python3-keras 2.1.1-1 / usr / lib / python3 / dist-packages / keras / optimizers.py

from __future__ import absolute_import
import six
import copy
from six.moves import zip

from . import backend as K
from .utils.generic_utils import serialize_keras_object
from .utils.generic_utils import deserialize_keras_object
from .legacy import interfaces

if K.backend() == 'tensorflow':
    import tensorflow as tf


def clip_norm(g, c, n):
    if c <= 0:  # if clipnorm == 0 no need to add ops to the graph
        return g

    # tf require using a special op to multiply IndexedSliced by scalar
    if K.backend() == 'tensorflow':
        condition = n >= c
        then_expression = tf.scalar_mul(c / n, g)
        else_expression = g

        # saving the shape to avoid converting sparse tensor to dense
        if isinstance(then_expression, tf.Tensor):
            g_shape = copy.copy(then_expression.get_shape())
        elif isinstance(then_expression, tf.IndexedSlices):
            g_shape = copy.copy(then_expression.dense_shape)
        if condition.dtype != tf.bool:
            condition = tf.cast(condition, 'bool')
        g = tf.cond(condition,
                    lambda: then_expression,
                    lambda: else_expression)
        if isinstance(then_expression, tf.Tensor):
            g.set_shape(g_shape)
        elif isinstance(then_expression, tf.IndexedSlices):
            g._dense_shape = g_shape
    else:
        g = K.switch(K.greater_equal(n, c), g * c / n, g)
    return g


class Optimizer(object):
    """Abstract optimizer base class.

    Note: this is the parent class of all optimizers, not an actual optimizer
    that can be used for training models.

    All Keras optimizers support the following keyword arguments:

        clipnorm: float >= 0. Gradients will be clipped
            when their L2 norm exceeds this value.
        clipvalue: float >= 0. Gradients will be clipped
            when their absolute value exceeds this value.
    """

    def __init__(self, **kwargs):
        allowed_kwargs = {'clipnorm', 'clipvalue'}
        for k in kwargs:
            if k not in allowed_kwargs:
                raise TypeError('Unexpected keyword argument '
                                'passed to optimizer: ' + str(k))
        self.__dict__.update(kwargs)
        self.updates = []
        self.weights = []

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        raise NotImplementedError

    def get_gradients(self, loss, params):
        grads = K.gradients(loss, params)
        if hasattr(self, 'clipnorm') and self.clipnorm > 0:
            norm = K.sqrt(sum([K.sum(K.square(g)) for g in grads]))
            grads = [clip_norm(g, self.clipnorm, norm) for g in grads]
        if hasattr(self, 'clipvalue') and self.clipvalue > 0:
            grads = [K.clip(g, -self.clipvalue, self.clipvalue) for g in grads]
        return grads

    def set_weights(self, weights):
        """Sets the weights of the optimizer, from Numpy arrays.

        Should only be called after computing the gradients
        (otherwise the optimizer has no weights).

        # Arguments
            weights: a list of Numpy arrays. The number
                of arrays and their shape must match
                number of the dimensions of the weights
                of the optimizer (i.e. it should match the
                output of `get_weights`).

        # Raises
            ValueError: in case of incompatible weight shapes.
        """
        params = self.weights
        weight_value_tuples = []
        param_values = K.batch_get_value(params)
        for pv, p, w in zip(param_values, params, weights):
            if pv.shape != w.shape:
                raise ValueError('Optimizer weight shape ' +
                                 str(pv.shape) +
                                 ' not compatible with '
                                 'provided weight shape ' + str(w.shape))
            weight_value_tuples.append((p, w))
        K.batch_set_value(weight_value_tuples)

    def get_weights(self):
        """Returns the current value of the weights of the optimizer.

        # Returns
            A list of numpy arrays.
        """
        return K.batch_get_value(self.weights)

    def get_config(self):
        config = {}
        if hasattr(self, 'clipnorm'):
            config['clipnorm'] = self.clipnorm
        if hasattr(self, 'clipvalue'):
            config['clipvalue'] = self.clipvalue
        return config

    @classmethod
    def from_config(cls, config):
        return cls(**config)


class SGD(Optimizer):
    """Stochastic gradient descent optimizer.

    Includes support for momentum,
    learning rate decay, and Nesterov momentum.

