336c6a042b
* Update symbolic_arguments.py Added validations to __init__ function * Update symbolic_arguments.py Removed the # TODO as requested
90 lines
3.3 KiB
Python
90 lines
3.3 KiB
Python
# flake8: noqa
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import numpy as np
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from keras_core import backend
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from keras_core import ops
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from keras_core import testing
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from keras_core.optimizers.sgd import SGD
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class SGDTest(testing.TestCase):
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def test_config(self):
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optimizer = SGD(
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learning_rate=0.5,
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momentum=0.06,
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nesterov=True,
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weight_decay=0.004,
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)
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self.run_class_serialization_test(optimizer)
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def test_single_step(self):
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optimizer = SGD(learning_rate=0.5)
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self.assertEqual(len(optimizer.variables), 2)
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grads = ops.array([1.0, 6.0, 7.0, 2.0])
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vars = backend.Variable([1.0, 2.0, 3.0, 4.0])
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optimizer.build([vars])
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optimizer.apply_gradients(zip([grads], [vars]))
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self.assertAllClose(vars, [0.5, -1.0, -0.5, 3.0], rtol=1e-4, atol=1e-4)
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self.assertEqual(len(optimizer.variables), 2)
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self.assertEqual(optimizer.variables[0], 1)
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self.assertEqual(optimizer.variables[1], 0.5)
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def test_weight_decay(self):
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grads, var1, var2, var3 = (
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ops.zeros(()),
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backend.Variable(2.0),
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backend.Variable(2.0, name="exclude"),
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backend.Variable(2.0),
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)
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optimizer_1 = SGD(learning_rate=1.0, weight_decay=0.004)
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optimizer_1.apply_gradients(zip([grads], [var1]))
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optimizer_2 = SGD(learning_rate=1.0, weight_decay=0.004)
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optimizer_2.exclude_from_weight_decay(var_names=["exclude"])
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optimizer_2.apply_gradients(zip([grads, grads], [var1, var2]))
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optimizer_3 = SGD(learning_rate=1.0, weight_decay=0.004)
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optimizer_3.exclude_from_weight_decay(var_list=[var3])
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optimizer_3.apply_gradients(zip([grads, grads], [var1, var3]))
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self.assertAlmostEqual(var1.numpy(), 1.9760959, decimal=6)
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self.assertAlmostEqual(var2.numpy(), 2.0, decimal=6)
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self.assertAlmostEqual(var3.numpy(), 2.0, decimal=6)
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def test_correctness_with_golden(self):
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optimizer = SGD(nesterov=True)
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x = backend.Variable(np.ones([10]))
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grads = ops.arange(0.1, 1.1, 0.1)
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first_grads = ops.full((10,), 0.01)
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# fmt: off
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golden = np.array(
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[[0.9999, 0.9999, 0.9999, 0.9999, 0.9999, 0.9999, 0.9999, 0.9999,
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0.9999, 0.9999], [0.9989, 0.9979, 0.9969, 0.9959, 0.9949, 0.9939,
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0.9929, 0.9919, 0.9909, 0.9899], [0.9979, 0.9959, 0.9939, 0.9919,
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0.9899, 0.9879, 0.9859, 0.9839, 0.9819, 0.9799], [0.9969, 0.9939,
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0.9909, 0.9879, 0.9849, 0.9819, 0.9789, 0.9759, 0.9729, 0.9699],
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[0.9959, 0.9919, 0.9879, 0.9839, 0.9799, 0.9759, 0.9719, 0.9679,
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0.9639, 0.9599]]
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)
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# fmt: on
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optimizer.apply_gradients(zip([first_grads], [x]))
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for i in range(5):
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self.assertAllClose(x, golden[i], rtol=5e-4, atol=5e-4)
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optimizer.apply_gradients(zip([grads], [x]))
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def test_clip_norm(self):
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optimizer = SGD(clipnorm=1)
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grad = [np.array([100.0, 100.0])]
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clipped_grad = optimizer._clip_gradients(grad)
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self.assertAllClose(clipped_grad[0], [2**0.5 / 2, 2**0.5 / 2])
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def test_clip_value(self):
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optimizer = SGD(clipvalue=1)
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grad = [np.array([100.0, 100.0])]
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clipped_grad = optimizer._clip_gradients(grad)
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self.assertAllClose(clipped_grad[0], [1.0, 1.0])
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