def test_default_params(self):
opt, data = util.build_rosen('rmsprop')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 14))
assert np.allclose(opt.epsilon.eval(), 1e-8)
break
def test_default_params(self):
opt, data = util.build_rosen('rmsprop')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 14))
assert np.allclose(opt.epsilon.eval(), 1e-8)
break
def test_default_params(self):
opt, data = util.build_rosen('adam')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.beta1.eval(), np.exp(-np.log(2) / 7))
assert np.allclose(opt.beta2.eval(), np.exp(-np.log(2) / 69))
assert np.allclose(opt.epsilon.eval(), 1e-8)
break
def test_params(self):
opt, data = util.build_rosen('adadelta')
for _ in opt.iterate(data,
rms_halflife=10,
rms_regularizer=20):
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.epsilon.eval(), 20)
break
def test_default_params(self):
opt, data = util.build_rosen('rprop')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.step_increase.eval(), 1.01)
assert np.allclose(opt.step_decrease.eval(), 0.99)
assert np.allclose(opt.min_step.eval(), 0)
assert np.allclose(opt.max_step.eval(), 100)
break
def test_default_params(self):
opt, data = util.build_rosen('rprop')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.step_increase.eval(), 1.01)
assert np.allclose(opt.step_decrease.eval(), 0.99)
assert np.allclose(opt.min_step.eval(), 0)
assert np.allclose(opt.max_step.eval(), 100)
break
def test_default_params(self):
opt, data = util.build_rosen('adam')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.beta1_decay.eval(), 1 - 1e-6)
assert np.allclose(opt.beta1.eval(), np.exp(-np.log(2) / 7))
assert np.allclose(opt.beta2.eval(), np.exp(-np.log(2) / 69))
assert np.allclose(opt.epsilon.eval(), 1e-8)
break
def test_params(self):
opt, data = util.build_rosen('esgd')
for _ in opt.iterate(data,
learning_rate=0.3,
rms_halflife=10,
rms_regularizer=20):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.epsilon.eval(), 20)
break
def test_rosen(self):
opt, train = util.build_rosen('tester')
assert isinstance(opt, Tester)
# run the optimizer for three iterations. check that the x and y values
# (being monitored) increase at each iteration.
for i, (tm, vm) in enumerate(opt.iterate(train, max_updates=3)):
assert tm['x'] >= vm['x']
assert tm['y'] >= vm['y']
assert i < 3
def test_rosen(self):
opt, train = util.build_rosen('tester')
assert isinstance(opt, Tester)
# run the optimizer for three iterations. check that the x and y values
# (being monitored) increase at each iteration.
for i, (tm, vm) in enumerate(opt.iterate(train, max_updates=3)):
assert tm['x'] >= vm['x']
assert tm['y'] >= vm['y']
assert i < 3
def test_params(self):
opt, data = util.build_rosen('rmsprop')
for _ in opt.iterate(data,
learning_rate=0.3,
rms_halflife=10,
rms_regularizer=20):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.epsilon.eval(), 20)
break
def test_gradient_clip(self):
opt, data = util.build_rosen('tester')
for _ in opt.iterate(data, gradient_clip=1):
assert opt.max_gradient_elem == 1
break
for _ in opt.iterate(data, max_gradient_clip=2):
assert opt.max_gradient_elem == 2
break
for _ in opt.iterate(data, max_gradient_elem=3):
assert opt.max_gradient_elem == 3
break
def test_params(self):
opt, data = util.build_rosen('esgd')
opt.hv_method = 'lop' # TODO(leif): incorporate into downhill.build()?
