class tf.Session A class for running TensorFlow operations. A Session object encapsulates the environment in which Operation objects are executed, and Tensor objects are evaluated. For example: # Build a graph. a = tf.constant(5.0) b = tf.constant(6.0) c = a * b # Launch the graph in a session. sess = tf.Session() # Evaluate the tensor `c`. print(sess.run(c)) A session may own resources, such as variables, queues, and readers. It is important to release these resources when they are no long

tf.unsorted_segment_sum(data, segment_ids, num_segments, name=None) Computes the sum along segments of a tensor. Read the section on Segmentation for an explanation of segments. Computes a tensor such that (output[i] = sum_{j...} data[j...] where the sum is over tuples j... such that segment_ids[j...] == i. Unlike SegmentSum, segment_ids need not be sorted and need not cover all values in the full range of valid values. If the sum is empty for a given segment ID i, output[i] = 0. num_segments

tf.contrib.distributions.BetaWithSoftplusAB.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Additional documentation from Beta: Note that the argument x must be a non-negative floating point tensor whose shape can

tf.nn.rnn_cell.LSTMCell.output_size

tf.contrib.distributions.Poisson.dtype The DType of Tensors handled by this Distribution.

tf.contrib.distributions.Beta.is_reparameterized

tf.contrib.distributions.WishartFull.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.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.distributions.Chi2.get_batch_shape() Shape of a single sample from a single event index as a TensorShape. Same meaning as batch_shape. May be only partially defined. Returns: batch_shape: TensorShape, possibly unknown.

tf.contrib.crf.CrfForwardRnnCell.state_size