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Documentation: Indicate missing defaults in flags. #144

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Documentation: Indicate missing defaults in flags. #144

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4 changes: 2 additions & 2 deletions doc/flags.md
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Expand Up @@ -16,15 +16,15 @@ The training script `train.lua` accepts the following command-line flags:
**Data options**:
- `-input_h5`, `-input_json`: Paths to the HDF5 and JSON files output from the preprocessing script.
- `-batch_size`: Number of sequences to use in a minibatch; default is 50.
- `-seq_length`: Number of timesteps for which the recurrent network is unrolled for backpropagation through time.
- `-seq_length`: Number of timesteps for which the recurrent network is unrolled for backpropagation through time; default is 50.

**Model options**:
- `-init_from`: Path to a checkpoint file from a previous run of `train.lua`. Use this to continue training from an existing checkpoint; if this flag is passed then the other flags in this section will be ignored and the architecture from the existing checkpoint will be used instead.
- `-reset_iterations`: Set this to 0 to restore the iteration counter of a previous run. Default is 1 (do not restore iteration counter). Only applicable if `-init_from` option is used.
- `-model_type`: The type of recurrent network to use; either `lstm` (default) or `rnn`. `lstm` is slower but better.
- `-wordvec_size`: Dimension of learned word vector embeddings; default is 64. You probably won't need to change this.
- `-rnn_size`: The number of hidden units in the RNN; default is 128. Larger values (256 or 512) are commonly used to learn more powerful models and for bigger datasets, but this will significantly slow down computation.
- `-dropout`: Amount of dropout regularization to apply after each RNN layer; must be in the range `0 <= dropout < 1`. Setting `dropout` to 0 disables dropout, and higher numbers give a stronger regularizing effect.
- `-dropout`: Amount of dropout regularization to apply after each RNN layer; must be in the range `0 <= dropout < 1`. Setting `dropout` to 0 disables dropout, and higher numbers give a stronger regularizing effect; default is 0.
- `-num_layers`: The number of layers present in the RNN; default is 2.

**Optimization options**:
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