tf.contrib.distributions.Distribution.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.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.bayesflow.stochastic_tensor.NormalWithSoftplusSigmaTensor.mean(name='mean')

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.input_dict

tf.contrib.distributions.BernoulliWithSigmoidP.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.QueueBase.dequeue_many(n, name=None) Dequeues and concatenates n elements from this queue. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. If the queue is closed and there are less than n elements left, then an OutOfRange exception is raised. At runtime, this operation may raise an error if the queue is closed before or during its executio

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters Dictionary of parameters used by this Distribution.

tf.contrib.layers.sum_regularizer(regularizer_list, scope=None) Returns a function that applies the sum of multiple regularizers. Args: regularizer_list: A list of regularizers to apply. scope: An optional scope name Returns: A function with signature sum_reg(weights) that applies the sum of all the input regularizers.

tf.contrib.distributions.Chi2WithAbsDf.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.layers.summarize_collection(collection, name_filter=None, summarizer=summarize_tensor) Summarize a graph collection of tensors, possibly filtered by name. The layers module defines convenience functions summarize_variables, summarize_weights and summarize_biases, which set the collection argument of summarize_collection to VARIABLES, WEIGHTS and BIASES, respectively.