tf.contrib.bayesflow.stochastic_tensor.BetaWithSoftplusABTensor.name

Static helper routines for PartialTensorShape. Includes a few common predicates on a partially known tensor shape. Member Details string tensorflow::PartialTensorShapeUtils::PartialShapeListString(const gtl::ArraySlice< PartialTensorShape > &shapes) bool tensorflow::PartialTensorShapeUtils::AreCompatible(const gtl::ArraySlice< PartialTensorShape > &shapes0, const gtl::ArraySlice< PartialTensorShape > &shapes1)

class tf.contrib.bayesflow.stochastic_tensor.GammaWithSoftplusAlphaBetaTensor GammaWithSoftplusAlphaBetaTensor is a StochasticTensor backed by the distribution GammaWithSoftplusAlphaBeta.

tf.contrib.graph_editor.select_ts(*args, **kwargs) Helper to select tensors. Args: *args: list of 1) regular expressions (compiled or not) or 2) (array of) tf.Tensor. tf.Operation instances are silently ignored. **kwargs: 'graph': tf.Graph in which to perform the regex query.This is required when using regex. 'positive_filter': an elem if selected only if positive_filter(elem) is True. This is optional. 'restrict_ts_regex': a regular expression is ignored if it doesn't start with the substri

tf.contrib.distributions.Gamma.entropy(name='entropy') Shanon entropy in nats. Additional documentation from Gamma: This is defined to be entropy = alpha - log(beta) + log(Gamma(alpha)) + (1-alpha)digamma(alpha) where digamma(alpha) is the digamma function.

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

tf.nn.rnn_cell.MultiRNNCell.__call__(inputs, state, scope=None) Run this multi-layer cell on inputs, starting from state.

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

void tensorflow::EnvWrapper::SleepForMicroseconds(int64 micros) override Sleeps/delays the thread for the prescribed number of micro-seconds.

tf.contrib.learn.run_n(output_dict, feed_dict=None, restore_checkpoint_path=None, n=1) Run output_dict tensors n times, with the same feed_dict each run. Args: output_dict: A dict mapping string names to tensors to run. Must all be from the same graph. feed_dict: dict of input values to feed each run. restore_checkpoint_path: A string containing the path to a checkpoint to restore. n: Number of times to repeat. Returns: A list of n dict objects, each containing values read from output_di