void tensorflow::PartialTensorShape::AsProto(TensorShapeProto *proto) const Fill *proto from *this.

tf.contrib.distributions.Beta.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.normal_conjugates_known_sigma_posterior(prior, sigma, s, n) Posterior Normal distribution with conjugate prior on the mean. This model assumes that n observations (with sum s) come from a Normal with unknown mean mu (described by the Normal prior) and known variance sigma^2. The "known sigma posterior" is the distribution of the unknown mu. Accepts a prior Normal distribution object, having parameters mu0 and sigma0, as well as known sigma values of the predictive dist

tf.contrib.distributions.Dirichlet.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.

class tf.TextLineReader A Reader that outputs the lines of a file delimited by newlines. Newlines are stripped from the output. See ReaderBase for supported methods.

tf.contrib.distributions.Gamma.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.Beta.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.training.NextQueuedSequenceBatch.state(state_name) Returns batched state tensors. Args: state_name: string, matches a key provided in initial_states. Returns: A Tensor: a batched set of states, either initial states (if this is the first run of the given example), or a value as stored during a previous iteration via save_state control flow. Its type is the same as initial_states["state_name"].dtype. If we had at input: initial_states[state_name].get_shape() == [d1, d2, ...], the

tf.contrib.graph_editor.Transformer.__init__() Transformer constructor. The following members can be modified: transform_op_handler: handle the transformation of a tf.Operation. This handler defaults to a simple copy. assign_collections_handler: handle the assignment of collections. This handler defaults to assigning new collections created under the given name-scope. transform_external_input_handler: handle the transform of the inputs to the given subgraph. This handler defaults to creating p

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).