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Default reuse should be None, never False. Correct it in layers and add tests.

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Change: 144903238
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Lukasz Kaiser 2017-01-18 17:14:33 -08:00 committed by TensorFlower Gardener
parent f0a1af4a8b
commit 1ed4e69f7b
6 changed files with 135 additions and 31 deletions
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10
tensorflow/python/layers/convolutional.py
View File

@ -268,7 +268,7 @@ def conv1d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for 1D convolution layer (e.g. temporal convolution).
This layer creates a convolution kernel that is convolved
@ -435,7 +435,7 @@ def conv2d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for the 2D convolution layer.
This layer creates a convolution kernel that is convolved
@ -608,7 +608,7 @@ def conv3d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for the 3D convolution layer.
This layer creates a convolution kernel that is convolved
@ -867,7 +867,7 @@ def separable_conv2d(inputs,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for the depthwise separable 2D convolution layer.
This layer performs a depthwise convolution that acts separately on
@ -1128,7 +1128,7 @@ def conv2d_transpose(inputs,
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Transposed convolution layer (sometimes called Deconvolution).
The need for transposed convolutions generally arises

127
tensorflow/python/layers/convolutional_test.py
View File

@ -18,11 +18,16 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import ops
from tensorflow.python.layers import convolutional as conv_layers
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@ -191,21 +196,45 @@ class ConvTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3], name='conv1')
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d(images, 32, [3, 3], name='conv1', reuse=True)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
def testFunctionalConv2DReuseFromScope(self):
with variable_scope.variable_scope('scope'):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3], name='conv1')
self.assertEqual(len(variables.trainable_variables()), 2)
with variable_scope.variable_scope('scope', reuse=True):
conv_layers.conv2d(images, 32, [3, 3], name='conv1')
self.assertEqual(len(variables.trainable_variables()), 2)
def testFunctionalConv2DInitializerFromScope(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
'scope', initializer=init_ops.ones_initializer()):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3], name='conv1')
weights = variables.trainable_variables()
# Check the names of weights in order.
self.assertTrue('kernel' in weights[0].name)
self.assertTrue('bias' in weights[1].name)
sess.run(variables.global_variables_initializer())
weights = sess.run(weights)
# Check that the kernel weights got initialized to ones (from scope)
self.assertAllClose(weights[0], np.ones((3, 3, 3, 32)))
# Check that the bias still got initialized to zeros.
self.assertAllClose(weights[1], np.zeros((32)))
def testFunctionalConv2DNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 4)
self.assertEqual(len(variables.trainable_variables()), 4)
class SeparableConv2DTest(test.TestCase):
@ -323,22 +352,48 @@ class SeparableConv2DTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
self.assertEqual(len(variables.trainable_variables()), 3)
conv_layers.separable_conv2d(
images, 32, [3, 3], name='sepconv1', reuse=True)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
self.assertEqual(len(variables.trainable_variables()), 3)
def testFunctionalConv2DReuseFromScope(self):
with variable_scope.variable_scope('scope'):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
self.assertEqual(len(variables.trainable_variables()), 3)
with variable_scope.variable_scope('scope', reuse=True):
conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
self.assertEqual(len(variables.trainable_variables()), 3)
def testFunctionalConv2DInitializerFromScope(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
'scope', initializer=init_ops.ones_initializer()):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3], name='sepconv1')
weights = variables.trainable_variables()
# Check the names of weights in order.
self.assertTrue('depthwise_kernel' in weights[0].name)
self.assertTrue('pointwise_kernel' in weights[1].name)
self.assertTrue('bias' in weights[2].name)
sess.run(variables.global_variables_initializer())
weights = sess.run(weights)
# Check that the kernel weights got initialized to ones (from scope)
self.assertAllClose(weights[0], np.ones((3, 3, 3, 1)))
self.assertAllClose(weights[1], np.ones((1, 1, 3, 32)))
# Check that the bias still got initialized to zeros.
self.assertAllClose(weights[2], np.zeros((32)))
def testFunctionalConv2DNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.separable_conv2d(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 3)
self.assertEqual(len(variables.trainable_variables()), 3)
conv_layers.separable_conv2d(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 6)
self.assertEqual(len(variables.trainable_variables()), 6)
def testSeparableConv2DDepthwiseRegularizer(self):
height, width = 7, 9
@ -511,21 +566,45 @@ class Conv2DTransposeTest(test.TestCase):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1', reuse=True)
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
def testFunctionalConv2DTransposeReuseFromScope(self):
with variable_scope.variable_scope('scope'):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
self.assertEqual(len(variables.trainable_variables()), 2)
with variable_scope.variable_scope('scope', reuse=True):
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
self.assertEqual(len(variables.trainable_variables()), 2)
def testFunctionalConv2DTransposeInitializerFromScope(self):
with self.test_session() as sess:
with variable_scope.variable_scope(
'scope', initializer=init_ops.ones_initializer()):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3], name='deconv1')
weights = variables.trainable_variables()
# Check the names of weights in order.
self.assertTrue('kernel' in weights[0].name)
self.assertTrue('bias' in weights[1].name)
sess.run(variables.global_variables_initializer())
weights = sess.run(weights)
# Check that the kernel weights got initialized to ones (from scope)
self.assertAllClose(weights[0], np.ones((3, 3, 32, 3)))
# Check that the bias still got initialized to zeros.
self.assertAllClose(weights[1], np.zeros((32)))
def testFunctionalConv2DTransposeNoReuse(self):
height, width = 7, 9
images = random_ops.random_uniform((5, height, width, 3), seed=1)
conv_layers.conv2d_transpose(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 2)
self.assertEqual(len(variables.trainable_variables()), 2)
conv_layers.conv2d_transpose(images, 32, [3, 3])
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 4)
self.assertEqual(len(variables.trainable_variables()), 4)
if __name__ == '__main__':

