tf.contrib.distributions.matrix_diag_transform(matrix, transform=None, name=None) Transform diagonal of [batch-]matrix, leave rest of matrix unchanged. Create a trainable covariance defined by a Cholesky factor: # Transform network layer into 2 x 2 array. matrix_values = tf.contrib.layers.fully_connected(activations, 4) matrix = tf.reshape(matrix_values, (batch_size, 2, 2)) # Make the diagonal positive. If the upper triangle was zero, this would be a # valid Cholesky factor. chol = matrix_di

tf.contrib.distributions.LaplaceWithSoftplusScale.__init__(loc, scale, validate_args=False, allow_nan_stats=True, name='LaplaceWithSoftplusScale')

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

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

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

tf.contrib.distributions.LaplaceWithSoftplusScale.scale Distribution parameter for scale.

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