ltp
+    self.ltp2 = self:find_param("ltp2", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[2], self.dim_out[1]}) --layer_conf.ltp
+    self.ltp3 = self:find_param("ltp3", layer_conf, global_conf, nerv.LinearTransParam, {self.dim_in[3], self.dim_out[1]}) --layer_conf.ltp
+    self.bp = self:find_param("bp", layer_conf, global_conf, nerv.BiasParam, {1, self.dim_out[1]})--layer_conf.bp
+  
+    self:check_dim_len(3, 1) -- exactly one input and one output
+end
+
+function GateFFFLayer:init(batch_size)
+    if self.ltp1.trans:ncol() ~= self.bp.trans:ncol() or 
+        self.ltp2.trans:ncol() ~= self.bp.trans:ncol() or
+        self.ltp3.trans:ncol() ~= self.bp.trans:ncol() then
+        nerv.error("mismatching dimensions of linear transform and bias paramter")
+    end
+    if self.dim_in[1] ~= self.ltp1.trans:nrow() or
+        self.dim_in[2] ~= self.ltp2.trans:nrow() or
+        self.dim_in[3] ~= self.ltp3.trans:nrow() then
+        nerv.error("mismatching dimensions of linear transform parameter and input")
+    end
+    if self.dim_out[1] ~= self.ltp1.trans:ncol() then
+        nerv.error("mismatching dimensions of linear transform parameter and output")
+    end
+    self.ltp1:train_init()
+    self.ltp2:train_init()
+    self.ltp3:train_init()
+    self.bp:train_init()
+    self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1])
+end
+
+function GateFFFLayer:batch_resize(batch_size)
+    if self.err_m:nrow() ~= batch_size then
+        self.err_bakm = self.gconf.cumat_type(batch_size, self.dim_out[1])
+    end
+end
+
+function GateFFFLayer:propagate(input, output)
+    -- apply linear transform
+    output[1]:mul(input[1], self.ltp1.trans, 1.0, 0.0, 'N', 'N')
+    output[1]:mul(input[2], self.ltp2.trans, 1.0, 1.0, 'N', 'N')
+    output[1]:mul(input[3], self.ltp3.trans, 1.0, 1.0, 'N', 'N')
+    -- add bias
+    output[1]:add_row(self.bp.trans, 1.0)
+    output[1]:sigmoid(output[1])
+end
+
+function GateFFFLayer:back_propagate(bp_err, next_bp_err, input, output)
+    self.err_bakm:sigmoid_grad(bp_err[1], output[1])
+    next_bp_err[1]:mul(self.err_bakm, self.ltp1.trans, 1.0, 0.0, 'N', 'T')
+    next_bp_err[2]:mul(self.err_bakm, self.ltp2.trans, 1.0, 0.0, 'N', 'T')
+    next_bp_err[3]:mul(self.err_bakm, self.ltp3.trans, 1.0, 0.0, 'N', 'T')
+end
+
+function GateFFFLayer:update(bp_err, input, output)
+    self.err_bakm:sigmoid_grad(bp_err[1], output[1])
+    self.ltp1:update_by_err_input(self.err_bakm, input[1])
+    self.ltp2:update_by_err_input(self.err_bakm, input[2])
+    self.ltp3:update_by_err_input(self.err_bakm, input[3])
+    self.bp:update_by_gradient(self.err_bakm:colsum())
+end
+
+function GateFFFLayer:get_params()
+    return nerv.ParamRepo({self.ltp1, self.ltp2, self.ltp3, self.bp})
+end