The TensorFlow official models are a collection of example models that use TensorFlow's high-level APIs. They are intended to be well-maintained, tested, and kept up to date with the latest TensorFlow API. They should also be reasonably optimized for fast performance while still being easy to read.
The TensorFlow official models are a collection of example models that use
TensorFlow's high-level APIs. They are intended to be well-maintained, tested,
and kept up to date with the latest TensorFlow API. They should also be
reasonably optimized for fast performance while still being easy to read.
These models are used as end-to-end tests, ensuring that the models run with the same speed and performance with each new TensorFlow build.
These models are used as end-to-end tests, ensuring that the models run with the
same speed and performance with each new TensorFlow build.
## Tensorflow releases
The master branch of the models are **in development**, and they target the [nightly binaries](https://github.com/tensorflow/tensorflow#installation) built from the [master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master). We aim to keep them backwards compatible with the latest release when possible (currently TensorFlow 1.5), but we cannot always guarantee compatibility.
**Stable versions** of the official models targeting releases of TensorFlow are available as tagged branches or [downloadable releases](https://github.com/tensorflow/models/releases). Model repository version numbers match the target TensorFlow release, such that [branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and [release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are compatible with [TensorFlow v1.4.0](https://github.com/tensorflow/tensorflow/releases/tag/v1.4.0).
If you are on a version of TensorFlow earlier than 1.4, please [update your installation](https://www.tensorflow.org/install/).
The master branch of the models are **in development**, and they target the
[nightly binaries](https://github.com/tensorflow/tensorflow#installation) built
from the
[master branch of TensorFlow](https://github.com/tensorflow/tensorflow/tree/master).
We aim to keep them backwards compatible with the latest release when possible
(currently TensorFlow 1.5), but we cannot always guarantee compatibility.
**Stable versions** of the official models targeting releases of TensorFlow are
available as tagged branches or
[downloadable releases](https://github.com/tensorflow/models/releases). Model
repository version numbers match the target TensorFlow release, such that
[branch r1.4.0](https://github.com/tensorflow/models/tree/r1.4.0) and
[release v1.4.0](https://github.com/tensorflow/models/releases/tag/v1.4.0) are
2. Add the top-level ***/models*** folder to the Python path with the command:
```
export PYTHONPATH="$PYTHONPATH:/path/to/models"
```
Using Colab:
```
import os
os.environ['PYTHONPATH'] += ":/path/to/models"
```
3. Install dependencies:
```
pip3 install --user -r official/requirements.txt
```
or
```
pip install --user -r official/requirements.txt
```
`export PYTHONPATH="$PYTHONPATH:/path/to/models"`
Using Colab: `import os os.environ['PYTHONPATH'] += ":/path/to/models"`
To make Official Models easier to use, we are planning to create a pip installable Official Models package. This is being tracked in [#917](https://github.com/tensorflow/models/issues/917).
3. Install dependencies: `pip3 install --user -r official/requirements.txt` or
`pip install --user -r official/requirements.txt`
To make Official Models easier to use, we are planning to create a pip
installable Official Models package. This is being tracked in
# BERT (Bidirectional Encoder Representations from Transformers)
Note> Please do not create pull request. This model is still under development
and testing.
The academic paper which describes BERT in detail and provides full results on a
number of tasks can be found here: https://arxiv.org/abs/1810.04805.
...
...
@@ -30,6 +27,31 @@ Our current released checkpoints are exactly the same as TF 1.x official BERT
repository, thus inside `BertConfig`, there is `backward_compatible=True`. We
are going to release new pre-trained checkpoints soon.
### Access to Pretrained Checkpoints
We provide checkpoints that are converted from [google-research/bert](https://github.com/google-research/bert),
in order to keep consistent with BERT paper.
***[`BERT-Large, Uncased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/tf_20/wwm_uncased_L-24_H-1024_A-16.tar.gz)**:
24-layer, 1024-hidden, 16-heads, 340M parameters
***[`BERT-Large, Cased (Whole Word Masking)`](https://storage.googleapis.com/cloud-tpu-checkpoints/bert/tf_20/wwm_cased_L-24_H-1024_A-16.tar.gz)**:
This provides a single scaffold to benchmark the Keras built-in application [models](https://keras.io/applications/). All the models are for image classification applications, and include:
- Xception
- VGG16
- VGG19
- ResNet50
- InceptionV3
- InceptionResNetV2
- MobileNet
- DenseNet
- NASNet
## Dataset
Synthetic dataset is used for the benchmark.
## Callbacks
Two custom callbacks are provided for model benchmarking: ExamplesPerSecondCallback and LoggingMetricCallback. For each callback, `epoch_based` and `batch_based` options are available to set the benchmark level. Check [model_callbacks.py](model_callbacks.py) for more details.
## Running Code
To benchmark a model, use `--model` to specify the model name. To perform the benchmark with eager execution, issue the following command:
```
python benchmark_main.py --model resnet50 --eager
```
Note that, if eager execution is enabled, only one GPU is utilized even if multiple GPUs are provided and multi_gpu_model is used.
To use distribution strategy in the benchmark, run the following:
