fixed UNKNOW in sample.lua

sample.lua

@@ -86,6 +86,7 @@ for L = 1,checkpoint.opt.num_layers do
 end
 state_size = #current_state
 
+-- parse characters from a string
 function get_char(str)
     local len  = #str
     local left = 0
@@ -108,6 +109,7 @@ function get_char(str)
         left = left + i
 		unordered[#unordered+1] = tmpString
     end
+	return unordered
 end
 
 -- do a few seeded timesteps
@@ -116,8 +118,7 @@ if string.len(seed_text) > 0 then
     gprint('seeding with ' .. seed_text)
     gprint('--------------------------')
 	local chars = get_char(seed_text)
-	print(chars)
-    for i,c in ipairs(chars)'.' do
+    for i,c in ipairs(chars) do
         prev_char = torch.Tensor{vocab[c]}
         io.write(ivocab[prev_char[1]])
         if opt.gpuid >= 0 then prev_char = prev_char:cuda() end
@@ -139,6 +140,9 @@ end
 for i=1, opt.length do
 
     -- log probabilities from the previous timestep
+	-- make sure the output char is not UNKNOW
+	prev_char = 'UNKNOW'
+	while(prev_char == 'UNKNOW') do
 		if opt.sample == 0 then
 			-- use argmax
 			local _, prev_char_ = prediction:max(2)
@@ -150,6 +154,7 @@ for i=1, opt.length do
 			probs:div(torch.sum(probs)) -- renormalize so probs sum to one
 			prev_char = torch.multinomial(probs:float(), 1):resize(1):float()
 		end
+	end
 
     -- forward the rnn for next character
     local lst = protos.rnn:forward{prev_char, unpack(current_state)}

train.lua

@@ -32,6 +32,7 @@ cmd:text()
 cmd:text('Options')
 -- data
 cmd:option('-data_dir','data/tinyshakespeare','data directory. Should contain the file input.txt with input data')
+cmd:option('-min_freq',0,'min frequent of character')
 -- model params
 cmd:option('-rnn_size', 128, 'size of LSTM internal state')
 cmd:option('-num_layers', 2, 'number of layers in the LSTM')
@@ -59,7 +60,6 @@ cmd:option('-savefile','lstm','filename to autosave the checkpont to. Will be in
 -- GPU/CPU
 cmd:option('-gpuid',0,'which gpu to use. -1 = use CPU')
 cmd:option('-opencl',0,'use OpenCL (instead of CUDA)')
-cmd:option('-min_freq',0,'min frequent of character')
 cmd:text()
 
 -- parse input params