Python chainer.optimizers.AdaDelta() Examples

The following are 4 code examples of chainer.optimizers.AdaDelta(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module chainer.optimizers , or try the search function .
Example #1
Source File: test_hdf5.py    From chainer with MIT License 6 votes vote down vote up 
def setUp(self):
        fd, path = tempfile.mkstemp()
        os.close(fd)
        self.temp_file_path = path

        child = link.Chain()
        with child.init_scope():
            child.linear = links.Linear(2, 3)
            child.Wc = chainer.Parameter(shape=(2, 3))

        self.parent = link.Chain()
        with self.parent.init_scope():
            self.parent.child = child
            self.parent.Wp = chainer.Parameter(shape=(2, 3))

        self.optimizer = optimizers.AdaDelta()
        self.optimizer.setup(self.parent)

        self.parent.cleargrads()
        self.optimizer.update()  # init states
Example #2
Source File: test_optimizers_by_linear_model.py    From chainer with MIT License 5 votes vote down vote up 
def create(self):
        return optimizers.AdaDelta(eps=1e-5)
Example #3
Source File: test_npz.py    From chainer with MIT License 5 votes vote down vote up 
def setUp(self):
        if self.file_type == 'filename':
            fd, path = tempfile.mkstemp()
            os.close(fd)
            self.file = path
        elif self.file_type == 'bytesio':
            self.file = six.BytesIO()
        else:
            assert False

        child = link.Chain()
        with child.init_scope():
            child.linear = links.Linear(2, 3)
            child.Wc = chainer.Parameter(shape=(2, 3))

        self.parent = link.Chain()
        with self.parent.init_scope():
            self.parent.child = child
            self.parent.Wp = chainer.Parameter(shape=(2, 3))

        self.optimizer = optimizers.AdaDelta()
        self.optimizer.setup(self.parent)

        self.parent.cleargrads()
        self.optimizer.update()  # init all states

        self.savez = numpy.savez_compressed if self.compress else numpy.savez
Example #4
Source File: chainer_backend.py    From Chimp with Apache License 2.0 5 votes vote down vote up 
def set_params(self, params):

        self.gpu = params.get('gpu',False)
        self.learning_rate = params.get('learning_rate',0.00025)
        self.decay_rate = params.get('decay_rate',0.95)
        self.discount = params.get('discount',0.95)
        self.clip_err = params.get('clip_err',False)
        self.target_net_update = params.get('target_net_update',10000)
        self.double_DQN = params.get('double_DQN',False)

        # setting up various possible gradient update algorithms
        opt = params.get('optim_name', 'ADAM')
        if opt == 'RMSprop':
            self.optimizer = optimizers.RMSprop(lr=self.learning_rate, alpha=self.decay_rate)

        elif opt == 'ADADELTA':
            print("Supplied learning rate not used with ADADELTA gradient update method")
            self.optimizer = optimizers.AdaDelta()

        elif opt == 'ADAM':
            self.optimizer = optimizers.Adam(alpha=self.learning_rate)

        elif opt == 'SGD':
            self.optimizer = optimizers.SGD(lr=self.learning_rate)

        else:
            print('The requested optimizer is not supported!!!')
            exit()

        if self.clip_err is not False:
            self.optimizer.add_hook(chainer.optimizer.GradientClipping(self.clip_err))

        self.optim_name = params['optim_name']