class tf.contrib.distributions.Categorical Categorical distribution. The categorical distribution is parameterized by the log-probabilities of a set of classes.

tf.contrib.distributions.Binomial.__init__(n, logits=None, p=None, validate_args=False, allow_nan_stats=True, name='Binomial') Initialize a batch of Binomial distributions. Args: n: Non-negative floating point tensor with shape broadcastable to [N1,..., Nm] with m >= 0 and the same dtype as p or logits. Defines this as a batch of N1 x ... x Nm different Binomial distributions. Its components should be equal to integer values. logits: Floating point tensor representing the log-odds of a po

tf.contrib.distributions.Binomial.variance(name='variance') Variance.

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

tf.contrib.distributions.Binomial.survival_function(value, name='survival_function') 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: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.distributions.Binomial.std(name='std') Standard deviation.

tf.contrib.distributions.Binomial.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

tf.contrib.distributions.Binomial.sample(sample_shape=(), seed=None, name='sample') 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 seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Binomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Binomial: For each batch member of counts value, P[counts] is the probability that after sampling n draws from this Binomial distribution, the number of successes is k. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient. value must be a non-negative tensor with dtype

tf.contrib.distributions.Binomial.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.