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

tf.svd(tensor, compute_uv=True, full_matrices=False, name=None) Computes the singular value decompositions of one or more matrices. Computes the SVD of each inner matrix in tensor such that tensor[..., :, :] = u[..., :, :] * diag(s[..., :, :]) * transpose(v[..., :, :]) # a is a tensor. # s is a tensor of singular values. # u is a tensor of left singular vectors. # v is a tensor of right singular vectors. s, u, v = svd(a) s = svd(a, compute_uv=False) Args: matrix: Tensor of shape [..., M, N].

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

tf.contrib.distributions.LaplaceWithSoftplusScale.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

class tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor LaplaceWithSoftplusScaleTensor is a StochasticTensor backed by the distribution LaplaceWithSoftplusScale.

tf.contrib.distributions.Bernoulli.mean(name='mean') Mean.

tf.contrib.bayesflow.stochastic_tensor.ExponentialWithSoftplusLamTensor.distribution

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.name

tf.contrib.learn.LinearClassifier.__init__(feature_columns, model_dir=None, n_classes=2, weight_column_name=None, optimizer=None, gradient_clip_norm=None, enable_centered_bias=None, _joint_weight=False, config=None) Construct a LinearClassifier estimator object. Args: feature_columns: An iterable containing all the feature columns used by the model. All items in the set should be instances of classes derived from FeatureColumn. model_dir: Directory to save model parameters, graph and etc. Th

tf.contrib.learn.monitors.ExportMonitor.last_export_dir Returns the directory containing the last completed export. Returns: The string path to the exported directory. NB: this functionality was added on 2016/09/25; clients that depend on the return value may need to handle the case where this function returns None because the estimator being fitted does not yet return a value during export.