In my previous post, I explained how to implement autoencoders as TensorFlow Estimator. I thought it would be nice to add convolutional autoencoders in addition to the existing fully-connected autoencoder. So that’s what I did. Moreover, I added the option to extract the low-dimensional encoding of the encoder and visualize it in TensorBoard.
The complete source code is available at https://github.com/sebp/tf_autoencoder.
Why convolutions? For the fully-connected autoencoder, we reshaped each 28x28 image to a 784-dimensional feature vector. Next, we assigned a separate weight to each edge connecting one of 784 pixels to one of 128 neurons of the first hidden layer, which amounts to 100,352 weights (excluding biases) that need to be learned during training. For the last layer of the decoder, we need another 100,352 weights to reconstruct the full-size image. Considering that the whole autoencoder consists of 222,384 weights, it is obvious that these two layers dominate other layers by a large margin. When using higher resolution images, this imbalance becomes even more dramatic.
I recently started to use Google’s deep learning framework TensorFlow. Since version 1.3, TensorFlow includes a high-level interface inspired by scikit-learn. Unfortunately, as of version 1.4, only 3 different classification and 3 different regression models implementing the Estimator interface are included. To better understand the Estimator interface, Dataset API, and components in tf-slim, I started to implement a simple Autoencoder and applied it to the well-known MNIST dataset of handwritten digits. This post is about my journey and is split in the following sections:
Custom Estimators Autoencoder network architecture Autoencoder as TensorFlow Estimator Using the Dataset API Denoising Autocendoer I will assume that you are familiar with TensorFlow basics. The full code is available at https://github.com/sebp/tf_autoencoder. A second part on Convolutional Autoencoders is available too.
Today, I released a new version of scikit-survival. This release adds support for the latest version of scikit-learn (0.19) and pandas (0.21). In turn, support for Python 3.4, scikit-learn 0.18 and pandas 0.18 has been dropped.
I’m pleased to announce that scikit-survival version 0.4 has been released.
This release adds CoxnetSurvivalAnalysis, which implements an efficient algorithm to fit Cox’s proportional hazards model with LASSO, ridge, and elastic net penalty. This allows fitting a Cox model to high-dimensional data and perform feature selection. Moreover, it includes support for Windows with Python 3.5 and later by making the cvxopt package optional.
Today, I released a new version of scikit-survival, a Python module for survival analysis built on top of scikit-learn.
This release adds predict_survival_function and predict_cumulative_hazard_function to sksurv.linear_model.CoxPHSurvivalAnalysis, which return the survival function and cumulative hazard function using Breslow’s estimator.
Moreover, it fixes a build error on Windows (#3) and adds the sksurv.preprocessing.OneHotEncoder class, which can be used in a scikit-learn pipeline.