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local Layer = nerv.class('nerv.LayerT')
function Layer:__init(id, global_conf, layer_conf)
nerv.error_method_not_implemented()
end
function Layer:init(batch_size, chunk_size)
nerv.error_method_not_implemented()
end
function Layer:update(bp_err, input, output, t)
nerv.error_method_not_implemented()
end
function Layer:propagate(input, output, t)
nerv.error_method_not_implemented()
end
function Layer:back_propagate(bp_err, next_bp_err, input, output, t)
nerv.error_method_not_implemented()
end
function Layer:check_dim_len(len_in, len_out)
local expected_in = #self.dim_in
local expected_out = #self.dim_out
if len_in > 0 and expected_in ~= len_in then
nerv.error("layer %s expects %d inputs, %d given",
self.id, len_in, expected_in)
end
if len_out > 0 and expected_out ~= len_out then
nerv.error("layer %s expects %d outputs, %d given",
self.id, len_out, expected_out)
end
end
function Layer:get_params()
nerv.error_method_not_implemented()
end
function Layer:get_dim()
return self.dim_in, self.dim_out
end
nerv.include('tnn.lua')
nerv.include('softmax_ce_t.lua')
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