![]() ![]() To generate a sample \(z\) for the decoder during training, you can sample from the latent distribution defined by the parameters outputted by the encoder, given an input observation \(x\). Model the latent distribution prior \(p(z)\) as a unit Gaussian. This defines the conditional distribution of the observation \(p(x|z)\), which takes a latent sample \(z\) as input and outputs the parameters for a conditional distribution of the observation. Output log-variance instead of the variance directly for numerical stability. In this example, simply model the distribution as a diagonal Gaussian, and the network outputs the mean and log-variance parameters of a factorized Gaussian. This defines the approximate posterior distribution \(q(z|x)\), which takes as input an observation and outputs a set of parameters for specifying the conditional distribution of the latent representation \(z\). Let \(x\) and \(z\) denote the observation and latent variable respectively in the following descriptions. Use tf.keras.Sequential to simplify implementation. In the literature, these networks are also referred to as inference/recognition and generative models respectively. In this VAE example, use two small ConvNets for the encoder and decoder networks. Test_dataset = (tf._tensor_slices(test_images)ĭefine the encoder and decoder networks with tf.keras.Sequential Use tf.data to batch and shuffle the data train_dataset = (tf._tensor_slices(train_images) Test_images = preprocess_images(test_images) Train_images = preprocess_images(train_images) Model each pixel with a Bernoulli distribution in our model, and statically binarize the dataset. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.Įach MNIST image is originally a vector of 784 integers, each of which is between 0-255 and represents the intensity of a pixel. 06:01:32.056789: W tensorflow/compiler/tf2tensorrt/utils/py_:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. 06:01:32.056778: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer_plugin.so.7' dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 06:01:32.056666: W tensorflow/compiler/xla/stream_executor/platform/default/dso_:64] Could not load dynamic library 'libnvinfer.so.7' dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory Setup pip install tensorflow-probability # to generate gifs pip install imageio pip install git from IPython import display This approach produces a continuous, structured latent space, which is useful for image generation. Unlike a traditional autoencoder, which maps the input onto a latent vector, a VAE maps the input data into the parameters of a probability distribution, such as the mean and variance of a Gaussian. A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation. This notebook demonstrates how to train a Variational Autoencoder (VAE) ( 1, 2) on the MNIST dataset.
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