tf.string_split(source, delimiter=' ') Split elements of source based on delimiter into a SparseTensor. Let N be the size of source (typically N will be the batch size). Split each element of source based on delimiter and return a SparseTensor containing the splitted tokens. Empty tokens are ignored. If delimiter is an empty string, each element of the source is split into individual 1 character strings. For example: N = 2, source[0] is 'hello world' and source[1] is 'a b c', then the output w

tf.contrib.distributions.Multinomial.prob(value, name='prob') Probability density/mass function (depending on is_continuous). Additional documentation from Multinomial: For each batch of counts [n_1,...,n_k], P[counts] is the probability that after sampling n draws from this Multinomial distribution, the number of draws falling in class j is n_j. Note that different sequences of draws can result in the same counts, thus the probability includes a combinatorial coefficient. Note that input "cou

tf.TextLineReader.reader_ref Op that implements the reader.

tf.contrib.distributions.NormalWithSoftplusSigma.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: flo

tf.contrib.learn.monitors.ExportMonitor.every_n_step_end(step, outputs)

tf.contrib.distributions.Categorical.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) + s

tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. mean: A 0-D Tensor or Python value of type dtype. The mean

tf.contrib.framework.get_global_step(graph=None) Get the global step tensor. The global step tensor must be an integer variable. We first try to find it in the collection GLOBAL_STEP, or by name global_step:0. Args: graph: The graph to find the global step in. If missing, use default graph. Returns: The global step variable, or None if none was found. Raises: TypeError: If the global step tensor has a non-integer type, or if it is not a Variable.

tf.contrib.metrics.streaming_recall(*args, **kwargs) Computes the recall of the predictions with respect to the labels. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-10-19. Instructions for updating: ignore_mask is being deprecated. Instead use weights with values 0.0 and 1.0 to mask values. For example, weights=tf.logical_not(mask). The streaming_recall function creates two local variables, true_positives and false_negatives, that are used to compute th

tf.image.resize_image_with_crop_or_pad(image, target_height, target_width) Crops and/or pads an image to a target width and height. Resizes an image to a target width and height by either centrally cropping the image or padding it evenly with zeros. If width or height is greater than the specified target_width or target_height respectively, this op centrally crops along that dimension. If width or height is smaller than the specified target_width or target_height respectively, this op centrall