def losses(self):
"""Retrieve the network's losses.
Will only include losses that are either
unconditional, or conditional on inputs to this model
(e.g. will not include losses that depend on tensors
that aren't inputs to this model).
Returns:
A list of loss tensors.
"""
losses = []
for layer in self.layers:
losses += layer.losses
if context.in_eager_mode():
return losses
relevant_inputs = self.inputs or []
for i in range(1, len(self._inbound_nodes)):
inputs = self.get_input_at(i)
if isinstance(inputs, list):
relevant_inputs += inputs
else:
relevant_inputs.append(inputs)
reachable = layers_util.get_reachable_from_inputs(relevant_inputs, losses)
relevant_conditional_losses = [x for x in losses if x in reachable]
unconditional_losses = [
x for x in losses if x._unconditional_loss] # pylint: disable=protected-access
return list(set(
relevant_conditional_losses + unconditional_losses + self._losses))
def losses(self):
"""Retrieve the network's losses.
Will only include losses that are either
unconditional, or conditional on inputs to this model
(e.g. will not include losses that depend on tensors
that aren't inputs to this model).
Returns:
A list of loss tensors.
"""
losses = []
if context.in_eager_mode():
for layer in self.layers:
losses += layer.losses
return losses
for layer in self.layers:
losses += layer.losses
relevant_inputs = []
for i in range(len(self._inbound_nodes)):
inputs = self.get_input_at(i)
if isinstance(inputs, list):
relevant_inputs += inputs
else:
relevant_inputs.append(inputs)
reachable = layers_util.get_reachable_from_inputs(relevant_inputs, losses)
relevant_conditional_losses = [x for x in losses if x in reachable]
unconditional_losses = [
x for x in losses if x._unconditional_loss] # pylint: disable=protected-access
return list(set(
relevant_conditional_losses + unconditional_losses + self._losses))
def testGetReachableFromInputs(self):
pl_1 = array_ops.placeholder(shape=None, dtype='float32')
pl_2 = array_ops.placeholder(shape=None, dtype='float32')
pl_3 = array_ops.placeholder(shape=None, dtype='float32')
x_1 = pl_1 + pl_2
x_2 = pl_2 * 2
x_3 = pl_3 + 1
x_4 = x_1 + x_2
x_5 = x_3 * pl_1
self.assertEqual({pl_1, x_1, x_4, x_5},
utils.get_reachable_from_inputs([pl_1]))
self.assertEqual({pl_1, pl_2, x_1, x_2, x_4, x_5},
utils.get_reachable_from_inputs([pl_1, pl_2]))
self.assertEqual({pl_3, x_3, x_5}, utils.get_reachable_from_inputs([pl_3]))
self.assertEqual({x_3, x_5}, utils.get_reachable_from_inputs([x_3]))
def testGetReachableFromInputs(self):
pl_1 = array_ops.placeholder(shape=None, dtype='float32')
pl_2 = array_ops.placeholder(shape=None, dtype='float32')
pl_3 = array_ops.placeholder(shape=None, dtype='float32')
x_1 = pl_1 + pl_2
x_2 = pl_2 * 2
x_3 = pl_3 + 1
x_4 = x_1 + x_2
x_5 = x_3 * pl_1
self.assertEqual({pl_1, x_1, x_4, x_5},
utils.get_reachable_from_inputs([pl_1]))
self.assertEqual({pl_1, pl_2, x_1, x_2, x_4, x_5},
utils.get_reachable_from_inputs([pl_1, pl_2]))
self.assertEqual({pl_3, x_3, x_5},
utils.get_reachable_from_inputs([pl_3]))
self.assertEqual({x_3, x_5}, utils.get_reachable_from_inputs([x_3]))
def updates(self):
"""Retrieve the network's updates.
Will only include updates that are either
unconditional, or conditional on inputs to this model
(e.g. will not include updates that were created by layers of this model
outside of the model).
Effectively, `network.updates` behaves like `layer.updates`.
