tensorflow::TensorShape::IsValidShape()

Status tensorflow::TensorShape::IsValidShape(const TensorShapeProto &proto) Returns OK iff proto is a valid tensor shape, and a descriptive error status otherwise.

tf.contrib.distributions.Chi2.is_continuous

tf.contrib.distributions.Chi2.is_continuous

tf.contrib.rnn.GridLSTMCell.zero_state()

tf.contrib.rnn.GridLSTMCell.zero_state(batch_size, dtype) Return zero-filled state tensor(s). Args: batch_size: int, float, or unit Tensor representing the batch size. dtype: the data type to use for the state. Returns: If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shape

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std()

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.std(name='std') Standard deviation.

tf.contrib.bayesflow.stochastic_tensor.StochasticTensor.clone()

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

tf.contrib.distributions.NormalWithSoftplusSigma.sigma

tf.contrib.distributions.NormalWithSoftplusSigma.sigma Distribution parameter for standard deviation.

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.loss()

tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor.loss(final_loss, name='Loss')

tf.contrib.graph_editor.ControlOutputs.get_all()

tf.contrib.graph_editor.ControlOutputs.get_all()

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value_type

tf.contrib.bayesflow.stochastic_tensor.NormalTensor.value_type

tensorflow::Env::NowMicros()

virtual uint64 tensorflow::Env::NowMicros()=0 Returns the number of micro-seconds since some fixed point in time. Only useful for computing deltas of time.