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Compute a recurrent neural net.
tf.contrib.recurrent.Recurrent( theta, state0, inputs, cell_fn, cell_grad=None, extras=None, max_input_length=None, use_tpu=False, aligned_end=False )
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.
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 this
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.
accumulate_state and the final state.