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Apr 30, 2020
Merged

R0.10 #907

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fc64234
Make convolutional layers work with modern Keras.
SiegeLordEx Apr 27, 2020
967443a
Bump version suffix from rc0 to rc1.
emilyfertig Apr 30, 2020
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26 changes: 10 additions & 16 deletions tensorflow_probability/python/layers/conv_variational.py
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Original file line number Diff line number Diff line change
Expand Up @@ -197,7 +197,6 @@ def build(self, input_shape):
self.kernel_prior = self.kernel_prior_fn(
dtype, kernel_shape, 'kernel_prior',
self.trainable, self.add_variable)
self._built_kernel_divergence = False

if self.bias_posterior_fn is None:
self.bias_posterior = None
Expand All @@ -212,7 +211,6 @@ def build(self, input_shape):
self.bias_prior = self.bias_prior_fn(
dtype, (self.filters,), 'bias_prior',
self.trainable, self.add_variable)
self._built_bias_divergence = False

self.input_spec = tf.keras.layers.InputSpec(
ndim=self.rank + 2, axes={channel_axis: input_dim})
Expand All @@ -234,20 +232,16 @@ def call(self, inputs):
outputs = self._apply_variational_bias(outputs)
if self.activation is not None:
outputs = self.activation(outputs)
if not self._built_kernel_divergence:
self._apply_divergence(self.kernel_divergence_fn,
self.kernel_posterior,
self.kernel_prior,
self.kernel_posterior_tensor,
name='divergence_kernel')
self._built_kernel_divergence = True
if not self._built_bias_divergence:
self._apply_divergence(self.bias_divergence_fn,
self.bias_posterior,
self.bias_prior,
self.bias_posterior_tensor,
name='divergence_bias')
self._built_bias_divergence = True
self._apply_divergence(self.kernel_divergence_fn,
self.kernel_posterior,
self.kernel_prior,
self.kernel_posterior_tensor,
name='divergence_kernel')
self._apply_divergence(self.bias_divergence_fn,
self.bias_posterior,
self.bias_prior,
self.bias_posterior_tensor,
name='divergence_bias')
return outputs

def compute_output_shape(self, input_shape):
Expand Down
57 changes: 30 additions & 27 deletions tensorflow_probability/python/layers/conv_variational_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,6 @@
# Dependency imports
import numpy as np

import tensorflow.compat.v1 as tf1
import tensorflow.compat.v2 as tf
import tensorflow_probability as tfp

Expand Down Expand Up @@ -180,12 +179,12 @@ def __call__(self, *args, **kwargs):
@test_util.test_all_tf_execution_regimes
class ConvVariational(object):

def maybe_transpose_inputs(self, inputs):
def maybe_transpose_tensor(self, tensor):
if self.data_format == 'channels_first':
order = channels_last_to_first(list(range(inputs.shape.rank)))
return tf.transpose(a=inputs, perm=order)
order = channels_last_to_first(list(range(tensor.shape.rank)))
return tf.transpose(a=tensor, perm=order)
else:
return inputs
return tensor

def _testKerasLayer(self, layer_class): # pylint: disable=invalid-name
def kernel_posterior_fn(dtype, shape, name, trainable, add_variable_fn):
Expand Down Expand Up @@ -241,7 +240,7 @@ def _testKLPenaltyKernel(self, layer_class): # pylint: disable=invalid-name
elif layer_class in (tfp.layers.Convolution3DReparameterization,
tfp.layers.Convolution3DFlipout):
inputs = tf.random.uniform([2, 3, 3, 3, 1], seed=1)
inputs = self.maybe_transpose_inputs(inputs)
inputs = self.maybe_transpose_tensor(inputs)

# No keys.
input_dependent_losses = layer.get_losses_for(inputs=None)
Expand Down Expand Up @@ -273,7 +272,7 @@ def _testKLPenaltyBoth(self, layer_class): # pylint: disable=invalid-name
elif layer_class in (tfp.layers.Convolution3DReparameterization,
tfp.layers.Convolution3DFlipout):
inputs = tf.random.uniform([2, 3, 3, 3, 1], seed=1)
inputs = self.maybe_transpose_inputs(inputs)
inputs = self.maybe_transpose_tensor(inputs)

