tf.errors.NotFoundError.__init__(node_def, op, message) Creates a NotFoundError.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.clone(name=None, **dist_args)

tf.contrib.distributions.Binomial.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.

tf.contrib.distributions.MultivariateNormalCholesky.is_reparameterized

tf.contrib.distributions.Binomial.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.

size_t tensorflow::Tensor::TotalBytes() const Returns the estimated memory usage of this tensor.

tf.contrib.learn.LinearClassifier.model_dir

class tf.contrib.distributions.Mixture Mixture distribution. The Mixture object implements batched mixture distributions. The mixture model is defined by a Categorical distribution (the mixture) and a python list of Distribution objects. Methods supported include log_prob, prob, mean, sample, and entropy_lower_bound.

tf.contrib.graph_editor.get_generating_ops(ts) Return all the generating ops of the tensors in ts. Args: ts: a list of tf.Tensor Returns: A list of all the generating tf.Operation of the tensors in ts. Raises: TypeError: if ts cannot be converted to a list of tf.Tensor.

tf.reduce_logsumexp(input_tensor, reduction_indices=None, keep_dims=False, name=None) Computes log(sum(exp(elements across dimensions of a tensor))). Reduces input_tensor along the dimensions given in reduction_indices. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in reduction_indices. If keep_dims is true, the reduced dimensions are retained with length 1. If reduction_indices has no entries, all dimensions are reduced, and a tensor with a single element is