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tfp.experimental.substrates.jax.distributions.PixelCNN

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The Pixel CNN++ distribution.

Inherits From: Distribution

tfp.experimental.substrates.jax.distributions.PixelCNN(
    image_shape, conditional_shape=None, num_resnet=5, num_hierarchies=3,
    num_filters=160, num_logistic_mix=10, receptive_field_dims=(3, 3),
    dropout_p=0.5, resnet_activation='concat_elu', use_weight_norm=True,
    use_data_init=True, high=255, low=0, dtype=tf.float32, name='PixelCNN'
)
Pixel CNN++ [(Salimans et al., 2017)][1] models a distribution over image
data, parameterized by a neural network. It builds on Pixel CNN and
Conditional Pixel CNN, as originally proposed by [(van den Oord et al.,
2016)][2, 3]. The model expresses the joint distribution over pixels as
the product of conditional distributions: `p(x|h) = prod{ p(x[i] | x[0:i], h)
i=0, ..., d }, in whichp(x[i] | x[0:i], h) : i=0, ..., dis the probability of thei-th pixel conditional on the pixels that preceded it in raster order (color channels in RGB order, then left to right, then top to bottom).his optional additional data on which to condition the image distribution, such as class labels or VAE embeddings. The Pixel CNN++ network enforces the dependency structure among pixels by applying a mask to the kernels of the convolutional layers that ensures that the values for each pixel depend only on other pixels up and to the left (seetfd.PixelCnnNetwork`).

Pixel values are modeled with a mixture of quantized logistic distributions, which can take on a set of distinct integer values (e.g. between 0 and 255 for an 8-bit image).

Color intensity v of each pixel is modeled as:

`v ~ sum{q[i] * quantized_logistic(loc[i], scale[i]) : i = 0, ..., k },

in which k is the number of mixture components and the q[i] are the Categorical probabilities over the components.

Sampling

Pixels are sampled one at a time, in raster order. This enforces the autoregressive dependency structure, in which the sample of pixel i is conditioned on the samples of pixels 1, ..., i-1. A single color image is sampled as follows:

samples = random_uniform([image_height, image_width, image_channels])
for i in image_height:
  for j in image_width:
    component_logits, locs, scales, coeffs = pixel_cnn_network(samples)
    components = Categorical(component_logits).sample()
    locs = gather(locs, components)
    scales = gather(scales, components)

    coef_count = 0
    channel_samples = []
    for k in image_channels:
      loc = locs[k]
      for m in range(k):
        loc += channel_samples[m] * coeffs[coef_count]
        coef_count += 1
      channel_samp = Logistic(loc, scales[k]).sample()
      channel_samples.append(channel_samp)
    samples[i, j, :] = tf.stack(channel_samples, axis=-1)
samples = round(samples)

Examples


# Build a small Pixel CNN++ model to train on MNIST.

from tensorflow_probability.python.internal.backend import jax as tf
import tensorflow_datasets as tfds
import tensorflow_probability as tfp; tfp = tfp.experimental.substrates.jax

tfd = tfp.distributions
tfk = tf.keras
tfkl = tf.keras.layers

tf.enable_v2_behavior()

# Load MNIST from tensorflow_datasets
data = tfds.load('mnist')
train_data, test_data = data['train'], data['test']

def image_preprocess(x):
  x['image'] = tf.cast(x['image'], tf.float32)
  return (x['image'],)  # (input, output) of the model

batch_size = 16
train_it = train_data.map(image_preprocess).batch(batch_size).shuffle(1000)

# Define a Pixel CNN network
dist = tfd.PixelCNN(
    image_shape=(28, 28, 1),
    num_resnet=1,
    num_hierarchies=2,
    num_filters=32,
    num_logistic_mix=5,
    dropout_p=.3,
)

# Define the model input
image_input = tfkl.Input(shape=input_shape)

# Define the log likelihood for the loss fn
log_prob = dist.log_prob(image_input)

# Define the model
model = tfk.Model(inputs=image_input, outputs=log_prob)
model.add_loss(-tf.reduce_mean(log_prob))

# Compile and train the model
model.compile(
    optimizer=tfk.optimizers.Adam(.001),
    metrics=[])

model.fit(train_it, epochs=10, verbose=True)

