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ConcreteDropout-0.2.1
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Concrete Dropout implementation for Tensorflow 2 and PyTorch
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ConcreteDropout-0.2.1 |
| نام | ConcreteDropout |
| نسخه کتابخانه | 0.2.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Aurelio Amerio <aurelio.amerio@ific.uv.es> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/ConcreteDropout/ |
| مجوز | MIT License Copyright (c) 2022 Aurelio Amerio Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
# ConcreteDropout
[](https://pepy.tech/project/concretedropout)
[](https://pypi.org/project/concretedropout/)
Concrete Dropout updated implementation for Tensorflow 2.0 and PyTorch, following the [original code](https://github.com/yaringal/ConcreteDropout) from the paper.
# Installation
To install this package, please use:
```bash
pip install concretedropout
```
# Introduction
Concrete dropout allows for the dropout probability of a layer to become a trainable parameter. For more information, see the original paper: [https://arxiv.org/abs/1705.07832](https://arxiv.org/abs/1705.07832)
This package implements Concrete Dropout for the following layers:
Tensorflow:
- Dense - `tensorflow.ConcreteDenseDropout`
- Conv1D - `tensorflow.ConcreteSpatialDropout1D`
- Conv2D - `tensorflow.ConcreteSpatialDropout2D`
- Conv3D - `tensorflow.ConcreteSpatialDropout3D`
- DepthwiseConv1D - `tensorflow.ConcreteSpatialDropoutDepthwise1D`
- DepthwiseConv2D - `tensorflow.ConcreteSpatialDropoutDepthwise2D`
PyTorch:
- Linear - `pytorch.ConcreteLinearDropout`
- Conv1d - `pytorch.ConcreteDropout1d`
- Conv2d - `pytorch.ConcreteDropout2d`
- Conv3d - `pytorch.ConcreteDropout3d`
Please notice that the dropout layer will be applied **before** the chosen layer.
# Arguments
Each concrete dropout layer supports the following arguments:
- `layer`:
an instance of the layer to which concrete dropout will be applied. Only required for Tensorflow.
- `weight_regularizer=1e-6`:
A positive number which satisfies weight_regularizer = $l^2 / (\tau * N)$ with prior lengthscale l, model precision τ (inverse observation noise), and N the number of instances in the dataset.
Note that kernel_regularizer is not needed.
The appropriate weight_regularizer value can be computed with the utility function `get_weight_regularizer(N, l, tau)`
- `dropout_regularizer=1e-5`:
A positive number which satisfies dropout_regularizer = $2 / (\tau * N)$ with model precision τ (inverse observation noise) and N the number of instances in the dataset.
Note the relation between dropout_regularizer and weight_regularizer: weight_regularizer / dropout_regularizer = $l^2 / 2$ with prior lengthscale l. Note also that the factor of two should be
ignored for cross-entropy loss, and used only for the eculedian loss.
The appropriate dropout_regularizer value can be computed with the utility function `get_dropout_regularizer(N, tau, cross_entropy_loss=False)`. By default, a regression problem will be assumed.
- `init_min=0.1`: minimum value for the random initial dropout probability
- `init_max=0.1`: maximum value for the random initial dropout probability
- `is_mc_dropout=False`: enables Monte Carlo Dropout (i.e. dropout will remain active also at prediction time). Default: False.
- `data_format=None`: channels_last or channels_first (only for Tensorflow). Defaults to channels_last for Tensorflow. PyTorch defaults to channel_first.
- `temperature`: temperature of the concrete distribution. For more information see [arXiv:1611.00712](https://arxiv.org/abs/1611.00712). Defaults to `0.1` for dense layers, and `2/3` for convolution layers.
# Example
The suggested way to employ concrete dropout layers is the following.
Tensorflow:
```python
import tensorflow as tf
from concretedropout.tensorflow import ConcreteDenseDropout
#... import the dataset
Ns = x_train.shape[0]
# get the regularizers
wr = get_weight_regularizer(Ns, l=1e-2, tau=1.0) # tau is the inverse
dr = get_dropout_regularizer(Ns, tau=1.0, cross_entropy_loss=True)
# ... a neural network with output x
dense1 = tf.keras.layers.Dense(N_neurons)
x = ConcreteDenseDropout(dense1, weight_regularizer=wr, dropout_regularizer=dr)(x)
```
PyTorch:
```python
import torch
from concretedropout.pytorch import ConcreteLinearDropout
#... import the dataset
Ns = x_train.shape[0]
# get the regularizers
wr = get_weight_regularizer(Ns, l=1e-2, tau=1.0) # tau is the inverse
dr = get_dropout_regularizer(Ns, tau=1.0, cross_entropy_loss=True)
# ... a neural network with output x
linear = torch.nn.Linear(n_input, N_neurons)
x = ConcreteLinearDropout(weight_regularizer=wr, dropout_regularizer=dr)(x, linear)
# inside the train step of your model, you need to add a new regularization term, which is due to the concrete dropout:
def training_step(self, batch, batch_nb):
x, y = batch
output = self(x)
reg = torch.zeros(1) # get the regularization term
for module in filter(lambda x: isinstance(x, ConcreteDropout), self.modules()):
reg += module.regularization
loss = self.loss(output, y) + reg # add the reg term
return loss
```
For a practical example on how to use concrete dropout for the mnist dataset, see this [Tensorflow example](https://github.com/aurelio-amerio/ConcreteDropout-TF2/blob/main/examples/Tensorflowmnist_convnet_concrete_dropout.ipynb) and this [PyTorch example](https://github.com/aurelio-amerio/ConcreteDropout-TF2/blob/main/examples/PyTorch/MNIST_pytorch.ipynb).
# Bayesian neural network with MCDropout
You can find [here](https://github.com/aurelio-amerio/ConcreteDropout-TF2/blob/main/examples/regression_MCDropout.ipynb) an example on how to use MCDropout and Concrete Dropout to implement a Bayesian Neural Network with MCDropout on Tensorflow. For more information, see [arXiv:1506.02142](https://arxiv.org/abs/1506.02142).
# Known issues
Due to the way the additional dropout loss term is added to the main loss term, during training and evaluation the **model loss** might become a **negative number**. This has no impact on the actual optimisation of the model. If you desire to track your loss function separately, as a work around it is advised to add it to the list of metrics.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl ConcreteDropout-0.2.1:
pip install ConcreteDropout-0.2.1.whl
نصب پکیج tar.gz ConcreteDropout-0.2.1:
pip install ConcreteDropout-0.2.1.tar.gz