tf.contrib.distributions.InverseGamma.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) +

tf.contrib.distributions.Chi2.pdf(value, name='pdf') Probability density function. 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. Raises: TypeError: if not is_continuous.

tf.contrib.distributions.Categorical.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.Exponential.__init__(lam, validate_args=False, allow_nan_stats=True, name='Exponential') Construct Exponential distribution with parameter lam. Args: lam: Floating point tensor, the rate of the distribution(s). lam must contain only positive values. validate_args: Boolean, default False. Whether to assert that lam > 0, and that x > 0 in the methods prob(x) and log_prob(x). If validate_args is False and the inputs are invalid, correct behavior is not guaranteed.

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.errors.OutOfRangeError Raised when an operation iterates past the valid input range. This exception is raised in "end-of-file" conditions, such as when a queue.dequeue() operation is blocked on an empty queue, and a queue.close() operation executes.

class tf.InteractiveSession A TensorFlow Session for use in interactive contexts, such as a shell. The only difference with a regular Session is that an InteractiveSession installs itself as the default session on construction. The methods Tensor.eval() and Operation.run() will use that session to run ops. This is convenient in interactive shells and IPython notebooks, as it avoids having to pass an explicit Session object to run ops. For example: sess = tf.InteractiveSession() a = tf.constant

tf.pow(x, y, name=None) Computes the power of one value to another. Given a tensor x and a tensor y, this operation computes \(x^y\) for corresponding elements in x and y. For example: # tensor 'x' is [[2, 2], [3, 3]] # tensor 'y' is [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]] Args: x: A Tensor of type float32, float64, int32, int64, complex64, or complex128. y: A Tensor of type float32, float64, int32, int64, complex64, or complex128. name: A name for the operation (opti

tf.contrib.losses.log_loss(predictions, targets, weight=1.0, epsilon=1e-07, scope=None) Adds a Log Loss term to the training procedure. 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 weight vector. If the shape of weight matches the shape of predictions, then the loss of each measurab

tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.clone(name=None, **dist_args)