2
tensorflow/python/layers/core.py
View File

@ -167,7 +167,7 @@ def dense(
activity_regularizer=None,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for the densely-connected layer.
This layer implements the operation:

10
tensorflow/python/layers/core_test.py
View File

@ -207,6 +207,16 @@ class DenseTest(test.TestCase):
vars2 = variables.trainable_variables()
self.assertEqual(vars1, vars2)
def testFunctionalDenseTwiceReuseFromScope(self):
with variable_scope.variable_scope('scope'):
inputs = random_ops.random_uniform((5, 3), seed=1)
core_layers.dense(inputs, 2, name='my_dense')
vars1 = variables.trainable_variables()
with variable_scope.variable_scope('scope', reuse=True):
core_layers.dense(inputs, 2, name='my_dense')
vars2 = variables.trainable_variables()
self.assertEqual(vars1, vars2)
def testFunctionalDenseInitializerFromScope(self):
with self.test_session() as sess:
with variable_scope.variable_scope(

2
tensorflow/python/layers/normalization.py
View File

@ -257,7 +257,7 @@ def batch_normalization(inputs,
training=False,
trainable=True,
name=None,
reuse=False):
reuse=None):
"""Functional interface for the batch normalization layer.
Reference: http://arxiv.org/abs/1502.03167

15
tensorflow/python/layers/normalization_test.py
View File

@ -26,6 +26,7 @@ from tensorflow.python.layers import normalization as normalization_layers
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
@ -454,6 +455,20 @@ class BNTest(test.TestCase):
self.assertAlmostEqual(np.mean(normed_np_output), 0., places=2)
self.assertAlmostEqual(np.std(normed_np_output), 1., places=1)
def testFunctionalReuseFromScope(self):
inputs = variables.Variable(
np.random.random((5, 4, 3, 6)), dtype=dtypes.float32)
epsilon = 1e-3
training = array_ops.placeholder(dtype='bool')
with variable_scope.variable_scope('scope'):
_ = normalization_layers.batch_norm(
inputs, axis=-1, momentum=0.9, epsilon=epsilon, training=training)
self.assertEqual(len(variables.global_variables()), 5)
with variable_scope.variable_scope('scope', reuse=True):
_ = normalization_layers.batch_norm(
inputs, axis=-1, momentum=0.9, epsilon=epsilon, training=training)
self.assertEqual(len(variables.global_variables()), 5)
def testNoCenter(self):
bn = normalization_layers.BatchNormalization(axis=1, center=False)
inputs = random_ops.random_uniform((5, 4, 3), seed=1)