sandeep-krishnamurthy closed pull request #13467: [MXNET-1235] Add a test for
AdaMax optimizer
URL: https://github.com/apache/incubator-mxnet/pull/13467
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diff --git a/tests/python/unittest/test_optimizer.py
b/tests/python/unittest/test_optimizer.py
index 334b7d4c0fd..b03dcdcfba4 100644
--- a/tests/python/unittest/test_optimizer.py
+++ b/tests/python/unittest/test_optimizer.py
@@ -16,6 +16,7 @@
# under the License.
import numpy as np
+import itertools
import mxnet as mx
import mxnet.lr_scheduler as lr_scheduler
from mxnet import gluon
@@ -501,7 +502,6 @@ def test_ftml():
# ADAM
-
class PyAdam(mx.optimizer.Optimizer):
"""python reference implemenation of adam"""
def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999,
epsilon=1e-8,
@@ -613,6 +613,80 @@ def test_adam():
dtype, w_stype='default',
g_stype='row_sparse',
rtol=1e-4, atol=2e-5)
+
+# AdaMax
+class PyAdamax(mx.optimizer.Optimizer):
+ """The python reference of AdaMax optimizer.
+
+ This class implements the AdaMax optimizer, one variant of Adam based on
the infinity norm,
+ available at http://arxiv.org/abs/1412.6980 Section 7.
+
+ The optimizer updates the weight by::
+ grad = clip(grad * rescale_grad + wd * weight, clip_gradient)
+ m = beta1 * m_t + (1 - beta1) * grad
+ u = maximum(beta2 * u, abs(grad))
+ weight -= lr / (1 - beta1**t) * m / u
+
+ This optimizer accepts the following parameters in addition to those
accepted
+ by :class:`.Optimizer`.
+
+ Parameters
+ ----------
+ beta1 : float, optional
+ Exponential decay rate for the first moment estimates.
+ beta2 : float, optional
+ Exponential decay rate for the second moment estimates.
+ """
+ def __init__(self, learning_rate=0.002, beta1=0.9, beta2=0.999, **kwargs):
+ super(PyAdamax, self).__init__(learning_rate=learning_rate, **kwargs)
+ self.beta1 = beta1
+ self.beta2 = beta2
+
+ def create_state(self, index, weight):
+ return (mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype),
# mean
+ mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype))
# variance
+
+ def update(self, index, weight, grad, state):
+ self._update_count(index)
+ lr = self._get_lr(index)
+ wd = self._get_wd(index)
+
+ t = self._index_update_count[index]
+ lr /= (1. - self.beta1**t)
+
+ # preprocess grad
+ grad = grad * self.rescale_grad + wd * weight
+ if self.clip_gradient is not None:
+ grad = mx.nd.clip(grad, -self.clip_gradient, self.clip_gradient)
+
+ # update m_t and u_t
+ m_t, u_t = state
+ m_t[:] = self.beta1 * m_t + (1. - self.beta1) * grad
+ u_t[:] = mx.nd.maximum(self.beta2 * u_t, mx.nd.abs(grad))
+
+ # update weight
+ weight[:] -= lr * m_t / u_t
+
+
+@with_seed()
+def test_adamax():
+ opt1 = PyAdamax
+ opt2 = mx.optimizer.Adamax
+ shape = (3, 4, 5)
+ cg_options = [{}, {'clip_gradient': 0.4}, {'clip_gradient': 0.5}]
+ rg_options = [{}, {'rescale_grad': 0.14}, {'rescale_grad': 0.8}]
+ wd_options = [{}, {'wd': 0.03}, {'wd': 0.05}, {'wd': 0.07}]
+ mp_options = [{}, {'multi_precision': False}, {'multi_precision': True}]
+ for dtype in [np.float16, np.float32, np.float64]:
+ for params in itertools.product(cg_options, rg_options, wd_options,
mp_options):
+ kwarg = {k: v for param in params for k, v in param.items()}
+ if (dtype == np.float16 and
+ ('multi_precision' not in kwarg or
+ not kwarg['multi_precision'])):
+ continue
+ compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype)
+
+
# Signum
class PySignum(mx.optimizer.Optimizer):
"""The python reference of Signum optimizer.
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