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Moved test_fit_simple_linear_model function out of run_all_in_graph_and_eager_mode. #1332

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Merged
merged 1 commit into from
Mar 23, 2020
Merged

Moved test_fit_simple_linear_model function out of run_all_in_graph_and_eager_mode. #1332

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53 changes: 26 additions & 27 deletions tensorflow_addons/optimizers/stochastic_weight_averaging_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,33 +70,6 @@ def test_averaging(self):
self.assertAllClose(var_0.read_value(), [0.8, 0.8])
self.assertAllClose(var_1.read_value(), [1.8, 1.8])

def test_fit_simple_linear_model(self):
seed = 0x2019
np.random.seed(seed)
tf.random.set_seed(seed)
num_examples = 100000
x = np.random.standard_normal((num_examples, 3))
w = np.random.standard_normal((3, 1))
y = np.dot(x, w) + np.random.standard_normal((num_examples, 1)) * 1e-4

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(input_shape=(3,), units=1))
# using num_examples - 1 since steps starts from 0.
optimizer = SWA(
"sgd", start_averaging=num_examples // 32 - 1, average_period=100
)
model.compile(optimizer, loss="mse")
model.fit(x, y, epochs=2)
optimizer.assign_average_vars(model.variables)

x = np.random.standard_normal((100, 3))
y = np.dot(x, w)

predicted = model.predict(x)

max_abs_diff = np.max(np.abs(predicted - y))
self.assertLess(max_abs_diff, 1e-3)

def test_optimizer_failure(self):
with self.assertRaises(TypeError):
_ = SWA(None, average_period=10)
Expand Down Expand Up @@ -128,5 +101,31 @@ def test_assign_batchnorm(self):
fit_bn(model, x, y)


def test_fit_simple_linear_model():
seed = 0x2019
np.random.seed(seed)
tf.random.set_seed(seed)
num_examples = 100000
x = np.random.standard_normal((num_examples, 3))
w = np.random.standard_normal((3, 1))
y = np.dot(x, w) + np.random.standard_normal((num_examples, 1)) * 1e-4

model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(input_shape=(3,), units=1))
# using num_examples - 1 since steps starts from 0.
optimizer = SWA("sgd", start_averaging=num_examples // 32 - 1, average_period=100)
model.compile(optimizer, loss="mse")
model.fit(x, y, epochs=2)
optimizer.assign_average_vars(model.variables)

x = np.random.standard_normal((100, 3))
y = np.dot(x, w)

predicted = model.predict(x)

max_abs_diff = np.max(np.abs(predicted - y))
assert max_abs_diff < 1e-3


if __name__ == "__main__":
sys.exit(pytest.main([__file__]))