Options -data_dir data directory. Should contain the file input.txt with input data [data/tinyshakespeare] 训练语料 -min_freq min frequent of character  -rnn_size size of LSTM internal state  -num_layers number of layers in the LSTM  -model for now only lstm is supported. keep fixed [lstm] -learning_rate learning rate [0.002] -learning_rate_decay learning rate decay [0.97] -learning_rate_decay_after in number of epochs, when to start decaying the learning rate  -decay_rate decay rate for rmsprop [0.95] -dropout dropout for regularization, used after each RNN hidden layer. 0 = no dropout  -seq_length number of timesteps to unroll for  -batch_size number of sequences to train on in parallel  -max_epochs number of full passes through the training data  -grad_clip clip gradients at this value  -train_frac fraction of data that goes into train set [0.95] -val_frac fraction of data that goes into validation set [0.05] -init_from initialize network parameters from checkpoint at this path  -seed torch manual random number generator seed  -print_every how many steps/minibatches between printing out the loss  -eval_val_every every how many iterations should we evaluate on validation data?  -checkpoint_dir output directory where checkpoints get written [cv] -savefile filename to autosave the checkpont to. Will be inside checkpoint_dir/ [lstm] -accurate_gpu_timing set this flag to 1 to get precise timings when using GPU. Might make code bit slower but reports accurate timings.  -gpuid which gpu to use. -1 = use CPU  -opencl use OpenCL (instead of CUDA)  -use_ss whether use scheduled sampling during training  -start_ss start amount of truth data to be given to the model when using ss  -decay_ss ss amount decay rate of each epoch [0.005] -min_ss minimum amount of truth data to be given to the model when using ss [0.9]
th train.lua -data_dir data/tinyshakespeare/shakespeare_input.txt -gpuid -1 vocab.t7 and data.t7 do not exist. Running preprocessing... one-time setup: preprocessing input text file data/tinyshakespeare/shakespeare_input.txt/input.txt... loading text file... /home/frank/torch/install/bin/luajit: cannot open <data/tinyshakespeare/shakespeare_input.txt/input.txt> in mode r at /home/frank/torch/pkg/torch/lib/TH/THDiskFile.c:649 stack traceback: [C]: at 0x7f9c42473540 [C]: in function 'DiskFile' ./util/CharSplitLMMinibatchLoader.lua:201: in function 'text_to_tensor' ./util/CharSplitLMMinibatchLoader.lua:38: in function 'create' train.lua:118: in main chunk [C]: in function 'dofile' ...rank/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:145: in main chunk [C]: at 0x00405d70
改写的，我认为或许使用karpathy作者的原版本教程可能会更加方便一些。于是使用As a sanity check，运行：
th train.lua -gpuid -1
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
th train.lua -gpuid -1 loading data files... cutting off end of data so that the batches/sequences divide evenly reshaping tensor... data load done. Number of data batches in train: 423, val: 23, test: 0 vocab size: 65 creating an LSTM with 2 layers setting forget gate biases to 1 in LSTM layer 1 setting forget gate biases to 1 in LSTM layer 2 number of parameters in the model: 240321 cloning rnn cloning criterion 1/21150 (epoch 0.002), train_loss = 4.19803724, grad/param norm = 5.1721e-01, time/batch = 2.3129s 2/21150 (epoch 0.005), train_loss = 3.93712133, grad/param norm = 1.4679e+00, time/batch = 2.3114s 3/21150 (epoch 0.007), train_loss = 3.43764434, grad/param norm = 9.5800e-01, time/batch = 2.3022s 4/21150 (epoch 0.009), train_loss = 3.41313742, grad/param norm = 7.5143e-01, time/batch = 2.5311s 5/21150 (epoch 0.012), train_loss = 3.33707270, grad/param norm = 6.9269e-01, time/batch = 2.4913s
Options <model> model checkpoint to use for sampling -seed random number generator's seed  -sample 0 to use max at each timestep, 1 to sample at each timestep  -primetext used as a prompt to "seed" the state of the LSTM using a given sequence, before we sample.  -length max number of characters to sample  采样字符大小，最大2000 -temperature temperature of sampling  -gpuid which gpu to use. -1 = use CPU  和训练时设置应该保持一致 -verbose set to 0 to ONLY print the sampled text, no diagnostics  -stop stop sampling when detected [
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