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Minor edits and fixes for kernelized layers.

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PiperOrigin-RevId: 239195286
This commit is contained in:
Petros Mol 2019-03-19 08:33:32 -07:00 committed by TensorFlower Gardener
parent ad2e814522
commit abf1fdc852
3 changed files with 14 additions and 12 deletions
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2
tensorflow/python/keras/layers/kernelized.py
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@ -85,7 +85,7 @@ class RandomFourierFeatures(base_layer.Layer):
model.add(random_features_layer)
model.add(tf.keras.layers.Dense(units=num_classes, activation='softmax')
model.compile(elif isinstance(identifier, six.string_types):
model.compile(
loss=tf.keras.losses.categorical_crossentropy, optimizer=..., metrics=...)
```

16
tensorflow/python/keras/layers/kernelized_test.py
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@ -356,21 +356,23 @@ class RandomFourierFeaturesTest(test.TestCase, parameterized.TestCase):
self.assertLess(abs_error, 0.5)
@parameterized.named_parameters(
('gaussian', 'gaussian', 10.0, _exact_gaussian(stddev=10.0)),
('laplacian', 'laplacian', 50.0, _exact_laplacian(stddev=50.0)))
('gaussian', 'gaussian', 5.0, _exact_gaussian(stddev=5.0)),
('laplacian', 'laplacian', 10.0, _exact_laplacian(stddev=10.0)))
@test_util.run_in_graph_and_eager_modes()
def test_good_kernel_approximation_multiple_inputs(self, initializer, scale,
exact_kernel_fn):
# Parameters.
input_dim = 5
output_dim = 5000
output_dim = 2000
x_rows = 20
y_rows = 30
random_seed.set_random_seed(1234)
x = random_ops.random_uniform(shape=(x_rows, input_dim), maxval=1.0)
y = random_ops.random_uniform(shape=(y_rows, input_dim), maxval=1.0)
x = constant_op.constant(
np.random.uniform(size=(x_rows, input_dim)), dtype=dtypes.float32)
y = constant_op.constant(
np.random.uniform(size=(y_rows, input_dim)), dtype=dtypes.float32)
random_seed.set_random_seed(1234)
rff_layer = kernel_layers.RandomFourierFeatures(
output_dim=output_dim,
kernel_initializer=initializer,
@ -384,7 +386,7 @@ class RandomFourierFeaturesTest(test.TestCase, parameterized.TestCase):
approx_kernel_matrix = kernelized_utils.inner_product(output_x, output_y)
exact_kernel_matrix = exact_kernel_fn(x, y)
self._assert_all_close(approx_kernel_matrix, exact_kernel_matrix, atol=0.1)
self._assert_all_close(approx_kernel_matrix, exact_kernel_matrix, atol=0.05)
if __name__ == '__main__':

8
tensorflow/python/keras/utils/kernelized_utils.py
View File

@ -58,7 +58,7 @@ def inner_product(u, v):
def exact_gaussian_kernel(x, y, stddev):
"""Computes exact Gaussian kernel value(s) for tensors x and y and stddev.
r"""Computes exact Gaussian kernel value(s) for tensors x and y and stddev.
The Gaussian kernel for vectors u, v is defined as follows:
K(u, v) = exp(-||u-v||^2 / (2* stddev^2))
@ -79,7 +79,7 @@ def exact_gaussian_kernel(x, y, stddev):
all (u,v) pairs where u, v are rows from x and y respectively.
Raises:
InvalidShapeError: if the shapes of x, y are not compatible.
ValueError: if the shapes of x, y are not compatible.
"""
x_aligned, y_aligned = _align_matrices(x, y)
diff_squared_l2_norm = math_ops.reduce_sum(
@ -88,7 +88,7 @@ def exact_gaussian_kernel(x, y, stddev):
def exact_laplacian_kernel(x, y, stddev):
"""Computes exact Laplacian kernel value(s) for tensors x and y using stddev.
r"""Computes exact Laplacian kernel value(s) for tensors x and y using stddev.
The Laplacian kernel for vectors u, v is defined as follows:
K(u, v) = exp(-||u-v|| / stddev)
@ -109,7 +109,7 @@ def exact_laplacian_kernel(x, y, stddev):
all (u,v) pairs where u, v are rows from x and y respectively.
Raises:
InvalidShapeError: if the shapes of x, y are not compatible.
ValueError: if the shapes of x, y are not compatible.
"""
x_aligned, y_aligned = _align_matrices(x, y)
diff_l1_norm = math_ops.reduce_sum(