# No keys.
input_dependent_losses = layer.get_losses_for(inputs=None)
Expand Down Expand Up @@ -307,7 +306,7 @@ def _testConvSetUp(self, layer_class, batch_size, depth=None,
inputs = tf.random.uniform([batch_size, depth, height, width, channels],
seed=seed())
kernel_size = (2, 2, 2)
inputs = self.maybe_transpose_inputs(inputs)
inputs = self.maybe_transpose_tensor(inputs)

kernel_shape = kernel_size + (channels, filters)
kernel_posterior = MockDistribution(
Expand Down Expand Up @@ -547,7 +546,7 @@ def _testRandomConvFlipout(self, layer_class): # pylint: disable=invalid-name
inputs = tf.random.uniform([batch_size, depth, height, width, channels],
seed=seed())
kernel_size = (2, 2, 2)
inputs = self.maybe_transpose_inputs(inputs)
inputs = self.maybe_transpose_tensor(inputs)

kernel_shape = kernel_size + (channels, filters)
bias_size = (filters,)
Expand Down Expand Up @@ -597,26 +596,30 @@ def _testRandomConvFlipout(self, layer_class): # pylint: disable=invalid-name
np.prod(outputs_one_.shape))

def _testLayerInSequential(self, layer_class): # pylint: disable=invalid-name
with self.cached_session() as sess:
if layer_class in (tfp.layers.Convolution1DReparameterization,
tfp.layers.Convolution1DFlipout):
inputs = tf.random.uniform([2, 3, 1])
elif layer_class in (tfp.layers.Convolution2DReparameterization,
tfp.layers.Convolution2DFlipout):
inputs = tf.random.uniform([2, 3, 3, 1])
elif layer_class in (tfp.layers.Convolution3DReparameterization,
tfp.layers.Convolution3DFlipout):
inputs = tf.random.uniform([2, 3, 3, 3, 1])
inputs = self.maybe_transpose_inputs(inputs)
if layer_class in (tfp.layers.Convolution1DReparameterization,
tfp.layers.Convolution1DFlipout):
inputs = tf.random.uniform([2, 3, 1])
outputs = tf.random.uniform([2, 1, 2])
elif layer_class in (tfp.layers.Convolution2DReparameterization,
tfp.layers.Convolution2DFlipout):
inputs = tf.random.uniform([2, 3, 3, 1])
outputs = tf.random.uniform([2, 1, 1, 2])
elif layer_class in (tfp.layers.Convolution3DReparameterization,
tfp.layers.Convolution3DFlipout):
inputs = tf.random.uniform([2, 3, 3, 3, 1])
outputs = tf.random.uniform([2, 1, 1, 1, 2])
inputs = self.maybe_transpose_tensor(inputs)
outputs = self.maybe_transpose_tensor(outputs)

net = tf.keras.Sequential([
layer_class(filters=2, kernel_size=3, data_format=self.data_format),
layer_class(filters=2, kernel_size=1, data_format=self.data_format)])

net = tf.keras.Sequential([
layer_class(filters=2, kernel_size=3, data_format=self.data_format),
layer_class(filters=2, kernel_size=1, data_format=self.data_format)])
output = net(inputs)
net.compile(loss='mse', optimizer='adam')
net.fit(inputs, outputs, batch_size=2, epochs=3, steps_per_epoch=2)

# Verify that the network runs without errors
sess.run(tf1.global_variables_initializer())
sess.run(output)
batch_output = self.evaluate(net(inputs))
self.assertAllEqual(outputs.shape, batch_output.shape)

def testKerasLayerConvolution1DReparameterization(self):
self._testKerasLayer(tfp.layers.Convolution1DReparameterization)
Expand Down
2 changes: 1 addition & 1 deletion tensorflow_probability/python/version.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
# stable release (indicated by `_VERSION_SUFFIX = ''`). Outside the context of a
# release branch, the current version is by default assumed to be a
# 'development' version, labeled 'dev'.
_VERSION_SUFFIX = 'rc0'
_VERSION_SUFFIX = 'rc1'

# Example, '0.4.0-dev'
__version__ = '.'.join([
Expand Down