tf.contrib.distributions.GammaWithSoftplusAlphaBeta.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. 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.

tf.contrib.distributions.Laplace.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='Laplace') Construct Laplace distribution with parameters loc and scale. The parameters loc and scale must be shaped in a way that supports broadcasting (e.g., loc / scale is a valid operation). Args: loc: Floating point tensor which characterizes the location (center) of the distribution. scale: Positive floating point tensor which characterizes the spread of the distribution. validate_arg

tf.contrib.distributions.ExponentialWithSoftplusLam.log_cdf(value, name='log_cdf') 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: The name to give this op. Returns: logcdf: a Tensor of shape samp

tf.contrib.distributions.MultivariateNormalFull.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. 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.

tf.contrib.distributions.Chi2.mean(name='mean') Mean.

tf.contrib.losses.log_loss(predictions, targets, weight=1.0, epsilon=1e-07, scope=None) Adds a Log Loss term to the training procedure. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the weight vector. If the shape of weight matches the shape of predictions, then the loss of each measurab

tf.contrib.distributions.InverseGamma.log_survival_function(value, name='log_survival_function') 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 doubl

class tf.contrib.distributions.WishartFull The matrix Wishart distribution on positive definite matrices. This distribution is defined by a scalar degrees of freedom df and a symmetric, positive definite scale matrix. Evaluation of the pdf, determinant, and sampling are all O(k^3) operations where (k, k) is the event space shape.

tf.matrix_inverse(input, adjoint=None, name=None) Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes). The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices. The output is a tensor of the same shape as the input containing the inverse for all input submatrices [..., :, :]. The op uses LU decomposition with partial pivoting to compute the inverses. If a matrix is not invertible there is no guarantee what

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.log_cdf(value, name='log_cdf') 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: The name to give this op. Returns: logcdf: a Tensor of shape