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Data compression in TensorFlow.
Modules
distributions module: Distributions, based on tfp.distributions.Distribution.
entropy_models module: Entropy models.
layers module: Layers, based on tf.keras.layers.Layer.
ops module: TensorFlow operations and functions.
Classes
class ContinuousBatchedEntropyModel: Batched entropy model for continuous random variables.
class ContinuousIndexedEntropyModel: Indexed entropy model for continuous random variables.
class DeepFactorized: Fully factorized distribution based on neural network cumulative.
class GDN: Generalized divisive normalization layer.
class GDNParameter: Nonnegative parameterization as needed for GDN parameters.
class IdentityInitializer: Initialize to the identity kernel with the given shape.
class LocationScaleIndexedEntropyModel: Indexed entropy model for location-scale family of random variables.
class MonotonicAdapter: Adapt a continuous distribution via an ascending monotonic function.
class NoisyDeepFactorized: DeepFactorized that is convolved with uniform noise.
class NoisyLaplace: Laplacian distribution with additive i.i.d. uniform noise.
class NoisyLogistic: Logistic distribution with additive i.i.d. uniform noise.
class NoisyLogisticMixture: Mixture of logistic distributions with additive i.i.d. uniform noise.
class NoisyMixtureSameFamily: Mixture of distributions with additive i.i.d. uniform noise.
class NoisyNormal: Gaussian distribution with additive i.i.d. uniform noise.
class NoisyNormalMixture: Mixture of normal distributions with additive i.i.d. uniform noise.
class NoisyRoundedDeepFactorized: Rounded DeepFactorized + uniform noise.
class NoisyRoundedNormal: Rounded normal distribution + uniform noise.
class NoisySoftRoundedDeepFactorized: Soft rounded DeepFactorized + uniform noise.
class NoisySoftRoundedNormal: Soft rounded normal distribution + uniform noise.
class PackedTensors: Packed representation of compressed tensors.
class Parameter: Reparameterized Layer variable.
class PowerLawEntropyModel: Entropy model for power-law distributed random variables.
class RDFTParameter: RDFT reparameterization of a convolution kernel.
class RoundAdapter: Continuous density function + round.
class SignalConv1D: 1D convolution layer.
class SignalConv2D: 2D convolution layer.
class SignalConv3D: 3D convolution layer.
class SoftRound: Applies a differentiable approximation of rounding.
class SoftRoundAdapter: Differentiable approximation to round.
class SoftRoundConditionalMean: Conditional mean of inputs given noisy soft rounded values.
class UniformNoiseAdapter: Additive i.i.d. uniform noise adapter distribution.
class UniversalBatchedEntropyModel: Batched entropy model model which implements Universal Quantization.
class UniversalIndexedEntropyModel: Indexed entropy model model which implements Universal Quantization.
class Y4MDataset: A tf.Dataset of Y'CbCr video frames from '.y4m' files.
Functions
create_range_decoder(...): Creates range decoder objects to be used by EntropyDecode* ops.
create_range_encoder(...): Creates range encoder objects to be used by EntropyEncode* ops.
entropy_decode_channel(...): Decodes the encoded stream inside handle.
entropy_decode_finalize(...): Finalizes the decoding process.
entropy_decode_index(...): Decodes the encoded stream inside handle.
entropy_encode_channel(...): Encodes each input in value.
entropy_encode_finalize(...): Finalizes the encoding process and extracts byte stream from the encoder.
entropy_encode_index(...): Encodes each input in value according to a distribution selected by index.
estimate_tails(...): Estimates approximate tail quantiles.
lower_bound(...): Same as tf.maximum, but with helpful gradient for inputs < bound.
lower_tail(...): Approximates lower tail quantile for range coding.
perturb_and_apply(...): Perturbs the inputs of a pointwise function.
pmf_to_quantized_cdf(...): Converts a PMF into a quantized CDF for range coding.
quantization_offset(...): Computes distribution-dependent quantization offset.
round_st(...): Straight-through round with optional quantization offset.
run_length_decode(...): Decodes data using run-length coding.
run_length_encode(...): Encodes data using run-length coding.
run_length_gamma_decode(...): Decodes data using run-length and Elias gamma coding.
run_length_gamma_encode(...): Encodes data using run-length and Elias gamma coding.
same_padding_for_kernel(...): Determine correct amount of padding for same convolution.
soft_round(...): Differentiable approximation to round.
soft_round_conditional_mean(...): Conditional mean of inputs given noisy soft rounded values.
soft_round_inverse(...): Inverse of soft_round.
stochastic_round(...): Rounds inputs / step_size stochastically.
upper_bound(...): Same as tf.minimum, but with helpful gradient for inputs > bound.
upper_tail(...): Approximates upper tail quantile for range coding.