tf.contrib.layers.summarize_activation(op) Summarize an activation. This applies the given activation and adds useful summaries specific to the activation. Args: op: The tensor to summarize (assumed to be a layer activation). Returns: The summary op created to summarize op.

tf.contrib.distributions.Categorical.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.contrib.learn.TensorFlowRNNClassifier.predict(x, axis=1, batch_size=None) Predict class or regression for x. For a classification model, the predicted class for each sample in x is returned. For a regression model, the predicted value based on x is returned. Args: x: array-like matrix, [n_samples, n_features...] or iterator. axis: Which axis to argmax for classification. By default axis 1 (next after batch) is used. Use 2 for sequence predictions. batch_size: If test set is too big, use

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

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.beta Inverse scale parameter.

tf.contrib.learn.monitors.SummarySaver.every_n_step_begin(step)

int64 tensorflow::Tensor::NumElements() const Convenience accessor for the tensor shape.

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

tf.scan(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, infer_shape=True, name=None) scan on the list of tensors unpacked from elems on dimension 0. The simplest version of scan repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invoca

tf.contrib.distributions.Mixture.mode(name='mode') Mode.