description: Data compression in TensorFlow.

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Data compression in TensorFlow.

`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 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 deep factorized distribution + uniform noise.

`class NoisySoftRoundedNormal`

: Soft rounded normal distribution + uniform noise.

`class PackedTensors`

: Packed representation of compressed tensors.

`class Parameter`

: Reparameterized `Layer`

variable.

`class RDFTParameter`

: RDFT reparameterization of a convolution kernel.

`class Round`

: Applies rounding.

`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.

`estimate_tails(...)`

: Estimates approximate tail quantiles.

`irdft_matrix(...)`

: Matrix for implementing kernel reparameterization with `tf.matmul`

.

`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.

`range_decode(...)`

: Range-decodes `code`

into an int32 tensor of shape `shape`

.

`range_encode(...)`

: Range encodes integer `data`

with a finite alphabet.

`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().

`unbounded_index_range_decode(...)`

: Range decodes `encoded`

using an indexed probability table.

`unbounded_index_range_encode(...)`

: Range encodes unbounded integer `data`

using an indexed probability table.

`upper_bound(...)`

: Same as `tf.minimum`

, but with helpful gradient for `inputs > bound`

.

`upper_tail(...)`

: Approximates upper tail quantile for range coding.