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ModelZoo
ResNet50_tensorflow
Commits
d523161e
Commit
d523161e
authored
Jun 15, 2016
by
Lukasz Kaiser
Committed by
GitHub
Jun 15, 2016
Browse files
Merge pull request #189 from orionr/neuralgpu-update-docs
Update neural_gpu documentation.
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neural_gpu/README.md
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@@ -6,6 +6,57 @@ Requirements:
...
@@ -6,6 +6,57 @@ Requirements:
*
TensorFlow (see tensorflow.org for how to install)
*
TensorFlow (see tensorflow.org for how to install)
*
Matplotlib for Python (sudo apt-get install python-matplotlib)
*
Matplotlib for Python (sudo apt-get install python-matplotlib)
Run: python neural_gpu_trainer.py --task=rev
The model can be trained on the following algorithmic tasks:
*
`sort`
- Sort a symbol list
*
`kvsort`
- Sort symbol keys in dictionary
*
`id`
- Return the same symbol list
*
`rev`
- Reverse a symbol list
*
`rev2`
- Reverse a symbol dictionary by key
*
`incr`
- Add one to a symbol value
*
`add`
- Long decimal addition
*
`left`
- First symbol in list
*
`right`
- Last symbol in list
*
`left-shift`
- Left shift a symbol list
*
`right-shift`
- Right shift a symbol list
*
`bmul`
- Long binary multiplication
*
`mul`
- Long decimal multiplication
*
`dup`
- Duplicate a symbol list with padding
*
`badd`
- Long binary addition
*
`qadd`
- Long quaternary addition
*
`search`
- Search for symbol key in dictionary
The value range for symbols are defined by the
`niclass`
and
`noclass`
flags.
In particular, the values are in the range
`min(--niclass, noclass) - 1`
.
So if you set
`--niclass=33`
and
`--noclass=33`
(the default) then
`--task=rev`
will be reversing lists of 32 symbols, and
`--task=id`
will be identity on a
list of up to 32 symbols.
To train the model on the reverse task run:
```
python neural_gpu_trainer.py --task=rev
```
While training, interim / checkpoint model parameters will be
written to
`/tmp/neural_gpu/`
.
Once the amount of error gets down to what you're comfortable
with, hit
`Ctrl-C`
to stop the training process. The latest
model parameters will be in
`/tmp/neural_gpu/neural_gpu.ckpt-<step>`
and used on any subsequent run.
To test a trained model on how well it decodes run:
```
python neural_gpu_trainer.py --task=rev --mode=1
```
To produce an animation of the result run:
```
python neural_gpu_trainer.py --task=rev --mode=1 --animate=True
```
Maintained by Lukasz Kaiser (lukaszkaiser)
Maintained by Lukasz Kaiser (lukaszkaiser)
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