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PR #50519: GPU Symmetric Fake Quantization #762

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PR #50519: GPU Symmetric Fake Quantization #762

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8 changes: 4 additions & 4 deletions ...rflow_model_optimization/python/core/quantization/keras/quantize_aware_activation_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,14 +103,14 @@ def testAppliesQuantizationPreActivation(self):

model = keras.Sequential([layer])

x = np.array([[1.0, 2.0]])
x = np.array([[1.0, 3.0]])
# expected_activation is determined using the float buckets when [-6, 6] is
# quantized. Derived using `tf.fake_quant_with_min_max_vars`. For sigmoid,
# quantization is applied twice.
#
# FakeQuant([1.0, 2.0]) = [0.9882355, 1.9764705]
# Softmax([0.9882355, 1.9764705]) = [0.27126083, 0.72873914]
expected_activation = np.array([[0.27126083, 0.72873914]])
# FakeQuant([1.0, 3.0]) = [0.9882353, 3.0117648]
# Softmax([0.9882355, 1.9764705]) = [0.11675452 0.88324547]
expected_activation = np.array([[0.11675452, 0.88324547]])

for weight in layer.weights:
self.assertIn('pre_activation', weight.name)
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