tf.contrib.learn.LinearClassifier.fit(x=None, y=None, input_fn=None, steps=None, batch_size=None, monitors=None, max_steps=None) See trainable.Trainable.

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalFullTensor.entropy(name='entropy')

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.

tf.contrib.learn.monitors.PrintTensor.every_n_step_begin(step)

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

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

tf.contrib.distributions.NormalWithSoftplusSigma.mu Distribution parameter for the mean.

class tf.errors.CancelledError Raised when an operation or step is cancelled. For example, a long-running operation (e.g. queue.enqueue() may be cancelled by running another operation (e.g. queue.close(cancel_pending_enqueues=True), or by closing the session. A step that is running such a long-running operation will fail by raising CancelledError.

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.std(name='std') Standard deviation.

tf.contrib.distributions.Normal.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.