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Add fp16 support to convolutional layers that support it.

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PiperOrigin-RevId: 158086284
This commit is contained in:
Francois Chollet 2017-06-05 17:44:15 -07:00 committed by TensorFlower Gardener
parent 7d3fbba48f
commit cc1a02d37a
2 changed files with 24 additions and 0 deletions
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3
tensorflow/python/layers/convolutional.py
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@ -371,6 +371,7 @@ def conv1d(inputs,
activity_regularizer=activity_regularizer,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@ -546,6 +547,7 @@ def conv2d(inputs,
activity_regularizer=activity_regularizer,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)
@ -1277,6 +1279,7 @@ def conv2d_transpose(inputs,
activity_regularizer=activity_regularizer,
trainable=trainable,
name=name,
dtype=inputs.dtype.base_dtype,
_reuse=reuse,
_scope=name)
return layer.apply(inputs)

21
tensorflow/python/layers/convolutional_test.py
View File

@ -69,6 +69,13 @@ class ConvTest(test.TestCase):
self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 4, 32])
self.assertListEqual(layer.bias.get_shape().as_list(), [32])
def testConv2DFloat16(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 4), dtype='float16')
output = conv_layers.conv2d(images, 32, [3, 3], activation=nn_ops.relu)
self.assertListEqual(output.get_shape().as_list(),
[5, height - 2, width - 2, 32])
def testCreateConv2DIntegerKernelSize(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 4))
@ -144,6 +151,12 @@ class ConvTest(test.TestCase):
self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 4, 32])
self.assertListEqual(layer.bias.get_shape().as_list(), [32])
def testConv1DFloat16(self):
width = 7
data = random_ops.random_uniform((5, width, 4), dtype='float16')
output = conv_layers.conv1d(data, 32, 3, activation=nn_ops.relu)
self.assertListEqual(output.get_shape().as_list(), [5, width - 2, 32])
def testCreateConv1DChannelsFirst(self):
width = 7
data = random_ops.random_uniform((5, 4, width))
@ -522,6 +535,14 @@ class Conv2DTransposeTest(test.TestCase):
self.assertListEqual(layer.kernel.get_shape().as_list(), [3, 3, 32, 4])
self.assertListEqual(layer.bias.get_shape().as_list(), [32])
def testConv2DTransposeFloat16(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 4), dtype='float16')
output = conv_layers.conv2d_transpose(images, 32, [3, 3],
activation=nn_ops.relu)
self.assertListEqual(output.get_shape().as_list(),
[5, height + 2, width + 2, 32])
def testCreateConv2DTransposeIntegerKernelSize(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 4))