4212fdd5cb
* Add golden correctness tests for Adam and SGD * Fix dtype issues * Sync with main (#56) * Minor touch ups * Fix a pretty major bug * Format code * Big rethink of Variable API * Make build-by-run the default build(), leveraging new zero_history KerasTensor mode * Minor fixes * Format code * Switch back to build-by-eager-run for simplicity * Add raise upon build failure * Work around JAX bug. * Add a few more tests. * Add saving tests * Adds test suite for SGD and golden correctness tests for all optimizers (#40) * Add golden correctness tests for Adam and SGD * Fix dtype issues * Add binary accuracy (#41) * chore: adding binary accuracy * chore: fix docstring * Add tests for add_loss and activity regularization. * Reformat code * Add ActivityRegularization layer * Fix JAX CI. * Add Lambda Callback (#42) * Add LambdaCallback * Add Lambda Callback * Add Lambda Callback * Rename lambda_callback_test.py * Add einsum (#43) * Add einsum * address comments * Fix format line length (#45) * Add Embedding layer * Shorten lines * Add .vscode to .gitignore (#46) * rm vscode settings * add .vscode to gitignore * Set demo program backend (#48) * Add tests for training arg resolution in Layer. * Implement mixed precision. * Replace backend.execute with backend.numpy.XXX (#50) * Add cosine similarity loss and update l2_normalize from regularizers (#34) * Begin cosine loss * Add testing for cosine similarity * Fix formatting * Docstring standardization * Formatting * Create numerical_utils * Fix issue with call context lingering. * Add the EarlyStopping callback (#44) * add earlystopping callback * addressing comments * address comments * addressing comments * remove unused imports * re-enable imports checks (#51) * Add nn.one_hot (#52) * Add GaussianDropout layer. * Add GaussianNoise layer * Add Categorical Accuracy Metric (#47) * chore: adding categorical accuracy metric * chore: reformat docstrings * chore: reformat * chore: ndims with len * refactor the docstring * Fix typos * Implement masking. --------- Co-authored-by: Francois Chollet <francois.chollet@gmail.com> Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com> Co-authored-by: Ramesh Sampath <1437573+sampathweb@users.noreply.github.com> Co-authored-by: Chen Qian <chenmoney@google.com> Co-authored-by: Haifeng Jin <5476582+haifeng-jin@users.noreply.github.com> Co-authored-by: Gabriel Rasskin <43894452+grasskin@users.noreply.github.com> * Adds rmsprop optimizer and tests * Add AdamW optimizer and tests, minor formatting changes * Implemented formatting fixes * Adds clip norm and clip value tests to Adam * Adds Adagrad and Adadelta optimizers * Applies fixes to formatting and deletes unnecessary kwargs --------- Co-authored-by: Francois Chollet <francois.chollet@gmail.com> Co-authored-by: Aritra Roy Gosthipaty <aritra.born2fly@gmail.com> Co-authored-by: Ramesh Sampath <1437573+sampathweb@users.noreply.github.com> Co-authored-by: Chen Qian <chenmoney@google.com> Co-authored-by: Haifeng Jin <5476582+haifeng-jin@users.noreply.github.com> Co-authored-by: Gabriel Rasskin <43894452+grasskin@users.noreply.github.com>
