tf.contrib.distributions.Gamma.prob(value, name='prob') Probability density/mass function (depending on is_continuous). 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.

tf.contrib.distributions.RegisterKL.__init__(dist_cls_a, dist_cls_b) Initialize the KL registrar. Args: dist_cls_a: the class of the first argument of the KL divergence. dist_cls_b: the class of the second argument of the KL divergence.

tf.image.rgb_to_hsv(images, name=None) Converts one or more images from RGB to HSV. Outputs a tensor of the same shape as the images tensor, containing the HSV value of the pixels. The output is only well defined if the value in images are in [0,1]. output[..., 0] contains hue, output[..., 1] contains saturation, and output[..., 2] contains value. All HSV values are in [0,1]. A hue of 0 corresponds to pure red, hue 1/3 is pure green, and 2/3 is pure blue. Args: images: A Tensor. Must be one o

tf.contrib.distributions.Uniform.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.metrics.streaming_mean(values, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the (weighted) mean of the given values. The streaming_mean function creates two local variables, total and count that are used to compute the average of values. This average is ultimately returned as mean which is an idempotent operation that simply divides total by count. For estimation of the metric over a stream of data, the function creates an update_op operation

tf.contrib.bayesflow.stochastic_tensor.BernoulliWithSigmoidPTensor.loss(final_loss, name='Loss')

tf.contrib.losses.mean_squared_error(*args, **kwargs) Adds a Sum-of-Squares loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_squared_error. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the total loss for each sample of the batch is rescaled by the corresponding element in the

tf.contrib.distributions.QuantizedDistribution.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. Additional documentation from QuantizedDistribution: For whole numbers y, cdf(y) := P[Y <= y] = 1, if y >

tf.contrib.learn.read_batch_record_features(file_pattern, batch_size, features, randomize_input=True, num_epochs=None, queue_capacity=10000, reader_num_threads=1, parser_num_threads=1, name='dequeue_record_examples') Reads TFRecord, queues, batches and parses Example proto. See more detailed description in read_examples. Args: file_pattern: List of files or pattern of file paths containing Example records. See tf.gfile.Glob for pattern rules. batch_size: An int or scalar Tensor specifying th

tf.contrib.distributions.Bernoulli.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.