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Merge pull request tensorflow#31 from graphaelli/inception-doc-links
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update broken code links
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Vijay Vasudevan committed Mar 25, 2016
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Expand Up @@ -67,7 +67,7 @@ download and convert the ImageNet data to native TFRecord format. The TFRecord
format consists of a set of sharded files where each entry is a serialized
`tf.Example` proto. Each `tf.Example` proto contains the ImageNet image (JPEG
encoded) as well as metadata such as label and bounding box information. See
[`parse_example_proto`](image_processing.py) for details.
[`parse_example_proto`](inception/image_processing.py) for details.

We provide a single
[script](inception/data/download_and_preprocess_imagenet.sh)
Expand Down Expand Up @@ -155,7 +155,7 @@ We have tested several hardware setups for training this model from scratch but
we emphasize that depending your hardware set up, you may need to adapt the
batch size and learning rate schedule.

Please see the comments in `inception_train.py` for a few selected learning rate
Please see the comments in [`inception_train.py`](inception/inception_train.py) for a few selected learning rate
plans based on some selected hardware setups.

To train this model, you simply need to specify the following:
Expand Down Expand Up @@ -281,7 +281,7 @@ prediction from the model matched the ImageNet label -- in this case, 73.5%.
If you wish to run the eval just once and not periodically, append the
`--run_once` option.

Much like the training script, `imagenet_eval.py` also
Much like the training script, [`imagenet_eval.py`](inception/imagenet_eval.py) also
exports summaries that may be visualized in TensorBoard. These summaries
calculate additional statistics on the predictions (e.g. recall @ 5) as well
as monitor the statistics of the model activations and weights during
Expand All @@ -303,7 +303,7 @@ There is a single automated script that downloads the data set and converts
it to the TFRecord format. Much like the ImageNet data set, each record in the
TFRecord format is a serialized `tf.Example` proto whose entries include
a JPEG-encoded string and an integer label. Please see
[`parse_example_proto`](image_processing.py) for details.
[`parse_example_proto`](inception/image_processing.py) for details.

The script just takes a few minutes to run depending your network connection
speed for downloading and processing the images. Your hard disk requires 200MB
Expand Down Expand Up @@ -333,10 +333,10 @@ files in the `DATA_DIR`. The files will match the patterns
`train-????-of-00001` and `validation-?????-of-00001`, respectively.

**NOTE** If you wish to prepare a custom image data set for transfer learning,
you will need to invoke [`build_image_data.py`](data/build_image_data.py)
you will need to invoke [`build_image_data.py`](inception/data/build_image_data.py)
on your custom data set.
Please see the associated options and assumptions behind this script by reading
the comments section of [`build_image_data.py`](data/build_image_data.py).
the comments section of [`build_image_data.py`](inception/data/build_image_data.py).

The second piece you will need is a trained Inception v3 image model. You have
the option of either training one yourself (See
Expand Down Expand Up @@ -390,7 +390,7 @@ if you wish to continue training a pre-trained model from a checkpoint. If you
set this flag to true, you can train a new classification layer from scratch.

In order to understand how `--fine_tune` works, please see the discussion
on `Variables` in the TensorFlow-Slim [`README.md`](slim/README.md).
on `Variables` in the TensorFlow-Slim [`README.md`](inception/slim/README.md).

Putting this all together you can retrain a pre-trained Inception-v3 model
on the flowers data set with the following command.
Expand Down Expand Up @@ -472,7 +472,7 @@ Succesfully loaded model from /tmp/flowers/model.ckpt-1999 at step=1999.

One can use the existing scripts supplied with this model to build a new
dataset for training or fine-tuning. The main script to employ is
[`build_image_data.py`](./build_image_data.py). Briefly,
[`build_image_data.py`](inception/data/build_image_data.py). Briefly,
this script takes a structured
directory of images and converts it to a sharded `TFRecord` that can be read
by the Inception model.
Expand Down Expand Up @@ -503,12 +503,12 @@ unique label for the images that reside within that sub-directory. The images
may be JPEG or PNG images. We do not support other images types currently.

Once the data is arranged in this directory structure, we can run
`build_image_data.py` on the data to generate the sharded `TFRecord` dataset.
[`build_image_data.py`](inception/data/build_image_data.py) on the data to generate the sharded `TFRecord` dataset.
Each entry of the `TFRecord` is a serialized `tf.Example` protocol buffer.
A complete list of information contained in the `tf.Example` is described
in the comments of `build_image_data.py`.
in the comments of [`build_image_data.py`](inception/data/build_image_data.py).

To run `build_image_data.py`, you can run the following command line:
To run [`build_image_data.py`](inception/data/build_image_data.py), you can run the following command line:

```shell
# location to where to save the TFRecord data.
Expand Down Expand Up @@ -578,7 +578,7 @@ some general considerations for novices.

Roughly 5-10 hyper-parameters govern the speed at which a network is trained.
In addition to `--batch_size` and `--num_gpus`, there are several constants
defined in [inception_train.py](./inception_train.py) which dictate the
defined in [inception_train.py](inception/inception_train.py) which dictate the
learning schedule.

```shell
Expand Down Expand Up @@ -652,7 +652,7 @@ model architecture, this corresponds to 16GB of CPU memory. You may lower
`input_queue_memory_factor` in order to decrease the memory footprint. Keep
in mind though that lowering this value drastically may result in a model
with slightly lower predictive accuracy when training from scratch. Please
see comments in [`image_processing.py`](./image_processing.py) for more details.
see comments in [`image_processing.py`](inception/image_processing.py) for more details.

## Troubleshooting

Expand Down Expand Up @@ -693,7 +693,7 @@ the entire model architecture.
We targeted a desktop with 128GB of CPU ram connected to 8 NVIDIA Tesla K40
GPU cards but we have run this on desktops with 32GB of CPU ram and 1 NVIDIA
Tesla K40. You can get a sense of the various training configurations we
tested by reading the comments in [`inception_train.py`](./inception_train.py).
tested by reading the comments in [`inception_train.py`](inception/inception_train.py).



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