tf.assert_positive(x, data=None, summarize=None, message=None, name=None) Assert the condition x > 0 holds element-wise. Example of adding a dependency to an operation: with tf.control_dependencies([tf.assert_positive(x)]): output = tf.reduce_sum(x) Example of adding dependency to the tensor being checked: x = tf.with_dependencies([tf.assert_positive(x)], x) Positive means, for every element x[i] of x, we have x[i] > 0. If x is empty this is trivially satisfied. Args: x: Numeric Ten

tf.contrib.layers.l2_regularizer(scale, scope=None) Returns a function that can be used to apply L2 regularization to weights. Small values of L2 can help prevent overfitting the training data. Args: scale: A scalar multiplier Tensor. 0.0 disables the regularizer. scope: An optional scope name. Returns: A function with signature l2(weights) that applies L2 regularization. Raises: ValueError: If scale is negative or if scale is not a float.

tf.contrib.framework.deprecated(date, instructions) Decorator for marking functions or methods deprecated. This decorator logs a deprecation warning whenever the decorated function is called. It has the following format: (from ) is deprecated and will be removed after . Instructions for updating: will include the class name if it is a method. It also edits the docstring of the function: ' (deprecated)' is appended to the first line of the docstring and a deprecation notice is prepended to t

tf.contrib.ffmpeg.encode_audio(audio, file_format=None, samples_per_second=None) Creates an op that encodes an audio file using sampled audio from a tensor. Args: audio: A rank 2 tensor that has time along dimension 0 and channels along dimension 1. Dimension 0 is samples_per_second * length long in seconds. file_format: The type of file to encode. "wav" is the only supported format. samples_per_second: The number of samples in the audio tensor per second of audio. Returns: A scalar tenso

tf.contrib.losses.sum_of_pairwise_squares(*args, **kwargs) Adds a pairwise-errors-squared loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_pairwise_squared_error. Unlike the sum_of_squares loss, which is a measure of the differences between corresponding elements of predictions and targets, sum_of_pairwise_squares is a measure of the differences between pairs of corresponding elements of predictio

tf.contrib.learn.run_feeds(*args, **kwargs) See run_feeds_iter(). Returns a list instead of an iterator.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.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.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.distributions.Distribution.log_prob(value, name='log_prob') Log probability density/mass function (depending on is_continuous). 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.

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