experiments/STT-tensorflow
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Do not have Sequential models cast inputs

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PiperOrigin-RevId: 276584832
Change-Id: I90c5b0d97ef31393de82a38b6340b3b3dc489d60
This commit is contained in:
Reed Wanderman-Milne 2019-10-24 15:59:13 -07:00 committed by TensorFlower Gardener
parent 00ea430324
commit b74e8fb4a5
2 changed files with 16 additions and 1 deletions
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2
tensorflow/python/keras/engine/sequential.py
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@ -100,7 +100,7 @@ class Sequential(training.Model):
@trackable.no_automatic_dependency_tracking
def __init__(self, layers=None, name=None):
super(Sequential, self).__init__(name=name)
super(Sequential, self).__init__(name=name, autocast=False)
self.supports_masking = True
self._build_input_shape = None
self._compute_output_and_mask_jointly = True

15
tensorflow/python/keras/engine/sequential_test.py
View File

@ -449,6 +449,21 @@ class TestSequential(keras_parameterized.TestCase):
model.pop()
self.assertEqual(model._layers[-1], layer)
@testing_utils.enable_v2_dtype_behavior
def test_sequential_does_not_autocast(self):
class AssertFloat64InputLayer(keras.layers.Layer):
def __init__(self):
super(AssertFloat64InputLayer, self).__init__(autocast=False)
def call(self, inputs):
assert inputs.dtype == 'float64', 'inputs are %s' % inputs.dtype
return array_ops.identity(inputs)
model = keras.Sequential([AssertFloat64InputLayer(), keras.layers.Dense(4)])
model(np.random.random((4, 4)))
class TestSequentialEagerIntegration(keras_parameterized.TestCase):