    # Arguments
        lr: float >= 0. Learning rate.
        momentum: float >= 0. Parameter updates momentum.
        decay: float >= 0. Learning rate decay over each update.
        nesterov: boolean. Whether to apply Nesterov momentum.
    """

    def __init__(self, lr=0.01, momentum=0., decay=0.,
                 nesterov=False, **kwargs):
        super(SGD, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.momentum = K.variable(momentum, name='momentum')
            self.decay = K.variable(decay, name='decay')
        self.initial_decay = decay
        self.nesterov = nesterov

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))
        # momentum
        shapes = [K.int_shape(p) for p in params]
        moments = [K.zeros(shape) for shape in shapes]
        self.weights = [self.iterations] + moments
        for p, g, m in zip(params, grads, moments):
            v = self.momentum * m - lr * g  # velocity
            self.updates.append(K.update(m, v))

            if self.nesterov:
                new_p = p + self.momentum * v - lr * g
            else:
                new_p = p + v

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'momentum': float(K.get_value(self.momentum)),
                  'decay': float(K.get_value(self.decay)),
                  'nesterov': self.nesterov}
        base_config = super(SGD, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class RMSprop(Optimizer):
    """RMSProp optimizer.

    It is recommended to leave the parameters of this optimizer
    at their default values
    (except the learning rate, which can be freely tuned).

    This optimizer is usually a good choice for recurrent
    neural networks.

    # Arguments
        lr: float >= 0. Learning rate.
        rho: float >= 0.
        epsilon: float >= 0. Fuzz factor.
        decay: float >= 0. Learning rate decay over each update.

    # References
        - [rmsprop: Divide the gradient by a running average of its recent magnitude](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
    """

    def __init__(self, lr=0.001, rho=0.9, epsilon=1e-8, decay=0.,
                 **kwargs):
        super(RMSprop, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.lr = K.variable(lr, name='lr')
            self.rho = K.variable(rho, name='rho')
            self.decay = K.variable(decay, name='decay')
            self.iterations = K.variable(0, dtype='int64', name='iterations')
        self.epsilon = epsilon
        self.initial_decay = decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        accumulators = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        self.weights = accumulators
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))

        for p, g, a in zip(params, grads, accumulators):
            # update accumulator
            new_a = self.rho * a + (1. - self.rho) * K.square(g)
            self.updates.append(K.update(a, new_a))
            new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'rho': float(K.get_value(self.rho)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon}
        base_config = super(RMSprop, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Adagrad(Optimizer):
    """Adagrad optimizer.

    It is recommended to leave the parameters of this optimizer
    at their default values.

    # Arguments
        lr: float >= 0. Learning rate.
        epsilon: float >= 0.
        decay: float >= 0. Learning rate decay over each update.

    # References
        - [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
    """

    def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs):
        super(Adagrad, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.lr = K.variable(lr, name='lr')
            self.decay = K.variable(decay, name='decay')
            self.iterations = K.variable(0, dtype='int64', name='iterations')
        self.epsilon = epsilon
        self.initial_decay = decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        shapes = [K.int_shape(p) for p in params]
        accumulators = [K.zeros(shape) for shape in shapes]
        self.weights = accumulators
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))

        for p, g, a in zip(params, grads, accumulators):
            new_a = a + K.square(g)  # update accumulator
            self.updates.append(K.update(a, new_a))
            new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon}
        base_config = super(Adagrad, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Adadelta(Optimizer):
    """Adadelta optimizer.

    It is recommended to leave the parameters of this optimizer
    at their default values.

    # Arguments
        lr: float >= 0. Learning rate.
            It is recommended to leave it at the default value.
        rho: float >= 0.
        epsilon: float >= 0. Fuzz factor.
        decay: float >= 0. Learning rate decay over each update.

    # References
        - [Adadelta - an adaptive learning rate method](http://arxiv.org/abs/1212.5701)
    """

    def __init__(self, lr=1.0, rho=0.95, epsilon=1e-8, decay=0.,
                 **kwargs):
        super(Adadelta, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.lr = K.variable(lr, name='lr')
            self.decay = K.variable(decay, name='decay')
            self.iterations = K.variable(0, dtype='int64', name='iterations')
        self.rho = rho
        self.epsilon = epsilon
        self.initial_decay = decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        shapes = [K.int_shape(p) for p in params]
        accumulators = [K.zeros(shape) for shape in shapes]
        delta_accumulators = [K.zeros(shape) for shape in shapes]
        self.weights = accumulators + delta_accumulators
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))

        for p, g, a, d_a in zip(params, grads, accumulators, delta_accumulators):
            # update accumulator
            new_a = self.rho * a + (1. - self.rho) * K.square(g)
            self.updates.append(K.update(a, new_a))

            # use the new accumulator and the *old* delta_accumulator
            update = g * K.sqrt(d_a + self.epsilon) / K.sqrt(new_a + self.epsilon)
            new_p = p - lr * update

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))

            # update delta_accumulator
            new_d_a = self.rho * d_a + (1 - self.rho) * K.square(update)
            self.updates.append(K.update(d_a, new_d_a))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'rho': self.rho,
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon}
        base_config = super(Adadelta, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Adam(Optimizer):
    """Adam optimizer.

