1 files changed, 71 insertions, 0 deletions
diff --git a/nerv/tnn/layersT/dropout_t.lua b/nerv/tnn/layersT/dropout_t.lua
new file mode 100644
index 0000000..4351285
--- /dev/null
+++ b/nerv/tnn/layersT/dropout_t.lua
@@ -0,0 +1,71 @@
+local Dropout = nerv.class("nerv.DropoutLayerT", "nerv.LayerT")
+
+function Dropout:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.gconf = global_conf
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self:check_dim_len(1, 1) -- two inputs: nn output and label
+end
+
+function Dropout:init(batch_size, chunk_size)
+    if self.dim_in[1] ~= self.dim_out[1] then
+        nerv.error("mismatching dimensions of input and output")
+    end
+    if chunk_size == nil then
+        chunk_size = 1
+    end
+    self.mask_t = {}
+    for t = 1, chunk_size do
+        self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+    end
+end
+
+function Dropout:batch_resize(batch_size, chunk_size)
+    if chunk_size == nil then
+        chunk_size = 1
+    end
+    for t = 1, chunk_size do
+        if self.mask_t[t] == nil or self.mask_t[t]:nrow() ~= batch_size then
+            self.mask_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
+        end
+    end
+end
+
+function Dropout:propagate(input, output, t)
+    if t == nil then
+        t = 1
+    end
+    if self.gconf.dropout_rate == nil then
+        nerv.info("DropoutLayerT:propagate warning, global_conf.dropout_rate is nil, setting it zero")
+        self.gconf.dropout_rate = 0
+    end
+    
+    if self.gconf.dropout_rate == 0 then
+        output[1]:copy_fromd(input[1])
+    else
+        self.mask_t[t]:rand_uniform()
+        --since we will lose a portion of the actvations, we multiply the activations by 1/(1-dr) to compensate
+        self.mask_t[t]:thres_mask(self.mask_t[t], self.gconf.dropout_rate, 0, 1 / (1.0 - self.gconf.dropout_rate))
+        output[1]:mul_elem(input[1], self.mask_t[t])
+    end
+end
+
+function Dropout:update(bp_err, input, output, t)
+    -- no params, therefore do nothing
+end
+
+function Dropout:back_propagate(bp_err, next_bp_err, input, output, t)
+    if t == nil then
+        t = 1
+    end
+    if self.gconf.dropout_rate == 0 then
+        next_bp_err[1]:copy_fromd(bp_err[1])
+    else
+        next_bp_err[1]:mul_elem(bp_err[1], self.mask_t[t])
+    end
+end
+
+function Dropout:get_params()
+    return nerv.ParamRepo({})
+end