Concrete example:
```python
bn = keras.layers.BatchNormalization()
x1 = keras.layers.Input(shape=(10,))
_ = bn(x1) # This creates 2 updates.
x2 = keras.layers.Input(shape=(10,))
y2 = bn(x2) # This creates 2 more updates.
# The BN layer has now 4 updates.
self.assertEqual(len(bn.updates), 4)
# Let's create a model from x2 to y2.
model = keras.models.Model(x2, y2)
# The model does not list all updates from its underlying layers,
# but only the updates that are relevant to it. Updates created by layers
# outside of the model are discarded.
self.assertEqual(len(model.updates), 2)
# If you keep calling the model, you append to its updates, just like
# what happens for a layer.
x3 = keras.layers.Input(shape=(10,))
y3 = model(x3)
self.assertEqual(len(model.updates), 4)
# But if you call the inner BN layer independently, you don't affect
# the model's updates.
x4 = keras.layers.Input(shape=(10,))
_ = bn(x4)
self.assertEqual(len(model.updates), 4)
```
Returns:
A list of update ops.
"""
if context.in_eager_mode():
return []
if not self.trainable and not self.stateful:
return []
updates = []
for layer in self.layers:
updates += layer.updates
# `updates` might contain irrelevant updates, so it needs to be filtered
# with respect to inputs the model has been called on.
relevant_inputs = self.inputs or []
for i in range(1, len(self._inbound_nodes)):
inputs = self.get_input_at(i)
if isinstance(inputs, list):
relevant_inputs += inputs
else:
relevant_inputs.append(inputs)
reachable = layers_util.get_reachable_from_inputs(relevant_inputs, updates)
relevant_conditional_updates = [x for x in updates if x in reachable]
unconditional_updates = [
x for x in updates if x._unconditional_update] # pylint: disable=protected-access
# A layer could be used multiple times in a nested structure,
# so the updates list must be de-duped.
return list(set(
relevant_conditional_updates + unconditional_updates + self._updates))
def updates(self):
"""Retrieve the network's updates.
Will only include updates that are either
unconditional, or conditional on inputs to this model
(e.g. will not include updates that were created by layers of this model
outside of the model).
Effectively, `network.updates` behaves like `layer.updates`.
Concrete example:
```python
bn = keras.layers.BatchNormalization()
x1 = keras.layers.Input(shape=(10,))
_ = bn(x1) # This creates 2 updates.
x2 = keras.layers.Input(shape=(10,))
y2 = bn(x2) # This creates 2 more updates.
# The BN layer has now 4 updates.
self.assertEqual(len(bn.updates), 4)
# Let's create a model from x2 to y2.
model = keras.models.Model(x2, y2)
# The model does not list all updates from its underlying layers,
# but only the updates that are relevant to it. Updates created by layers
# outside of the model are discarded.
self.assertEqual(len(model.updates), 2)
# If you keep calling the model, you append to its updates, just like
# what happens for a layer.
x3 = keras.layers.Input(shape=(10,))
y3 = model(x3)
self.assertEqual(len(model.updates), 4)
# But if you call the inner BN layer independently, you don't affect
# the model's updates.
x4 = keras.layers.Input(shape=(10,))
_ = bn(x4)
self.assertEqual(len(model.updates), 4)
```
Returns:
A list of update ops.
"""
if not self.trainable and not self.stateful:
return []
updates = []
for layer in self.layers:
updates += layer.updates
# `updates` might contain irrelevant updates, so it needs to be filtered
# with respect to inputs the model has been called on.
relevant_inputs = []
for i in range(len(self._inbound_nodes)):
inputs = self.get_input_at(i)
if isinstance(inputs, list):
relevant_inputs += inputs
else:
relevant_inputs.append(inputs)
reachable = layers_util.get_reachable_from_inputs(relevant_inputs, updates)
relevant_conditional_updates = [x for x in updates if x in reachable]
unconditional_updates = [
x for x in updates if x._unconditional_update] # pylint: disable=protected-access
# A layer could be used multiple times in a nested structure,
# so the updates list must be de-duped.
return list(set(
relevant_conditional_updates + unconditional_updates + self._updates))