    Default parameters follow those provided in the original paper.

    # Arguments
        lr: float >= 0. Learning rate.
        beta_1: float, 0 < beta < 1. Generally close to 1.
        beta_2: float, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor.
        decay: float >= 0. Learning rate decay over each update.

    # References
        - [Adam - A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980v8)
    """

    def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-8, decay=0., **kwargs):
        super(Adam, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.beta_1 = K.variable(beta_1, name='beta_1')
            self.beta_2 = K.variable(beta_2, name='beta_2')
            self.decay = K.variable(decay, name='decay')
        self.epsilon = epsilon
        self.initial_decay = decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))

        t = K.cast(self.iterations, K.floatx()) + 1
        lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
                     (1. - K.pow(self.beta_1, t)))

        ms = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        vs = [K.zeros(K.int_shape(p), dtype=K.dtype(p)) for p in params]
        self.weights = [self.iterations] + ms + vs

        for p, g, m, v in zip(params, grads, ms, vs):
            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g)
            p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_t))
            new_p = p_t

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'beta_1': float(K.get_value(self.beta_1)),
                  'beta_2': float(K.get_value(self.beta_2)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon}
        base_config = super(Adam, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Adamax(Optimizer):
    """Adamax optimizer from Adam paper's Section 7.

    It is a variant of Adam based on the infinity norm.
    Default parameters follow those provided in the paper.

    # Arguments
        lr: float >= 0. Learning rate.
        beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor.
        decay: float >= 0. Learning rate decay over each update.

    # References
        - [Adam - A Method for Stochastic Optimization](http://arxiv.org/abs/1412.6980v8)
    """

    def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-8, decay=0., **kwargs):
        super(Adamax, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.lr = K.variable(lr, name='lr')
            self.beta_1 = K.variable(beta_1, name='beta_1')
            self.beta_2 = K.variable(beta_2, name='beta_2')
            self.decay = K.variable(decay, name='decay')
        self.epsilon = epsilon
        self.initial_decay = decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        lr = self.lr
        if self.initial_decay > 0:
            lr *= (1. / (1. + self.decay * K.cast(self.iterations,
                                                  K.dtype(self.decay))))

        t = K.cast(self.iterations, K.floatx()) + 1
        lr_t = lr / (1. - K.pow(self.beta_1, t))

        shapes = [K.int_shape(p) for p in params]
        # zero init of 1st moment
        ms = [K.zeros(shape) for shape in shapes]
        # zero init of exponentially weighted infinity norm
        us = [K.zeros(shape) for shape in shapes]
        self.weights = [self.iterations] + ms + us

        for p, g, m, u in zip(params, grads, ms, us):

            m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
            u_t = K.maximum(self.beta_2 * u, K.abs(g))
            p_t = p - lr_t * m_t / (u_t + self.epsilon)

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(u, u_t))
            new_p = p_t

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'beta_1': float(K.get_value(self.beta_1)),
                  'beta_2': float(K.get_value(self.beta_2)),
                  'decay': float(K.get_value(self.decay)),
                  'epsilon': self.epsilon}
        base_config = super(Adamax, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class Nadam(Optimizer):
    """Nesterov Adam optimizer.

    Much like Adam is essentially RMSprop with momentum,
    Nadam is Adam RMSprop with Nesterov momentum.

    Default parameters follow those provided in the paper.
    It is recommended to leave the parameters of this optimizer
    at their default values.

    # Arguments
        lr: float >= 0. Learning rate.
        beta_1/beta_2: floats, 0 < beta < 1. Generally close to 1.
        epsilon: float >= 0. Fuzz factor.

    # References
        - [Nadam report](http://cs229.stanford.edu/proj2015/054_report.pdf)
        - [On the importance of initialization and momentum in deep learning](http://www.cs.toronto.edu/~fritz/absps/momentum.pdf)
    """

    def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999,
                 epsilon=1e-8, schedule_decay=0.004, **kwargs):
        super(Nadam, self).__init__(**kwargs)
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')
            self.m_schedule = K.variable(1., name='m_schedule')
            self.lr = K.variable(lr, name='lr')
            self.beta_1 = K.variable(beta_1, name='beta_1')
            self.beta_2 = K.variable(beta_2, name='beta_2')
        self.epsilon = epsilon
        self.schedule_decay = schedule_decay

