1 files changed, 77 insertions, 0 deletions
diff --git a/nerv/layer/dropout.lua b/nerv/layer/dropout.lua
new file mode 100644
index 0000000..42660cc
--- /dev/null
+++ b/nerv/layer/dropout.lua
@@ -0,0 +1,77 @@
+local DropoutLayer = nerv.class("nerv.DropoutLayer", "nerv.Layer")
+
+function DropoutLayer:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.gconf = global_conf
+    if self.gconf.use_cpu then
+        self.mat_type = self.gconf.mmat_type
+    else
+        self.mat_type = self.gconf.cumat_type
+    end
+    self.rate = layer_conf.dropout_rate or global_conf.dropout_rate
+    if self.rate == nil then
+        nerv.warning("[DropoutLayer:propagate] dropout rate is not set")
+    end
+    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 DropoutLayer: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 = {}
+    for t = 1, chunk_size do
+        self.mask[t] = self.mat_type(batch_size, self.dim_in[1])
+    end
+end
+
+function DropoutLayer: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] == nil or self.mask[t]:nrow() ~= batch_size then
+            self.mask[t] = self.mat_type(batch_size, self.dim_in[1])
+        end
+    end
+end
+
+function DropoutLayer:propagate(input, output, t)
+    if t == nil then
+        t = 1
+    end
+    if self.rate then
+        self.mask[t]:rand_uniform()
+        -- since we will lose a portion of the actvations, we multiply the
+        -- activations by 1 / (1 - rate) to compensate
+        self.mask[t]:thres_mask(self.mask[t], self.rate,
+                                0, 1 / (1.0 - self.rate))
+        output[1]:mul_elem(input[1], self.mask[t])
+    else
+        output[1]:copy_fromd(input[1])
+    end
+end
+
+function DropoutLayer:update(bp_err, input, output, t)
+    -- no params, therefore do nothing
+end
+
+function DropoutLayer:back_propagate(bp_err, next_bp_err, input, output, t)
+    if t == nil then
+        t = 1
+    end
+    if self.rate then
+        next_bp_err[1]:mul_elem(bp_err[1], self.mask[t])
+    else
+        next_bp_err[1]:copy_fromd(bp_err[1])
+    end
+end
+
+function DropoutLayer:get_params()
+    return nerv.ParamRepo({})
+end