Allow callers to control the names of the slot variables.
Other optimizers which have only a single slot variable allow control through the 'name' constructor parameter, but the FtrlOptimizer has two variables. Because they both are created with the same 'name' parameter, one of them has name as a suffix, and the other has name + "_1" as a suffix. This change allows them to be specified in a more controllable way. Change: 152802478
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
75fa9ea1ac
commit
0f7df20b2c
@ -12,7 +12,6 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Ftrl-proximal for TensorFlow."""
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from __future__ import absolute_import
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from __future__ import division
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@ -32,12 +31,16 @@ class FtrlOptimizer(optimizer.Optimizer):
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https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf).
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"""
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def __init__(self, learning_rate,
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def __init__(self,
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learning_rate,
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learning_rate_power=-0.5,
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initial_accumulator_value=0.1,
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l1_regularization_strength=0.0,
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l2_regularization_strength=0.0,
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use_locking=False, name="Ftrl"):
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use_locking=False,
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name="Ftrl",
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accum_name=None,
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linear_name=None):
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"""Construct a new FTRL optimizer.
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Args:
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@ -52,6 +55,10 @@ class FtrlOptimizer(optimizer.Optimizer):
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use_locking: If `True` use locks for update operations.
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name: Optional name prefix for the operations created when applying
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gradients. Defaults to "Ftrl".
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accum_name: The suffix for the variable that keeps the gradient squared
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accumulator. If not present, defaults to name.
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linear_name: The suffix for the variable that keeps the linear gradient
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accumulator. If not present, defaults to name + "_1".
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Raises:
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ValueError: If one of the arguments is invalid.
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@ -82,35 +89,36 @@ class FtrlOptimizer(optimizer.Optimizer):
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self._learning_rate_power_tensor = None
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self._l1_regularization_strength_tensor = None
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self._l2_regularization_strength_tensor = None
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self._accum_name = accum_name
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self._linear_name = linear_name
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def _create_slots(self, var_list):
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# Create the "accum" and "linear" slots.
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for v in var_list:
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with ops.colocate_with(v):
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val = constant_op.constant(self._initial_accumulator_value,
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dtype=v.dtype, shape=v.get_shape())
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self._get_or_make_slot(v, val, "accum", self._name)
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self._zeros_slot(v, "linear", self._name)
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val = constant_op.constant(
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self._initial_accumulator_value, dtype=v.dtype, shape=v.get_shape())
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self._get_or_make_slot(v, val, "accum", self._accum_name or self._name)
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self._zeros_slot(v, "linear", self._linear_name or self._name)
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def _prepare(self):
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self._learning_rate_tensor = ops.convert_to_tensor(
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self._learning_rate,
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name="learning_rate")
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self._learning_rate, name="learning_rate")
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self._l1_regularization_strength_tensor = ops.convert_to_tensor(
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self._l1_regularization_strength,
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name="l1_regularization_strength")
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self._l1_regularization_strength, name="l1_regularization_strength")
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self._l2_regularization_strength_tensor = ops.convert_to_tensor(
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self._l2_regularization_strength,
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name="l2_regularization_strength")
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self._l2_regularization_strength, name="l2_regularization_strength")
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self._learning_rate_power_tensor = ops.convert_to_tensor(
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self._learning_rate_power,
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name="learning_rate_power")
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self._learning_rate_power, name="learning_rate_power")
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def _apply_dense(self, grad, var):
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accum = self.get_slot(var, "accum")
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linear = self.get_slot(var, "linear")
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return training_ops.apply_ftrl(
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var, accum, linear, grad,
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var,
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accum,
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linear,
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grad,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength_tensor,
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var.dtype.base_dtype),
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@ -123,7 +131,10 @@ class FtrlOptimizer(optimizer.Optimizer):
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accum = self.get_slot(var, "accum")
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linear = self.get_slot(var, "linear")
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return training_ops.resource_apply_ftrl(
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var.handle, accum.handle, linear.handle, grad,
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var.handle,
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accum.handle,
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linear.handle,
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grad,
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math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength_tensor,
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grad.dtype.base_dtype),
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@ -136,7 +147,11 @@ class FtrlOptimizer(optimizer.Optimizer):
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accum = self.get_slot(var, "accum")
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linear = self.get_slot(var, "linear")
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return training_ops.sparse_apply_ftrl(
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var, accum, linear, grad.values, grad.indices,
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var,
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accum,
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linear,
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grad.values,
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grad.indices,
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math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
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math_ops.cast(self._l1_regularization_strength_tensor,
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var.dtype.base_dtype),
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@ -149,11 +164,13 @@ class FtrlOptimizer(optimizer.Optimizer):
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accum = self.get_slot(var, "accum")
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linear = self.get_slot(var, "linear")
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return training_ops.resource_sparse_apply_ftrl(
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var.handle, accum.handle, linear.handle, grad, indices,
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var.handle,
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accum.handle,
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linear.handle,
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grad,
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indices,
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math_ops.cast(self._learning_rate_tensor, grad.dtype),
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math_ops.cast(self._l1_regularization_strength_tensor,
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grad.dtype),
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math_ops.cast(self._l2_regularization_strength_tensor,
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grad.dtype),
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math_ops.cast(self._l1_regularization_strength_tensor, grad.dtype),
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math_ops.cast(self._l2_regularization_strength_tensor, grad.dtype),
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math_ops.cast(self._learning_rate_power_tensor, grad.dtype),
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use_locking=self._use_locking)
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@ -17,7 +17,7 @@ tf_class {
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}
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member_method {
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name: "__init__"
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argspec: "args=[\'self\', \'learning_rate\', \'learning_rate_power\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\'], varargs=None, keywords=None, defaults=[\'-0.5\', \'0.1\', \'0.0\', \'0.0\', \'False\', \'Ftrl\'], "
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argspec: "args=[\'self\', \'learning_rate\', \'learning_rate_power\', \'initial_accumulator_value\', \'l1_regularization_strength\', \'l2_regularization_strength\', \'use_locking\', \'name\', \'accum_name\', \'linear_name\'], varargs=None, keywords=None, defaults=[\'-0.5\', \'0.1\', \'0.0\', \'0.0\', \'False\', \'Ftrl\', \'None\', \'None\'], "
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}
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member_method {
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name: "apply_gradients"
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