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.QuantizedDistribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). Additional documentation from QuantizedDistribution: For whole numbers y, P[Y = y] := P[X <= lower_cutoff], if y == lower_cutoff, := P[X > upper_cutoff - 1], y == upper_cutoff, := 0, if j < lower_cutoff or y > upper_cutoff, := P[y - 1 < X <= y], all other y. The base distribution's log_cdf method mus

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.loss(final_loss, name='Loss')

class tf.SparseTensor Represents a sparse tensor. TensorFlow represents a sparse tensor as three separate dense tensors: indices, values, and shape. In Python, the three tensors are collected into a SparseTensor class for ease of use. If you have separate indices, values, and shape tensors, wrap them in a SparseTensor object before passing to the ops below. Concretely, the sparse tensor SparseTensor(indices, values, shape) comprises the following components, where N and ndims are the number of

tf.contrib.bayesflow.stochastic_graph.surrogate_loss(sample_losses, stochastic_tensors=None, name='SurrogateLoss') Surrogate loss for stochastic graphs. This function will call loss_fn on each StochasticTensor upstream of sample_losses, passing the losses that it influenced. Note that currently surrogate_loss does not work with StochasticTensors instantiated in while_loops or other control structures. Args: sample_losses: a list or tuple of final losses. Each loss should be per example in the

tf.contrib.distributions.Dirichlet.log_pdf(value, name='log_pdf') Log probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: log_prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Beta.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.

class tf.contrib.learn.monitors.PrintTensor Prints given tensors every N steps. This is an EveryN monitor and has consistent semantic for every_n and first_n. The tensors will be printed to the log, with INFO severity.

class tf.contrib.learn.DNNRegressor A regressor for TensorFlow DNN models. Example: education = sparse_column_with_hash_bucket(column_name="education", hash_bucket_size=1000) occupation = sparse_column_with_hash_bucket(column_name="occupation", hash_bucket_size=1000) education_emb = embedding_column(sparse_id_column=education, dimension=16, combiner="sum") occupation_emb = e

tf.contrib.util.ops_used_by_graph_def(graph_def) Collect the list of ops used by a graph. Does not validate that the ops are all registered. Args: graph_def: A GraphDef proto, as from graph.as_graph_def(). Returns: A list of strings, each naming an op used by the graph.