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

tf.contrib.bayesflow.stochastic_tensor.Chi2Tensor.input_dict

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.Beta.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 or double Tensor

tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.dtype

tf.nn.rnn_cell.GRUCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [ba

tf.FixedLengthRecordReader.__init__(record_bytes, header_bytes=None, footer_bytes=None, name=None) Create a FixedLengthRecordReader. Args: record_bytes: An int. header_bytes: An optional int. Defaults to 0. footer_bytes: An optional int. Defaults to 0. name: A name for the operation (optional).

tf.contrib.distributions.Beta.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is unde