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.get_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]

        t = K.cast(self.iterations, K.floatx()) + 1

        # Due to the recommendations in [2], i.e. warming momentum schedule
        momentum_cache_t = self.beta_1 * (1. - 0.5 * (K.pow(K.cast_to_floatx(0.96), t * self.schedule_decay)))
        momentum_cache_t_1 = self.beta_1 * (1. - 0.5 * (K.pow(K.cast_to_floatx(0.96), (t + 1) * self.schedule_decay)))
        m_schedule_new = self.m_schedule * momentum_cache_t
        m_schedule_next = self.m_schedule * momentum_cache_t * momentum_cache_t_1
        self.updates.append((self.m_schedule, m_schedule_new))

        shapes = [K.int_shape(p) for p in params]
        ms = [K.zeros(shape) for shape in shapes]
        vs = [K.zeros(shape) for shape in shapes]

        self.weights = [self.iterations] + ms + vs

        for p, g, m, v in zip(params, grads, ms, vs):
            # the following equations given in [1]
            g_prime = g / (1. - m_schedule_new)
            m_t = self.beta_1 * m + (1. - self.beta_1) * g
            m_t_prime = m_t / (1. - m_schedule_next)
            v_t = self.beta_2 * v + (1. - self.beta_2) * K.square(g)
            v_t_prime = v_t / (1. - K.pow(self.beta_2, t))
            m_t_bar = (1. - momentum_cache_t) * g_prime + momentum_cache_t_1 * m_t_prime

            self.updates.append(K.update(m, m_t))
            self.updates.append(K.update(v, v_t))

            p_t = p - self.lr * m_t_bar / (K.sqrt(v_t_prime) + self.epsilon)
            new_p = p_t

            # Apply constraints.
            if getattr(p, 'constraint', None) is not None:
                new_p = p.constraint(new_p)

            self.updates.append(K.update(p, new_p))
        return self.updates

    def get_config(self):
        config = {'lr': float(K.get_value(self.lr)),
                  'beta_1': float(K.get_value(self.beta_1)),
                  'beta_2': float(K.get_value(self.beta_2)),
                  'epsilon': self.epsilon,
                  'schedule_decay': self.schedule_decay}
        base_config = super(Nadam, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class TFOptimizer(Optimizer):
    """Wrapper class for native TensorFlow optimizers.
    """

    def __init__(self, optimizer):
        self.optimizer = optimizer
        with K.name_scope(self.__class__.__name__):
            self.iterations = K.variable(0, dtype='int64', name='iterations')

    @interfaces.legacy_get_updates_support
    def get_updates(self, loss, params):
        grads = self.optimizer.compute_gradients(loss, params)
        self.updates = [K.update_add(self.iterations, 1)]
        opt_update = self.optimizer.apply_gradients(
            grads, global_step=self.iterations)
        self.updates.append(opt_update)
        return self.updates

    @property
    def weights(self):
        raise NotImplementedError

    def get_config(self):
        raise NotImplementedError

    def from_config(self, config):
        raise NotImplementedError


# Aliases.

sgd = SGD
rmsprop = RMSprop
adagrad = Adagrad
adadelta = Adadelta
adam = Adam
adamax = Adamax
nadam = Nadam


def serialize(optimizer):
    return serialize_keras_object(optimizer)


def deserialize(config, custom_objects=None):
    """Inverse of the `serialize` function.

    # Arguments
        config: Optimizer configuration dictionary.
        custom_objects: Optional dictionary mapping
            names (strings) to custom objects
            (classes and functions)
            to be considered during deserialization.

    # Returns
        A Keras Optimizer instance.
    """
    all_classes = {
        'sgd': SGD,
        'rmsprop': RMSprop,
        'adagrad': Adagrad,
        'adadelta': Adadelta,
        'adam': Adam,
        'adamax': Adamax,
        'nadam': Nadam,
        'tfoptimizer': TFOptimizer,
    }
    # Make deserialization case-insensitive for built-in optimizers.
    if config['class_name'].lower() in all_classes:
        config['class_name'] = config['class_name'].lower()
    return deserialize_keras_object(config,
                                    module_objects=all_classes,
                                    custom_objects=custom_objects,
                                    printable_module_name='optimizer')


def get(identifier):
    """Retrieves a Keras Optimizer instance.

    # Arguments
        identifier: Optimizer identifier, one of
            - String: name of an optimizer
            - Dictionary: configuration dictionary.
            - Keras Optimizer instance (it will be returned unchanged).
            - TensorFlow Optimizer instance
                (it will be wrapped as a Keras Optimizer).

    # Returns
        A Keras Optimizer instance.

    # Raises
        ValueError: If `identifier` cannot be interpreted.
    """
    if K.backend() == 'tensorflow':
        # Wrap TF optimizer instances
        if isinstance(identifier, tf.train.Optimizer):
            return TFOptimizer(identifier)
    if isinstance(identifier, dict):
        return deserialize(identifier)
    elif isinstance(identifier, six.string_types):
        config = {'class_name': str(identifier), 'config': {}}
        return deserialize(config)
    if isinstance(identifier, Optimizer):
        return identifier
    else:
        raise ValueError('Could not interpret optimizer identifier:',
                         identifier)