tfq.optimizers.spsa_minimize

Applies the SPSA algorithm.

tfq.optimizers.spsa_minimize(
    expectation_value_function,
    initial_position,
    tolerance=1e-05,
    max_iterations=200,
    alpha=0.602,
    learning_rate=1.0,
    perturb=1.0,
    gamma=0.101,
    blocking=False,
    allowed_increase=0.5,
    seed=None,
    name=None
)

The SPSA algorithm can be used to minimize a noisy function. See:

SPSA website

Usage:

Here is an example of optimize a function which consists the summation of a few quadratics.

n = 5  # Number of quadratics
coefficient = tf.random.uniform(minval=0, maxval=1, shape=[n])
min_value = 0
func = func = lambda x : tf.math.reduce_sum(np.power(x, 2) *             coefficient)
# Optimize the function with SPSA, start with random parameters
result = tfq.optimizers.spsa_minimize(func, np.random.random(n))
result.converged
tf.Tensor(True, shape=(), dtype=bool)
result.objective_value
tf.Tensor(0.0013349084, shape=(), dtype=float32)

Args
`expectation_value_function`	Python callable that accepts a real valued tf.Tensor with shape [n] where n is the number of function parameters. The return value is a real `tf.Tensor` Scalar (matching shape `[1]`).
`initial_position`	Real `tf.Tensor` of shape `[n]`. The starting point, or points when using batching dimensions, of the search procedure. At these points the function value and the gradient norm should be finite.
`tolerance`	Scalar `tf.Tensor` of real dtype. Specifies the tolerance for the procedure. If the supremum norm between two iteration vector is below this number, the algorithm is stopped.
`learning_rate`	Scalar `tf.Tensor` of real dtype. Specifies the learning rate.
`alpha`	Scalar `tf.Tensor` of real dtype. Specifies scaling of the learning rate.
`perturb`	Scalar `tf.Tensor` of real dtype. Specifies the size of the perturbations.
`gamma`	Scalar `tf.Tensor` of real dtype. Specifies scaling of the size of the perturbations.
`blocking`	Boolean. If true, then the optimizer will only accept updates that improve the objective function.
`allowed_increase`	Scalar `tf.Tensor` of real dtype. Specifies maximum allowable increase in objective function (only applies if blocking is true).
`seed`	(Optional) Python integer. Used to create a random seed for the perturbations.
`name`	(Optional) Python `str`. The name prefixed to the ops created by this function. If not supplied, the default name 'minimize' is used.

Returns
`optimizer_results`	A SPSAOptimizerResults object contains the result of the optimization process.

tfq.optimizers.spsa_minimize

Usage:

Args

Returns