tf.contrib.distributions.Chi2.df

tf.contrib.distributions.Chi2.cdf(value, name='cdf') 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: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

tf.contrib.distributions.Chi2.beta Inverse scale parameter.

tf.contrib.distributions.Chi2.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.distributions.Chi2.alpha Shape parameter.

tf.contrib.distributions.Chi2.allow_nan_stats Python boolean 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 unde

class tf.contrib.distributions.Chi2 The Chi2 distribution with degrees of freedom df. The PDF of this distribution is: pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(df/2)Gamma(df/2)), x > 0 Note that the Chi2 distribution is a special case of the Gamma distribution, with Chi2(df) = Gamma(df/2, 1/2).

tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, validate_args=False, allow_nan_stats=True, name='Categorical') Initialize Categorical distributions using class log-probabilities. Args: logits: An N-D Tensor, N >= 1, representing the log probabilities of a set of Categorical distributions. The first N - 1 dimensions index into a batch of independent distributions and the last dimension indexes into the classes. dtype: The type of the event samples (default: int32). v

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

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