# sample five images from the trained model
samples = dist.sample(5)

To train a class-conditional model:


data = tfds.load('mnist')
train_data, test_data = data['train'], data['test']

def image_preprocess(x):
  x['image'] = tf.cast(x['image'], tf.float32)
  # return model (inputs, outputs): inputs are (image, label) and there are no
  # outputs
  return ((x['image'], x['label']),)

batch_size = 16
train_ds = train_data.map(image_preprocess).batch(batch_size).shuffle(1000)
optimizer = tfk.optimizers.Adam()

image_shape = (28, 28, 1)
label_shape = ()
dist = tfd.PixelCNN(
    image_shape=image_shape,
    conditional_shape=label_shape,
    num_resnet=1,
    num_hierarchies=2,
    num_filters=32,
    num_logistic_mix=5,
    dropout_p=.3,
)

image_input = tfkl.Input(shape=image_shape)
label_input = tfkl.Input(shape=label_shape)

log_prob = dist.log_prob(image_input, conditional_input=label_input)

class_cond_model = tfk.Model(
    inputs=[image_input, label_input], outputs=log_prob)
class_cond_model.add_loss(-tf.reduce_mean(log_prob))
class_cond_model.compile(
    optimizer=tfk.optimizers.Adam(),
    metrics=[])
class_cond_model.fit(train_ds, epochs=10)

# Take 10 samples of the digit '5'
samples = dist.sample(10, conditional_input=5.)

# Take 4 samples each of the digits '1', '2', '3'.
# Note that when a batch of conditional input is passed, the sample shape
# (the first argument of `dist.sample`) must have its last dimension(s) equal
# the batch shape of the conditional input (here, (3,)).
samples = dist.sample((4, 3), conditional_input=[1., 2., 3.])

References

[1]: Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma. PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications. In International Conference on Learning Representations, 2017. https://pdfs.semanticscholar.org/9e90/6792f67cbdda7b7777b69284a81044857656.pdf Additional details at https://github.com/openai/pixel-cnn

[2]: Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu. Conditional Image Generation with PixelCNN Decoders. In Neural Information Processing Systems, 2016. https://arxiv.org/abs/1606.05328

[3]: Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel Recurrent Neural Networks. In International Conference on Machine Learning, 2016. https://arxiv.org/pdf/1601.06759.pdf

Args:

  • image_shape: 3D TensorShape or tuple for the [height, width, channels] dimensions of the image.
  • conditional_shape: TensorShape or tuple for the shape of the conditional input, or None if there is no conditional input.
  • num_resnet: int, the number of layers (shown in Figure 2 of [2]) within each highest-level block of Figure 2 of [1].
  • num_hierarchies: int, the number of hightest-level blocks (separated by expansions/contractions of dimensions in Figure 2 of [1].)
  • num_filters: int, the number of convolutional filters.
  • num_logistic_mix: int, number of components in the logistic mixture distribution.
  • receptive_field_dims: tuple, height and width in pixels of the receptive field of the convolutional layers above and to the left of a given pixel. The width (second element of the tuple) should be odd. Figure 1 (middle) of [2] shows a receptive field of (3, 5) (the row containing the current pixel is included in the height). The default of (3, 3) was used to produce the results in [1].
  • dropout_p: float, the dropout probability. Should be between 0 and 1.
  • resnet_activation: string, the type of activation to use in the resnet blocks. May be 'concat_elu', 'elu', or 'relu'.
  • use_weight_norm: bool, if True then use weight normalization (works only in Eager mode).
  • use_data_init: bool, if True then use data-dependent initialization (has no effect if use_weight_norm is False).
  • high: int, the maximum value of the input data (255 for an 8-bit image).
  • low: int, the minimum value of the input data.
  • dtype: Data type of the Distribution.
  • name: string, the name of the Distribution.

Attributes:

  • allow_nan_stats: Python bool describing behavior when a stat is undefined.

    Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.

  • batch_shape: Shape of a single sample from a single event index as a TensorShape.

    May be partially defined or unknown.

    The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

  • dtype: The DType of Tensors handled by this Distribution.

  • event_shape: Shape of a single sample from a single batch as a TensorShape.

    May be partially defined or unknown.

  • name: Name prepended to all ops created by this Distribution.

  • parameters: Dictionary of parameters used to instantiate this Distribution.

  • reparameterization_type: Describes how samples from the distribution are reparameterized.

