tf.contrib.recurrent.Recurrent(
theta,
state0,
inputs,
cell_fn,
cell_grad=None,
extras=None,
max_input_length=None,
use_tpu=False,
aligned_end=False
)
Defined in tensorflow/contrib/recurrent/python/ops/recurrent.py.
Compute a recurrent neural net.
Roughly, Recurrent() computes the following: state = state0 for t in inputs' sequence length: state = cell_fn(theta, state, inputs[t, :]) accumulate_state[t, :] = state return accumulate_state, state
theta, state, inputs are all structures of tensors.
inputs[t, :] means taking a slice out from every tensor in the inputs.
accumulate_state[t, :] = state means that we stash every tensor in 'state' into a slice of the corresponding tensor in accumulate_state.
cell_fn is a python callable computing (building up a TensorFlow graph) the recurrent neural network's one forward step. Two calls of cell_fn must describe two identical computations.
By construction, Recurrent()'s backward computation does not access any intermediate values computed by cell_fn during forward computation. We may extend Recurrent() to support that by taking a customized backward function of cell_fn.
Args:
theta: weights. A structure of tensors.state0: initial state. A structure of tensors.inputs: inputs. A structure of tensors.cell_fn: A python function, which computes: state1, extras = cell_fn(theta, state0, inputs[t, :])cell_grad: A python function which computes: dtheta, dstate0, dinputs[t, :] = cell_grad( theta, state0, inputs[t, :], extras, dstate1)extras: A structure of tensors. The 2nd return value of every invocation of cell_fn is a structure of tensors with matching keys and shapes of thisextras.max_input_length: maximum length of effective input. This is used to truncate the computation if the inputs have been allocated to a larger size. A scalar tensor.use_tpu: whether or not we are on TPU.aligned_end: A boolean indicating whether the sequence is aligned at the end.
Returns:
accumulate_state and the final state.