tf.complex(real, imag, name=None) Converts two real numbers to a complex number. Given a tensor real representing the real part of a complex number, and a tensor imag representing the imaginary part of a complex number, this operation returns complex numbers elementwise of the form (a + bj), where a represents the real part and b represents the imag part. The input tensors real and imag must have the same shape. For example: # tensor 'real' is [2.25, 3.25] # tensor `imag` is [4.75, 5.75] tf.co

class tf.contrib.learn.BaseEstimator Abstract BaseEstimator class to train and evaluate TensorFlow models. Concrete implementation of this class should provide the following functions: _get_train_ops _get_eval_ops _get_predict_ops Estimator implemented below is a good example of how to use this class.

class tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor Base Class for Tensor-like objects that emit stochastic values.

tf.contrib.distributions.Laplace.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.parameters Dictionary of parameters used by this Distribution.

tf.contrib.distributions.Chi2.mean(name='mean') Mean.

tf.contrib.distributions.BetaWithSoftplusAB.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.Distribution.variance(name='variance') Variance.

tf.contrib.learn.monitors.StopAtStep.begin(max_steps=None) Called at the beginning of training. When called, the default graph is the one we are executing. Args: max_steps: int, the maximum global step this training will run until. Raises: ValueError: if we've already begun a run.

tf.contrib.metrics.streaming_covariance(predictions, labels, weights=None, metrics_collections=None, updates_collections=None, name=None) Computes the unbiased sample covariance between predictions and labels. The streaming_covariance function creates four local variables, comoment, mean_prediction, mean_label, and count, which are used to compute the sample covariance between predictions and labels across multiple batches of data. The covariance is ultimately returned as an idempotent operati