    Currently this is one of the static instances tfd.FULLY_REPARAMETERIZED or tfd.NOT_REPARAMETERIZED.

  • trainable_variables

  • validate_args: Python bool indicating possibly expensive checks are enabled.

  • variables

Methods

__getitem__

View source

__getitem__(
    slices
)

Slices the batch axes of this distribution, returning a new instance.

b = tfd.Bernoulli(logits=tf.zeros([3, 5, 7, 9]))
b.batch_shape  # => [3, 5, 7, 9]
b2 = b[:, tf.newaxis, ..., -2:, 1::2]
b2.batch_shape  # => [3, 1, 5, 2, 4]

x = tf.random.normal([5, 3, 2, 2])
cov = tf.matmul(x, x, transpose_b=True)
chol = tf.cholesky(cov)
loc = tf.random.normal([4, 1, 3, 1])
mvn = tfd.MultivariateNormalTriL(loc, chol)
mvn.batch_shape  # => [4, 5, 3]
mvn.event_shape  # => [2]
mvn2 = mvn[:, 3:, ..., ::-1, tf.newaxis]
mvn2.batch_shape  # => [4, 2, 3, 1]
mvn2.event_shape  # => [2]

Args:

  • slices: slices from the [] operator

Returns:

  • dist: A new tfd.Distribution instance with sliced parameters.

__iter__

View source

__iter__()

batch_shape_tensor

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batch_shape_tensor(
    name='batch_shape_tensor'
)

Shape of a single sample from a single event index as a 1-D Tensor.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Args:

  • name: name to give to the op

Returns:

  • batch_shape: Tensor.

cdf

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cdf(
    value, name='cdf', **kwargs
)

Cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

cdf(x) := P[X <= x]

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

copy

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copy(
    **override_parameters_kwargs
)

Creates a deep copy of the distribution.

Args:

  • **override_parameters_kwargs: String/value dictionary of initialization arguments to override with new values.

Returns:

  • distribution: A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

covariance

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covariance(
    name='covariance', **kwargs
)

Covariance.

Covariance is (possibly) defined only for non-scalar-event distributions.

For example, for a length-k, vector-valued distribution, it is calculated as,

Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]

where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E denotes expectation.

Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), Covariance shall return a (batch of) matrices under some vectorization of the events, i.e.,

Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]

where Cov is a (batch of) k' x k' matrices, 0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function mapping indices of this distribution's event dimensions to indices of a length-k' vector.

Args:

  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • covariance: Floating-point Tensor with shape [B1, ..., Bn, k', k'] where the first n dimensions are batch coordinates and k' = reduce_prod(self.event_shape).

cross_entropy

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cross_entropy(
    other, name='cross_entropy'
)

Computes the (Shannon) cross entropy.

Denote this distribution (self) by P and the other distribution by Q. Assuming P, Q are absolutely continuous with respect to one another and permit densities p(x) dr(x) and q(x) dr(x), (Shannon) cross entropy is defined as:

H[P, Q] = E_p[-log q(X)] = -int_F p(x) log q(x) dr(x)

where F denotes the support of the random variable X ~ P.

Args:

Returns:

  • cross_entropy: self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of (Shannon) cross entropy.

entropy

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entropy(
    name='entropy', **kwargs
)

Shannon entropy in nats.

event_shape_tensor

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event_shape_tensor(
    name='event_shape_tensor'
)

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:

  • name: name to give to the op

Returns:

  • event_shape: Tensor.

is_scalar_batch

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is_scalar_batch(
    name='is_scalar_batch'
)

Indicates that batch_shape == [].

Args:

  • name: Python str prepended to names of ops created by this function.

Returns:

  • is_scalar_batch: bool scalar Tensor.

is_scalar_event

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is_scalar_event(
    name='is_scalar_event'
)

Indicates that event_shape == [].

Args:

  • name: Python str prepended to names of ops created by this function.

Returns:

  • is_scalar_event: bool scalar Tensor.

kl_divergence

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kl_divergence(
    other, name='kl_divergence'
)

Computes the Kullback--Leibler divergence.

Denote this distribution (self) by p and the other distribution by q. Assuming p, q are absolutely continuous with respect to reference measure r, the KL divergence is defined as:

KL[p, q] = E_p[log(p(X)/q(X))]
         = -int_F p(x) log q(x) dr(x) + int_F p(x) log p(x) dr(x)
         = H[p, q] - H[p]

where F denotes the support of the random variable X ~ p, H[., .] denotes (Shannon) cross entropy, and H[.] denotes (Shannon) entropy.

