tf.contrib.learn.DNNRegressor.config

tf.OpError.node_def The NodeDef proto representing the op that failed.

class tf.contrib.distributions.NormalWithSoftplusSigma Normal with softplus applied to sigma.

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

tf.rsqrt(x, name=None) Computes reciprocal of square root of x element-wise. I.e., \(y = 1 / \sqrt{x}\). Args: x: A Tensor. Must be one of the following types: half, float32, float64, complex64, complex128. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.contrib.distributions.Beta.entropy(name='entropy') Shanon entropy in nats.

tf.contrib.framework.assert_global_step(global_step_tensor) Asserts global_step_tensor is a scalar int Variable or Tensor. Args: global_step_tensor: Tensor to test.

Represents an n-dimensional array of values. Member Details tensorflow::Tensor::Tensor() Creates a 1-dimensional, 0-element float tensor. The returned Tensor is not a scalar (shape {}), but is instead an empty one-dimensional Tensor (shape {0}, NumElements() == 0). Since it has no elements, it does not need to be assigned a value and is initialized by default ( IsInitialized() is true). If this is undesirable, consider creating a one-element scalar which does require initialization: tensorflow:

tf.contrib.learn.BaseEstimator.config

tf.contrib.graph_editor.SubGraphView.outputs The output tensors of this subgraph view.