Lastly, the noisy latents are passed back through the synthesis transform to produce an image reconstruction \(\tilde x\). Rate: tf.Tensor(, shape=(1,), dtype=float32) Y_tilde, rate = entropy_model(y, training=True) entropy_model = tfc.ContinuousBatchedEntropyModel( prior = tfc.NoisyLogistic(loc=0., scale=tf.linspace(.01, 2., 10))ĭuring training, tfc.ContinuousBatchedEntropyModel adds uniform noise, and uses the noise and the prior to compute a (differentiable) upper bound on the rate (the average number of bits necessary to encode the latent representation). As the scale approaches zero, a logistic distribution approaches a dirac delta (spike), but the added noise causes the "noisy" distribution to approach the uniform distribution instead. tfc.NoisyLogistic accounts for the fact that the latents have additive noise. For example, it could be a set of independent logistic distributions with different scales for each latent dimension. The "prior" is a probability density that we train to model the marginal distribution of the noisy latents. y_tilde = y + tf.random.uniform(y.shape, -.5. This is the same terminology as used in the paper End-to-end Optimized Image Compression. To model this in a differentiable way during training, we add uniform noise in the interval \((-.5. The latents will be quantized at test time. To get the latent representation \(y\), we need to cast it to float32, add a batch dimension, and pass it through the analysis transform. You should use `dataset.take(k).cache().repeat()` instead. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. 04:24:17.385236: W tensorflow/core/kernels/data/cache_dataset_ops.cc:854] The calling iterator did not fully read the dataset being cached. # Installs the latest version of TFC compatible with the installed TF version. More background on learned data compression can be found in this paper targeted at people familiar with classical data compression, or this survey targeted at a machine learning audience. The method is based on the paper End-to-end Optimized Image Compression. The examples below use an autoencoder-like model to compress images from the MNIST dataset. Lossy compression involves making a trade-off between rate, the expected number of bits needed to encode a sample, and distortion, the expected error in the reconstruction of the sample. This notebook shows how to do lossy data compression using neural networks and TensorFlow Compression.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |