tf.contrib.layers.l1_regularizer(scale, scope=None) Returns a function that can be used to apply L1 regularization to weights. L1 regularization encourages sparsity. Args: scale: A scalar multiplier Tensor. 0.0 disables the regularizer. scope: An optional scope name. Returns: A function with signature l1(weights) that apply L1 regularization. Raises: ValueError: If scale is negative or if scale is not a float.

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

void tensorflow::TensorShape::set_dim(int d, int64 size) Modifies the size of the dimension d to be size REQUIRES: 0 <= d < dims() REQUIRES: size >= 0

class tf.ReaderBase Base class for different Reader types, that produce a record every step. Conceptually, Readers convert string 'work units' into records (key, value pairs). Typically the 'work units' are filenames and the records are extracted from the contents of those files. We want a single record produced per step, but a work unit can correspond to many records. Therefore we introduce some decoupling using a queue. The queue contains the work units and the Reader dequeues from the queue

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.value_type

tf.contrib.learn.monitors.ExportMonitor.epoch_end(epoch) End epoch. Args: epoch: int, the epoch number. Raises: ValueError: if we've not begun an epoch, or epoch number does not match.

tf.contrib.learn.LinearRegressor.linear_bias_ Returns bias of the linear part.

tf.self_adjoint_eigvals(tensor, name=None) Computes the eigenvalues of one or more self-adjoint matrices. Args: tensor: Tensor of shape [..., N, N]. name: string, optional name of the operation. Returns: e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].

tf.contrib.bayesflow.stochastic_tensor.Chi2WithAbsDfTensor.value(name='value')

Status tensorflow::Env::GetFileSize(const string &fname, uint64 *file_size) Stores the size of fname in *file_size.