Args:

Returns:

  • kl_divergence: self.dtype Tensor with shape [B1, ..., Bn] representing n different calculations of the Kullback-Leibler divergence.

log_cdf

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log_cdf(
    value, name='log_cdf', **kwargs
)

Log cumulative distribution function.

Given random variable X, the cumulative distribution function cdf is:

log_cdf(x) := Log[ P[X <= x] ]

Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1.

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • logcdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_prob

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log_prob(
    value, name='log_prob', **kwargs
)

Log probability density/mass function.

Additional documentation from PixelCNN:

Log probability function with optional conditional input.

Calculates the log probability of a batch of data under the modeled distribution (or conditional distribution, if conditional input is provided).

Args:

  • value: Tensor or Numpy array of image data. May have leading batch dimension(s), which must broadcast to the leading batch dimensions of conditional_input.
  • conditional_input: Tensor on which to condition the distribution (e.g. class labels), or None. May have leading batch dimension(s), which must broadcast to the leading batch dimensions of value.
  • training: bool or None. If bool, it controls the dropout layer, where True implies dropout is active. If None, it defaults to tf.keras.backend.learning_phase().

Returns:

  • log_prob_values: Tensor.

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

log_survival_function

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log_survival_function(
    value, name='log_survival_function', **kwargs
)

Log survival function.

Given random variable X, the survival function is defined:

log_survival_function(x) = Log[ P[X > x] ]
                         = Log[ 1 - P[X <= x] ]
                         = Log[ 1 - cdf(x) ]

Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1.

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

mean

View source

mean(
    name='mean', **kwargs
)

Mean.

mode

View source

mode(
    name='mode', **kwargs
)

Mode.

param_shapes

View source

@classmethod
param_shapes(
    cls, sample_shape, name='DistributionParamShapes'
)

Shapes of parameters given the desired shape of a call to sample().

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample().

Subclasses should override class method _param_shapes.

Args:

  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.

Returns:

dict of parameter name to Tensor shapes.

param_static_shapes

View source

@classmethod
param_static_shapes(
    cls, sample_shape
)

param_shapes with static (i.e. TensorShape) shapes.

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Assumes that the sample's shape is known statically.

Subclasses should override class method _param_shapes to return constant-valued tensors when constant values are fed.

Args:

  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().

Returns:

dict of parameter name to TensorShape.

Raises:

  • ValueError: if sample_shape is a TensorShape and is not fully defined.

prob

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prob(
    value, name='prob', **kwargs
)

Probability density/mass function.

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

quantile

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quantile(
    value, name='quantile', **kwargs
)

Quantile function. Aka 'inverse cdf' or 'percent point function'.

Given random variable X and p in [0, 1], the quantile is:

quantile(p) := x such that P[X <= x] == p

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • quantile: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

sample

View source

sample(
    sample_shape=(), seed=None, name='sample', **kwargs
)

Generate samples of the specified shape.

Note that a call to sample() without arguments will generate a single sample.

Args:

  • sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples.
  • seed: Python integer or tfp.util.SeedStream instance, for seeding PRNG.
  • name: name to give to the op.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • samples: a Tensor with prepended dimensions sample_shape.

stddev

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stddev(
    name='stddev', **kwargs
)

Standard deviation.

Standard deviation is defined as,

stddev = E[(X - E[X])**2]**0.5

where X is the random variable associated with this distribution, E denotes expectation, and stddev.shape = batch_shape + event_shape.

Args:

  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • stddev: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().

survival_function

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survival_function(
    value, name='survival_function', **kwargs
)

Survival function.

Given random variable X, the survival function is defined:

survival_function(x) = P[X > x]
                     = 1 - P[X <= x]
                     = 1 - cdf(x).

Args:

  • value: float or double Tensor.
  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

variance

View source

variance(
    name='variance', **kwargs
)

Variance.

Variance is defined as,

Var = E[(X - E[X])**2]

where X is the random variable associated with this distribution, E denotes expectation, and Var.shape = batch_shape + event_shape.

Args:

  • name: Python str prepended to names of ops created by this function.
  • **kwargs: Named arguments forwarded to subclass implementation.

Returns:

  • variance: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().