tf.assert_greater(x, y, data=None, summarize=None, message=None, name=None) Assert the condition x > y holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_greater(x, y)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_greater(x, y)], x) This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have x[i] > y[i]. If both x and y

class tf.contrib.bayesflow.stochastic_tensor.UniformTensor UniformTensor is a StochasticTensor backed by the distribution Uniform.

tf.contrib.distributions.TransformedDistribution.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.distributions.Uniform.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Normal.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.InverseGamma.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.distributions.Distribution.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.distributions.MultivariateNormalDiagPlusVDVT.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.learn.monitors.SummarySaver.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.image.random_flip_up_down(image, seed=None) Randomly flips an image vertically (upside down). With a 1 in 2 chance, outputs the contents of image flipped along the first dimension, which is height. Otherwise output the image as-is. Args: image: A 3-D tensor of shape [height, width, channels]. seed: A Python integer. Used to create a random seed. See set_random_seed for behavior. Returns: A 3-D tensor of the same type and shape as image. Raises: ValueError: if the shape of image not su