86 lines
3.1 KiB
Python
86 lines
3.1 KiB
Python
# flake8: noqa
|
|
|
|
|
|
import numpy as np
|
|
|
|
from keras_core import backend
|
|
from keras_core import testing
|
|
from keras_core.optimizers.adagrad import Adagrad
|
|
|
|
|
|
class AdagradTest(testing.TestCase):
|
|
def test_config(self):
|
|
optimizer = Adagrad(
|
|
learning_rate=0.5,
|
|
initial_accumulator_value=0.2,
|
|
epsilon=1e-5,
|
|
)
|
|
self.run_class_serialization_test(optimizer)
|
|
|
|
def test_single_step(self):
|
|
optimizer = Adagrad(learning_rate=0.5)
|
|
grads = np.array([1.0, 6.0, 7.0, 2.0])
|
|
vars = backend.Variable([1.0, 2.0, 3.0, 4.0])
|
|
optimizer.apply_gradients(zip([grads], [vars]))
|
|
self.assertAllClose(
|
|
vars, [0.5233, 1.5007, 2.5005, 3.5061], rtol=1e-4, atol=1e-4
|
|
)
|
|
|
|
def test_weight_decay(self):
|
|
grads, var1, var2, var3 = (
|
|
np.zeros(()),
|
|
backend.Variable(2.0),
|
|
backend.Variable(2.0, name="exclude"),
|
|
backend.Variable(2.0),
|
|
)
|
|
optimizer_1 = Adagrad(learning_rate=1.0, weight_decay=0.004)
|
|
optimizer_1.apply_gradients(zip([grads], [var1]))
|
|
|
|
optimizer_2 = Adagrad(learning_rate=1.0, weight_decay=0.004)
|
|
optimizer_2.exclude_from_weight_decay(var_names=["exclude"])
|
|
optimizer_2.apply_gradients(zip([grads, grads], [var1, var2]))
|
|
|
|
optimizer_3 = Adagrad(learning_rate=1.0, weight_decay=0.004)
|
|
optimizer_3.exclude_from_weight_decay(var_list=[var3])
|
|
optimizer_3.apply_gradients(zip([grads, grads], [var1, var3]))
|
|
|
|
self.assertAlmostEqual(var1.numpy(), 1.9760959, decimal=6)
|
|
self.assertAlmostEqual(var2.numpy(), 2.0, decimal=6)
|
|
self.assertAlmostEqual(var3.numpy(), 2.0, decimal=6)
|
|
|
|
def test_correctness_with_golden(self):
|
|
optimizer = Adagrad(
|
|
learning_rate=0.2, initial_accumulator_value=0.3, epsilon=1e-6
|
|
)
|
|
|
|
x = backend.Variable(np.ones([10]))
|
|
grads = np.arange(0.1, 1.1, 0.1)
|
|
first_grads = np.full((10,), 0.01)
|
|
|
|
# fmt: off
|
|
golden = np.array(
|
|
[[0.9963, 0.9963, 0.9963, 0.9963, 0.9963, 0.9963, 0.9963, 0.9963, 0.9963, 0.9963],
|
|
[0.9604, 0.9278, 0.9003, 0.8784, 0.8615, 0.8487, 0.8388, 0.8313, 0.8255, 0.8209],
|
|
[0.9251, 0.8629, 0.8137, 0.7768, 0.7497, 0.7298, 0.7151, 0.704, 0.6956, 0.6891],
|
|
[0.8903, 0.8012, 0.7342, 0.6862, 0.6521, 0.6277, 0.6099, 0.5967, 0.5867, 0.579],
|
|
[0.856, 0.7422, 0.6604, 0.6037, 0.5644, 0.5367, 0.5168, 0.5021, 0.491, 0.4825]]
|
|
)
|
|
# fmt: on
|
|
|
|
optimizer.apply_gradients(zip([first_grads], [x]))
|
|
for i in range(5):
|
|
self.assertAllClose(x, golden[i], rtol=5e-4, atol=5e-4)
|
|
optimizer.apply_gradients(zip([grads], [x]))
|
|
|
|
def test_clip_norm(self):
|
|
optimizer = Adagrad(clipnorm=1)
|
|
grad = [np.array([100.0, 100.0])]
|
|
clipped_grad = optimizer._clip_gradients(grad)
|
|
self.assertAllClose(clipped_grad[0], [2**0.5 / 2, 2**0.5 / 2])
|
|
|
|
def test_clip_value(self):
|
|
optimizer = Adagrad(clipvalue=1)
|
|
grad = [np.array([100.0, 100.0])]
|
|
clipped_grad = optimizer._clip_gradients(grad)
|
|
self.assertAllClose(clipped_grad[0], [1.0, 1.0])
|