1 files changed, 0 insertions, 93 deletions
diff --git a/nerv/tnn/layersT/softmax_ce_t.lua b/nerv/tnn/layersT/softmax_ce_t.lua
deleted file mode 100644
index a9ce975..0000000
--- a/nerv/tnn/layersT/softmax_ce_t.lua
+++ /dev/null
@@ -1,93 +0,0 @@
-local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayerT", "nerv.LayerT")
-
-function SoftmaxCELayer:__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.compressed = layer_conf.compressed
-    if self.compressed == nil then
-        self.compressed = false
-    end
-    self:check_dim_len(2, -1) -- two inputs: nn output and label
-end
-
-function SoftmaxCELayer:init(batch_size, chunk_size)
-    if not self.compressed and (self.dim_in[1] ~= self.dim_in[2]) then
-        nerv.error("mismatching dimensions of previous network output and labels")
-    end
-    if chunk_size == nil then
-        chunk_size = 1
-    end
-    self.total_ce = 0.0
-    self.total_correct = 0
-    self.total_frames = 0
-    self.softmax_t = {}
-    self.ce_t = {}
-    for t = 1, chunk_size do
-        self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
-        self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
-    end
-end
-
-function SoftmaxCELayer:batch_resize(batch_size, chunk_size)
-    if chunk_size == nil then
-        chunk_size = 1
-    end
-    for t = 1, chunk_size do
-        if self.softmax_t[t]:nrow() ~= batch_size then
-            self.softmax_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
-            self.ce_t[t] = self.gconf.cumat_type(batch_size, self.dim_in[1])
-        end
-    end
-end
-
-function SoftmaxCELayer:update(bp_err, input, output, t)
-    -- no params, therefore do nothing
-end
-
-function SoftmaxCELayer:propagate(input, output, t)
-    if t == nil then
-        t = 1
-    end
-    local softmax = self.softmax_t[t]
-    local ce = self.ce_t[t]
-    local classified = softmax:softmax(input[1])
-    local label = input[2]
-    ce:log_elem(softmax)
-    if self.compressed then
-        label = label:decompress(input[1]:ncol())
-    end
-    ce:mul_elem(ce, label)
-    ce = ce:rowsum()
-    if output[1] ~= nil then
-        output[1]:copy_fromd(ce)
-    end
-    -- add total ce
-    self.total_ce = self.total_ce - ce:colsum()[0][0]
-    self.total_frames = self.total_frames + softmax:nrow()
-    -- TODO: add colsame for uncompressed label
-    if self.compressed then
-        self.total_correct = self.total_correct + classified:colsame(input[2])[0][0]
-    end
-end
-
-function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output, t)
-    -- softmax output - label
-    if t == nil then
-        t = 1
-    end
-    local label = input[2]
-    if self.compressed then
-        label = label:decompress(input[1]:ncol())
-    end
-    local nbe = next_bp_err[1]
-    nbe:add(self.softmax_t[t], label, 1.0, -1.0)
-    if bp_err[1] ~= nil then
-        nbe:scale_rows_by_col(bp_err[1])
-    end
-end
-
-function SoftmaxCELayer:get_params()
-    return nerv.ParamRepo({})
-end