tf.contrib.bayesflow.stochastic_tensor.TransformedDistributionTensor.graph

tf.contrib.graph_editor.reroute_a2b(sgv0, sgv1) Re-route the inputs and outputs of sgv0 to sgv1 (see _reroute).

tf.nn.rnn_cell.EmbeddingWrapper.state_size

tf.ReaderBase.supports_serialize Whether the Reader implementation can serialize its state.

class tf.contrib.distributions.Binomial Binomial distribution. This distribution is parameterized by a vector p of probabilities and n, the total counts.

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

tf.contrib.learn.monitors.CaptureVariable.step_end(step, output) Overrides BaseMonitor.step_end. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. output: dict mapping string values representing tensor names to the value resulted from running these tensors. Values may be either scalars, for scalar tensors, or Numpy array, for non-scalar tensors. Returns: bool, the result of every_n_step_end, if that was called this s

Eigen::DSizes< Eigen::DenseIndex, NDIMS > tensorflow::TensorShape::AsEigenDSizes() const Fill *dsizes from *this.

class tf.nn.rnn_cell.LSTMStateTuple Tuple used by LSTM Cells for state_size, zero_state, and output state. Stores two elements: (c, h), in that order. Only used when state_is_tuple=True.

class tf.contrib.bayesflow.stochastic_tensor.MultinomialTensor MultinomialTensor is a StochasticTensor backed by the distribution Multinomial.