Concrete-Dropout

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Concrete-Dropout

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A clean TensorFlow implementation of Concrete Dropout

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# Concrete Dropout

TensorFlow implementation of Concrete Dropout, as presented in the paper: "Concrete Dropout." Yarin Gal, Jiri Hron, Alex Kendall. [ArXiv 2017](https://arxiv.org/abs/1705.07832).

## Usage

The concrete dropout layer exposes the same API as other classes in `tf.layers`. It additionally returns a kernel/bias regularizer, which should be fed into the subsequent dense or convolutional layer. When the `training` argument is set to `False`, the layer is disabled, which is equivalent to setting the dropout rate to 0. See `mnist.py` for a concrete (!) example.

```python
from concrete_dropout import concrete_dropout
from tensorflow import layers as tfl

...

dropped, reg = concrete_dropout(
    inputs,
    weight_regularizer=wr,
    dropout_regularizer=dr,
    init_min=init_rate,
    init_max=init_rate,
    name='concrete_dropout',
    training=training)
outputs = tfl.conv2d(
    dropped,
    filters,
    kernel_size,
    kernel_regularizer=reg,
    bias_regularizer=reg,
    name='conv')
```

Don't forget to add the regularisers to your loss when you're done with building the graph:

```python
loss += tf.reduce_sum(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
```

Finally, you can obtain the summaries for the learned rates with:

```python
rates = tf.get_collection('DROPOUT_RATES')
for r in rates:
    tf.summary.scalar(r.name, r)
```

## Credits

[Original implementation](https://github.com/yaringal/ConcreteDropout) by Yarin Gal.

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