tf.python_io.tf_record_iterator()

tf.python_io.tf_record_iterator(path, options=None) An iterator that read the records from a TFRecords file. Args: path: The path to the TFRecords file. options: (optional) A TFRecordOptions object. Yields: Strings. Raises: IOError: If path cannot be opened for reading.

tf.contrib.distributions.Mixture.log_prob()

tf.contrib.distributions.Mixture.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.graph_editor.reroute_b2a_ts()

tf.contrib.graph_editor.reroute_b2a_ts(ts0, ts1, can_modify=None, cannot_modify=None) For each tensor's pair, replace the end of t0 by the end of t1. B0 B1 B0 B1 | | => | A0 A1 A0 A1 The end of the tensors in ts0 are left dangling. Args: ts0: an object convertible to a list of tf.Tensor. ts1: an object convertible to a list of tf.Tensor. can_modify: iterable of operations which can be modified. Any operation outside within_ops will be left untouched by this function. cannot_modify: iter

tf.contrib.distributions.Laplace.cdf()

tf.contrib.distributions.Laplace.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.

tf.contrib.learn.TensorFlowRNNRegressor.get_tensor()

tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name) Returns tensor by name. Args: name: string, name of the tensor. Returns: Tensor.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.batch_shape()

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.learn.monitors.ExportMonitor.signature_fn

tf.contrib.learn.monitors.ExportMonitor.signature_fn

tf.contrib.distributions.Uniform.mean()

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

tf.contrib.bayesflow.stochastic_tensor.MixtureTensor

class tf.contrib.bayesflow.stochastic_tensor.MixtureTensor MixtureTensor is a StochasticTensor backed by the distribution Mixture.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.__init__()

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)