tf.contrib.distributions.Laplace.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.

tf.contrib.learn.TensorFlowRNNRegressor.get_tensor(name) Returns tensor by name. Args: name: string, name of the tensor. Returns: Tensor.

tf.python_io.TFRecordWriter.write(record) Write a string record to the file. Args: record: str

tf.contrib.distributions.Exponential.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.bayesflow.stochastic_tensor.WishartCholeskyTensor.distribution

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

tf.contrib.learn.monitors.StopAtStep.step_begin(step)

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

tf.contrib.bayesflow.stochastic_tensor.BinomialTensor.loss(final_loss, name='Loss')

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss(final_loss, name='Loss')