tf.ceil(x, name=None) Returns element-wise smallest integer in not less than x. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.bayesflow.stochastic_tensor.BernoulliTensor.graph

tf.contrib.distributions.Beta.sample_n(n, seed=None, name='sample_n') Generate n samples. Args: n: Scalar Tensor of type int32 or int64, the number of observations to sample. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with a prepended dimension (n,). Raises: TypeError: if n is not an integer type.

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.learn.monitors.PrintTensor.epoch_begin(epoch) Begin epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've already begun an epoch, or epoch < 0.

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.learn.TensorFlowRNNClassifier.model_dir

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

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