def _run_joint_optimization(self, initial_states, inputs):
'''Finds multiple fixed points via a joint optimization over multiple
state vectors.
Args:
initial_states: Either an [n x n_states] numpy array or an
LSTMStateTuple with initial_states.c and initial_states.h as
[n_inits x n_states] numpy arrays. These data specify the initial
states of the RNN, from which the optimization will search for
fixed points. The choice of type must be consistent with state
type of rnn_cell.
inputs: A [n x n_inputs] numpy array specifying a set of constant
inputs into the RNN.
Returns:
fps: A FixedPoints object containing the optimized fixed points
and associated metadata.
'''
self._print_if_verbose('\tFinding fixed points '
'via joint optimization.')
n, _ = tf_utils.safe_shape(initial_states)
x, F = self._grab_RNN(initial_states, inputs)
# A shape [n,] TF Tensor of objectives (one per initial state) to be
# combined in _run_optimization_loop.
q = 0.5 * tf.reduce_sum(tf.square(F - x), axis=1)
xstar, F_xstar, qstar, dq, n_iters = self._run_optimization_loop(
q, x, F)
fps = FixedPoints(
xstar=xstar,
x_init=tf_utils.maybe_convert_from_LSTMStateTuple(initial_states),
inputs=inputs,
F_xstar=F_xstar,
qstar=qstar,
dq=dq,
n_iters=n_iters,
tol_unique=self.tol_unique,
dtype=self.np_dtype)
return fps
def _run_single_optimization(self, initial_state, inputs):
'''Finds a single fixed point from a single initial state.
Args:
initial_state: A [1 x n_states] numpy array or an
LSTMStateTuple with initial_state.c and initial_state.h as
[1 x n_states/2] numpy arrays. These data specify an initial
state of the RNN, from which the optimization will search for
a single fixed point. The choice of type must be consistent with
state type of rnn_cell.
inputs: A [1 x n_inputs] numpy array specifying the inputs to the
RNN for this fixed point optimization.
Returns:
A FixedPoints object containing the optimized fixed point and
associated metadata.
'''
x, F = self._grab_RNN(initial_state, inputs)
q = 0.5 * tf.reduce_sum(tf.square(F - x))
xstar, F_xstar, qstar, dq, n_iters = \
self._run_optimization_loop(q, x, F)
fp = FixedPoints(
xstar=xstar,
x_init=tf_utils.maybe_convert_from_LSTMStateTuple(initial_state),
inputs=inputs,
F_xstar=F_xstar,
qstar=qstar,
dq=dq,
n_iters=n_iters,
tol_unique=self.tol_unique,
dtype=self.np_dtype)
return fp