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Changes in torch #197

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9 changes: 4 additions & 5 deletions Readme.md
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Expand Up @@ -14,14 +14,13 @@ If you are new to Torch/Lua/Neural Nets, it might be helpful to know that this c
This code is written in Lua and requires [Torch](http://torch.ch/). If you're on Ubuntu, installing Torch in your home directory may look something like:

```bash
$ curl -s https://raw.githubusercontent.com/torch/ezinstall/master/install-deps | bash
$ git clone https://github.com/torch/distro.git ~/torch --recursive
$ cd ~/torch;
$ ./install.sh # and enter "yes" at the end to modify your bashrc
$ cd ~/torch; bash install-deps;
$ ./install.sh
$ source ~/.bashrc
```

See the Torch installation documentation for more details. After Torch is installed we need to get a few more packages using [LuaRocks](https://luarocks.org/) (which already came with the Torch install). In particular:
See the [Torch installation documentation](http://torch.ch/docs/getting-started.html) for more details. After Torch is installed we need to get a few more packages using [LuaRocks](https://luarocks.org/) (which already came with the Torch install). In particular:

```bash
$ luarocks install nngraph
Expand Down Expand Up @@ -115,7 +114,7 @@ The two most important parameters that control the model are `rnn_size` and `num
These two should be about the same order of magnitude. It's a little tricky to tell. Here are some examples:

- I have a 100MB dataset and I'm using the default parameter settings (which currently print 150K parameters). My data size is significantly larger (100 mil >> 0.15 mil), so I expect to heavily underfit. I am thinking I can comfortably afford to make `rnn_size` larger.
- I have a 10MB dataset and running a 10 million parameter model. I'm slightly nervous and I'm carefully monitoring my validation loss. If it's larger than my training loss then I may want to try to increase dropout a bit and see if that heps the validation loss.
- I have a 10MB dataset and running a 10 million parameter model. I'm slightly nervous and I'm carefully monitoring my validation loss. If it's larger than my training loss then I may want to try to increase dropout a bit and see if that helps the validation loss.

### Best models strategy

Expand Down