tf_autoencoder
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sebp
Denoising autoencoder implementation as TensorFlow estimator
Denoising Autoencoder as TensorFlow estimator
This repository contains an implementation of a (Denoising) Autoencoder using TensorFlow's Estimator and Dataset API. You can find a more detailed description in my blog post.
Two flavors of autoencoders are currently implemented
-
fully_connected: Uses fully-connected layers with 128, 64, and 32 units, respectively. -
convolutional: Uses 2D convolutional layers with 3x3 kernels and 16, 8, and 8 filters, respectively.
Training
You can train an autoencoder on the MNIST dataset using
train_mnist_autoencoder.py.
The first argument specifies which autoencoder to train
(fully_connected or convolutional), the remaining arguments
are listed below.
| Argument | Description |
|---|---|
| --model_dir | Output directory for model and training stats. |
| --data_dir | Directory to download the data to. |
| --noise_factor | Amount of noise to add to input (default: 0) |
| --learning_rate | Learning rate (default: 0.001) |
| --batch_size | Batch size (default: 256) |
| --epochs | Number of epochs to perform for training (default: 50) |
| --weight_decay | Amount of weight decay to apply (default: 1e-5) |
| --dropout | The probability that each element is kept in dropout layers (default: 1) |
| --save-images | Path to directory to store pairs of input and reconstructed images after each epoch (default: disabled) |
If --noise_factor is non-zero, a denoising autoencoder will be trained.
Prediction
After training an autoencoder, you can inspect the model using
test_mnist_autoencoder.py.
You can visualize a model's predictions on the MNIST test dataset or
save the output of the encoder to display it in TensorBoard.
The first argument specifies which autoencoder to evaluate
(fully_connected or convolutional), the remaining arguments
are listed below.
| Argument | Description |
|---|---|
| --model_dir | Output directory for model and training stats. |
| --data_dir | Directory to download the data to. |
| --noise_factor | Amount of noise to add to input (default: 0) |
| --dropout | The probability that each element is kept in dropout layers (default: 1) |
| --images | Number of test images to reconstruct or save embedding for (default: 10) |
| --what | How to visualize a trained model. reconstruction displays input and reconstructed images next to each other. embedding creates a checkpoint that can be used with TensorBoard to visualize the low-dimensional embedding of the encoder (default: reconstruction) |
Make sure you pass the same values you used during training.
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