class tf.contrib.distributions.Gamma The Gamma distribution with parameter alpha and beta. The parameters are the shape and inverse scale parameters alpha, beta. The PDF of this distribution is: pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0 and the CDF of this distribution is: cdf(x) = GammaInc(alpha, beta * x) / Gamma(alpha), x > 0 where GammaInc is the incomplete lower Gamma function. WARNING: This distribution may draw 0-valued samples for small alpha values. See

tf.contrib.distributions.Exponential.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

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.metrics.streaming_root_mean_squared_error(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the root mean squared error between the labels and predictions. The streaming_root_mean_squared_error function creates two local variables, total and count that are used to compute the root mean squared error. This average is weighted by weights, and it is ultimately returned as root_mean_squared_error: an idempotent operation that take

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.distributions.Multinomial.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

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.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