tf.image.adjust_hue(image, delta, name=None) Adjust hue of an RGB image. This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the hue channel, converts back to RGB and then back to the original data type. If several adjustments are chained it is advisable to minimize the number of redundant conversions. image is an RGB image. The image hue is adjusted by converting the image to HSV and rotating the hue channel (H) by delta. The i

tf.contrib.distributions.NormalWithSoftplusSigma.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.learn.monitors.PrintTensor.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this step,

tf.contrib.distributions.Beta.is_reparameterized

TensorShapeIter tensorflow::TensorShape::end() const

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.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.bayesflow.stochastic_tensor.BinomialTensor.clone(name=None, **dist_args)

tf.placeholder_with_default(input, shape, name=None) A placeholder op that passes though input when its output is not fed. Args: input: A Tensor. The default value to produce when output is not fed. shape: A tf.TensorShape or list of ints. The (possibly partial) shape of the tensor. name: A name for the operation (optional). Returns: A Tensor. Has the same type as input. A placeholder tensor that defaults to input if it is not fed. For feeding SparseTensors which are composite type, there

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.training.stratified_sample(tensors, labels, target_probs, batch_size, init_probs=None, enqueue_many=False, queue_capacity=16, threads_per_queue=1, name=None) Stochastically creates batches based on per-class probabilities. This method discards examples. Internally, it creates one queue to amortize the cost of disk reads, and one queue to hold the properly-proportioned batch. See stratified_sample_unknown_dist for a function that performs stratified sampling with one queue per class