tf.image.draw_bounding_boxes(images, boxes, name=None) Draw bounding boxes on a batch of images. Outputs a copy of images but draws on top of the pixels zero or more bounding boxes specified by the locations in boxes. The coordinates of the each bounding box in boxes are encoded as [y_min, x_min, y_max, x_max]. The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image. For example, if an image is 100 x 200 pixels and the bounding box is [0.1

A file abstraction for randomly reading the contents of a file. Member Details tensorflow::RandomAccessFile::RandomAccessFile() tensorflow::RandomAccessFile::~RandomAccessFile() virtual Status tensorflow::RandomAccessFile::Read(uint64 offset, size_t n, StringPiece *result, char *scratch) const =0 Reads up to n bytes from the file starting at offset. scratch[0..n-1] may be written by this routine. Sets *result to the data that was read (including if fewer than n bytes were successfully read). Ma

tf.contrib.distributions.Categorical.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.

virtual Status tensorflow::Session::Run(const std::vector< std::pair< string, Tensor > > &inputs, const std::vector< string > &output_tensor_names, const std::vector< string > &target_node_names, std::vector< Tensor > *outputs)=0 Runs the graph with the provided input tensors and fills outputs for the endpoints specified in output_tensor_names. Runs to but does not return Tensors for the nodes in target_node_names. The order of tensors in outputs will

tf.contrib.graph_editor.filter_ts(ops, positive_filter) Get all the tensors which are input or output of an op in ops. Args: ops: an object convertible to a list of tf.Operation. positive_filter: a function deciding whether to keep a tensor or not. If True, all the tensors are returned. Returns: A list of tf.Tensor. Raises: TypeError: if ops cannot be converted to a list of tf.Operation.

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.InverseGamma.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. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape sample_shape(x) +

tf.contrib.distributions.DirichletMultinomial.log_survival_function(value, name='log_survival_function') Log survival function. Given random variable X, the survival function is defined: log_survival_function(x) = Log[ P[X > x] ] = Log[ 1 - P[X <= x] ] = Log[ 1 - cdf(x) ] Typically, different numerical approximations can be used for the log survival function, which are more accurate than 1 - cdf(x) when x >> 1. Args: value: float

tf.contrib.distributions.Chi2.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: 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.Categorical.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.