tf.contrib.learn.BaseEstimator.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See Trainable. Raises: ValueError: If x or y are not None while input_fn is not None. ValueError: If both steps and max_steps are not None.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalDiagWithSoftplusStDevTensor.__init__(name=None, dist_value_type=None, loss_fn=score_function, **dist_args)

tf.FixedLenSequenceFeature.dtype Alias for field number 1

tf.contrib.bayesflow.stochastic_tensor.CategoricalTensor.mean(name='mean')

tf.contrib.distributions.WishartFull.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

Status tensorflow::Env::DeleteDir(const string &dirname) Deletes the specified directory.

tf.contrib.distributions.Distribution.validate_args Python boolean indicated possibly expensive checks are enabled.

tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor.value_type

tf.image.adjust_brightness(image, delta) Adjust the brightness of RGB or Grayscale images. This is a convenience method that converts an RGB image to float representation, adjusts its brightness, and then converts it back to the original data type. If several adjustments are chained it is advisable to minimize the number of redundant conversions. The value delta is added to all components of the tensor image. Both image and delta are converted to float before adding (and image is scaled approp

class tf.contrib.graph_editor.ControlOutputs The control outputs topology.