1 files changed, 0 insertions, 71 deletions
diff --git a/nerv/tnn/layersT/dropout_t.lua b/nerv/tnn/layersT/dropout_t.lua
deleted file mode 100644
index 4351285..0000000
--- a/nerv/tnn/layersT/dropout_t.lua
+++ /dev/null
@@ -1,71 +0,0 @@
-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