tf.contrib.bayesflow.stochastic_tensor.LaplaceWithSoftplusScaleTensor.name

class tf.errors.FailedPreconditionError Operation was rejected because the system is not in a state to execute it. This exception is most commonly raised when running an operation that reads a tf.Variable before it has been initialized.

tf.contrib.distributions.Exponential.is_reparameterized

void tensorflow::Tensor::UnsafeCopyFromInternal(const Tensor &, const TensorShape &) Copy the other tensor into this tensor and reshape it and reinterpret the buffer's datatype. This tensor shares other's underlying storage.

tf.contrib.distributions.StudentT.df Degrees of freedom in these Student's t distribution(s).

tf.contrib.rnn.GRUBlockCell.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

class tf.contrib.rnn.AttentionCellWrapper Basic attention cell wrapper. Implementation based on https://arxiv.org/pdf/1601.06733.pdf.

tf.contrib.distributions.Binomial.mode(name='mode') Mode. Additional documentation from Binomial: Note that when (n + 1) * p is an integer, there are actually two modes. Namely, (n + 1) * p and (n + 1) * p - 1 are both modes. Here we return only the larger of the two modes.

tf.contrib.rnn.TimeFreqLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "TimeFreqLSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor represe

tf.contrib.rnn.GridLSTMCell.__call__(inputs, state, scope=None) Run one step of LSTM. Args: inputs: input Tensor, 2D, batch x num_units. state: state Tensor, 2D, batch x state_size. scope: VariableScope for the created subgraph; defaults to "LSTMCell". Returns: A tuple containing: - A 2D, batch x output_dim, Tensor representing the output of the LSTM after reading "inputs" when previous state was "state". Here output_dim is num_units. - A 2D, batch x state_size, Tensor representing the ne