for _ in opt.iterate(data,
learning_rate=0.3,
rms_halflife=10,
rms_regularizer=20):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.ewma.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.epsilon.eval(), 20)
break
def test_default_params(self):
opt, data = util.build_rosen('nag')
for _ in opt.iterate(data):
assert opt.nesterov == True
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.momentum, 0)
assert np.allclose(opt.patience, 5)
assert np.allclose(opt.min_improvement, 0)
assert np.allclose(opt.max_gradient_norm, 0)
assert np.allclose(opt.max_gradient_elem, 0)
break
def test_gradient_clip(self):
opt, data = util.build_rosen('tester')
for _ in opt.iterate(data, gradient_clip=1):
assert opt.max_gradient_elem == 1
break
for _ in opt.iterate(data, max_gradient_clip=2):
assert opt.max_gradient_elem == 2
break
for _ in opt.iterate(data, max_gradient_elem=3):
assert opt.max_gradient_elem == 3
break
def test_default_params(self):
opt, data = util.build_rosen('nag')
for _ in opt.iterate(data):
assert opt.nesterov == True
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.momentum, 0)
assert np.allclose(opt.patience, 5)
assert np.allclose(opt.min_improvement, 0)
assert np.allclose(opt.max_gradient_norm, 0)
assert np.allclose(opt.max_gradient_elem, 0)
break
def test_params(self):
opt, data = util.build_rosen('adam')
for _ in opt.iterate(data,
learning_rate=0.3,
beta1_halflife=10,
beta2_halflife=20,
rms_regularizer=11):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.beta1.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.beta2.eval(), np.exp(-np.log(2) / 20))
assert np.allclose(opt.epsilon.eval(), 11)
break
def test_rosen_unnamed(self):
opt, train = util.build_rosen('tester', name=False, monitor_gradients=True)
assert isinstance(opt, Tester)
# run the optimizer for three iterations. check that the x and y values
# (being monitored) increase at each iteration.
for i, (tm, vm) in enumerate(opt.iterate(train, max_updates=3)):
assert tm['x'] >= vm['x']
assert tm['y'] >= vm['y']
# check there's a manually-named parameter in here.
assert 1 == sum(1 for k in tm if 'unnamed' in k), tm
assert i < 3
def test_rosen_unnamed(self):
opt, train = util.build_rosen('tester',
name=False,
monitor_gradients=True)
assert isinstance(opt, Tester)
# run the optimizer for three iterations. check that the x and y values
# (being monitored) increase at each iteration.
for i, (tm, vm) in enumerate(opt.iterate(train, max_updates=3)):
assert tm['x'] >= vm['x']
assert tm['y'] >= vm['y']
# check there's a manually-named parameter in here.
assert 1 == sum(1 for k in tm if 'unnamed' in k), tm
assert i < 3
def test_params(self):
opt, data = util.build_rosen('rprop')
for _ in opt.iterate(data,
learning_rate=0.3,
rprop_increase=22,
rprop_decrease=101,
rprop_min_step=50,
rprop_max_step=-10):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.step_increase.eval(), 22)
assert np.allclose(opt.step_decrease.eval(), 101)
assert np.allclose(opt.min_step.eval(), 50)
assert np.allclose(opt.max_step.eval(), -10)
break
def test_params(self):
opt, data = util.build_rosen('adam')
for _ in opt.iterate(data,
learning_rate=0.3,
beta1_decay=0.5,
beta1_halflife=10,
beta2_halflife=20,
rms_regularizer=11):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.beta1_decay.eval(), 0.5)
assert np.allclose(opt.beta1.eval(), np.exp(-np.log(2) / 10))
assert np.allclose(opt.beta2.eval(), np.exp(-np.log(2) / 20))
assert np.allclose(opt.epsilon.eval(), 11)
break
def test_params(self):
opt, data = util.build_rosen('rprop')
for _ in opt.iterate(data,
learning_rate=0.3,
rprop_increase=22,
rprop_decrease=101,
rprop_min_step=50,
rprop_max_step=-10):
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.step_increase.eval(), 22)
assert np.allclose(opt.step_decrease.eval(), 101)
assert np.allclose(opt.min_step.eval(), 50)
assert np.allclose(opt.max_step.eval(), -10)
break
def test_params(self):
opt, data = util.build_rosen('nag')
for _ in opt.iterate(data,
learning_rate=0.3,
momentum=10,
patience=20,
min_improvement=0.1,
max_gradient_elem=4,
