tf.contrib.distributions.Multinomial.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.LoggingTrainable.end(session=None)

tf.contrib.metrics.streaming_sparse_average_precision_at_k(predictions, labels, k, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes average precision@k of predictions with respect to sparse labels. See sparse_average_precision_at_k for details on formula. weights are applied to the result of sparse_average_precision_at_k streaming_sparse_average_precision_at_k creates two local variables, average_precision_at_<k>/count and average_precision_at_<k&g

tf.nn.rnn_cell.EmbeddingWrapper.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 s

tf.contrib.distributions.Dirichlet.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 T

tf.contrib.learn.monitors.EveryN.every_n_step_end(step, outputs) Callback after every n'th step finished. This callback provides access to the tensors/ops evaluated at this step, including the additional tensors for which evaluation was requested in step_begin. In addition, the callback has the opportunity to stop training by returning True. This is useful for early stopping, for example. Args: step: int, the current value of the global step. outputs: dict mapping string values representing

tf.contrib.distributions.Uniform.is_continuous

tf.image.random_flip_left_right(image, seed=None) Randomly flip an image horizontally (left to right). With a 1 in 2 chance, outputs the contents of image flipped along the second dimension, which is width. Otherwise output the image as-is. Args: image: A 3-D tensor of shape [height, width, channels]. seed: A Python integer. Used to create a random seed. See set_random_seed for behavior. Returns: A 3-D tensor of the same type and shape as image. Raises: ValueError: if the shape of image

class tf.contrib.rnn.CoupledInputForgetGateLSTMCell Long short-term memory unit (LSTM) recurrent network cell. The default non-peephole implementation is based on: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997. The peephole implementation is based on: https://research.google.com/pubs/archive/43905.pdf Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurren

tf.contrib.distributions.Dirichlet.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.