tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.BetaTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.InverseGammaTensor.value_type

tf.contrib.distributions.Beta.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.entropy(name='entropy')

tf.contrib.distributions.Chi2.event_shape(name='event_shape') 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.

tf.contrib.learn.monitors.ExportMonitor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

tf.image.central_crop(image, central_fraction) Crop the central region of the image. Remove the outer parts of an image but retain the central region of the image along each dimension. If we specify central_fraction = 0.5, this function returns the region marked with "X" in the below diagram. -------- | | | XXXX | | XXXX | | | where "X" is the central 50% of the image. -------- Args: image: 3-D float Tensor of shape [height, width, depth] central_fraction: float (0, 1]

tf.contrib.distributions.QuantizedDistribution.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static 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.

tf.contrib.bayesflow.stochastic_tensor.DirichletTensor.name