max_gradient_norm=5,
nesterov=False):
assert opt.nesterov == True # nesterov always True for NAG
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.momentum, 10)
assert np.allclose(opt.patience, 20)
assert np.allclose(opt.min_improvement, 0.1)
assert np.allclose(opt.max_gradient_norm, 5)
assert np.allclose(opt.max_gradient_elem, 4)
break
def test_params(self):
opt, data = util.build_rosen('nag')
for _ in opt.iterate(data,
learning_rate=0.3,
momentum=10,
patience=20,
min_improvement=0.1,
max_gradient_elem=4,
max_gradient_norm=5,
nesterov=False):
assert opt.nesterov == True # nesterov always True for NAG
assert np.allclose(opt.learning_rate.eval(), 0.3)
assert np.allclose(opt.momentum, 10)
assert np.allclose(opt.patience, 20)
assert np.allclose(opt.min_improvement, 0.1)
assert np.allclose(opt.max_gradient_norm, 5)
assert np.allclose(opt.max_gradient_elem, 4)
break
def test_sgd(self):
assert isinstance(util.build_rosen('sgd')[0], downhill.SGD)
assert isinstance(util.build_factor('sgd')[0], downhill.SGD)
def test_set_best_params(self):
opt, _ = util.build_rosen('tester')
opt._best_params = [[1, 2]]
opt.set_params('best')
assert np.allclose(opt._params[0].get_value(), [1, 2])
def test_adam(self):
assert isinstance(util.build_rosen('Adam')[0], downhill.Adam)
def test_default_params(self):
opt, data = util.build_rosen('adagrad')
for _ in opt.iterate(data):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.epsilon.eval(), 1e-8)
break
def test_params(self):
opt, data = util.build_rosen('adagrad')
for _ in opt.iterate(data, rms_regularizer=0.1):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.epsilon.eval(), 0.1)
break
def test_esgd(self):
assert isinstance(util.build_rosen('EsGd')[0], downhill.ESGD)
def test_nag(self):
assert isinstance(util.build_rosen('nag')[0], downhill.NAG)
def test_params(self):
opt, data = util.build_rosen('adagrad')
for _ in opt.iterate(data, regularizer=0.1):
assert np.allclose(opt.learning_rate.eval(), 1e-4)
assert np.allclose(opt.epsilon.eval(), 0.1)
break
def test_set_params(self):
opt, _ = util.build_rosen('straight')
opt.set_params([[1, 2]])
assert np.allclose(opt._params[0].get_value(), [1, 2])
def test_rosen(self):
util.assert_progress(*util.build_rosen('adagrad'))
def test_adam(self):
assert isinstance(util.build_rosen('Adam')[0], downhill.Adam)
def test_adadelta(self):
assert isinstance(util.build_rosen('ADADELTA')[0], downhill.ADADELTA)
def test_nag(self):
assert isinstance(util.build_rosen('nag')[0], downhill.NAG)
def test_rosen(self):
util.assert_progress(*util.build_rosen('nag'))
def test_rmsprop(self):
assert isinstance(util.build_rosen('RmsProp')[0], downhill.RMSProp)
def test_rosen(self):
util.assert_progress(
*util.build_rosen('sgd'),
monitor_gradients=True)
def test_adadelta(self):
assert isinstance(util.build_rosen('ADADELTA')[0], downhill.ADADELTA)
def test_rosen(self):
util.assert_progress(*util.build_rosen('esgd'), learning_rate=1e-6)
def test_rosen(self):
util.assert_progress(*util.build_rosen('rmsprop'))
def test_rmsprop(self):
assert isinstance(util.build_rosen('RmsProp')[0], downhill.RMSProp)
def test_rosen(self):
util.assert_progress(*util.build_rosen('nag'))
def test_esgd(self):
assert isinstance(util.build_rosen('EsGd')[0], downhill.ESGD)
def test_rosen(self):
util.assert_progress(*util.build_rosen('sgd'), monitor_gradients=True)
def test_rosen(self):
util.assert_progress(*util.build_rosen('rmsprop'))
def test_gradient_clip(self):
opt, data = util.build_rosen('straight')
for _ in opt.iterate(data, max_gradient_elem=3):
assert opt.max_gradient_elem == 3
break
def test_sgd(self):
assert isinstance(util.build_rosen('sgd')[0], downhill.SGD)
assert isinstance(util.build_factor('sgd')[0], downhill.SGD)
def test_rosen(self):
util.assert_progress(*util.build_rosen('adadelta'))
def test_set_best_params(self):
opt, _ = util.build_rosen('tester')
opt._best_params = [[1, 2]]
opt.set_params('best')
assert np.allclose(opt._params[0].get_value(), [1, 2])
def test_rosen(self):
util.assert_progress(*util.build_rosen('esgd'), learning_rate=1e-6)
