196 lines
7.3 KiB
Python
196 lines
7.3 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
|
|
"""Adagrad for TensorFlow."""
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import gen_array_ops
|
|
from tensorflow.python.ops import init_ops
|
|
from tensorflow.python.ops import math_ops
|
|
from tensorflow.python.training import optimizer
|
|
from tensorflow.python.training import training_ops
|
|
from tensorflow.python.util.tf_export import tf_export
|
|
|
|
|
|
@tf_export(v1=["train.AdagradOptimizer"])
|
|
class AdagradOptimizer(optimizer.Optimizer):
|
|
"""Optimizer that implements the Adagrad algorithm.
|
|
|
|
References:
|
|
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
|
|
:[Duchi et al., 2011](http://jmlr.org/papers/v12/duchi11a.html)
|
|
([pdf](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf))
|
|
|
|
@compatibility(TF2)
|
|
tf.compat.v1.train.AdagradOptimizer is compatible with eager mode and
|
|
`tf.function`.
|
|
When eager execution is enabled, `learning_rate`,
|
|
`initial_accumulator_value`, and `epsilon` can each be a callable that
|
|
takes no arguments and returns the actual value to use. This can be useful
|
|
for changing these values across different invocations of optimizer
|
|
functions.
|
|
|
|
To switch to native TF2 style, use [`tf.keras.optimizers.Adagrad`]
|
|
(https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adagrad)
|
|
instead. Please notice that due to the implementation differences,
|
|
`tf.keras.optimizers.Adagrad` and
|
|
`tf.compat.v1.train.AdagradOptimizer` may have slight differences in
|
|
floating point numerics even though the formula used for the variable
|
|
updates still matches.
|
|
|
|
#### Structural mapping to native TF2
|
|
|
|
Before:
|
|
|
|
```python
|
|
optimizer = tf.compat.v1.train.AdagradOptimizer(
|
|
learning_rate=learning_rate,
|
|
initial_accumulator_value=initial_accumulator_value)
|
|
```
|
|
|
|
After:
|
|
|
|
```python
|
|
optimizer = tf.keras.optimizers.Adagrad(
|
|
learning_rate=learning_rate,
|
|
initial_accumulator_value=initial_accumulator_value,
|
|
epsilon=1e-07)
|
|
```
|
|
|
|
#### How to map arguments
|
|
| TF1 Arg Name | TF2 Arg Name | Note |
|
|
| ------------------ | ------------- | ------------------------------- |
|
|
| `learning_rate` | `learning_rate` | Be careful of setting |
|
|
: : : learning_rate tensor value computed from the global step. :
|
|
: : : In TF1 this was usually meant to imply a dynamic learning rate and :
|
|
: : : would recompute in each step. In TF2 (eager + function) it will :
|
|
: : : treat it as a scalar value that only gets computed once instead of :
|
|
: : : a symbolic placeholder to be computed each time. :
|
|
| `initial_accumulator_value` | `initial_accumulator_value` | The |
|
|
: : : argument can be value of zero in TF2, which is not accepted in TF1.|
|
|
| - | `epsilon` | `epsilon` is become configurable in TF2. The |
|
|
: : : defualt value is changed from 1e-8 to 1e-7 :
|
|
| `use_locking` | - | Not applicable in TF2. |
|
|
|
|
#### Before & after usage example
|
|
Before:
|
|
|
|
```python
|
|
x = tf.Variable([1,2,3], dtype=tf.float32)
|
|
grad = tf.constant([0.1, 0.2, 0.3])
|
|
optimizer = tf.compat.v1.train.AdagradOptimizer(learning_rate=0.001)
|
|
optimizer.apply_gradients(zip([grad], [x]))
|
|
```
|
|
|
|
After:
|
|
|
|
```python
|
|
x = tf.Variable([1,2,3], dtype=tf.float32)
|
|
grad = tf.constant([0.1, 0.2, 0.3])
|
|
optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.001)
|
|
optimizer.apply_gradients(zip([grad], [x]))
|
|
```
|
|
|
|
@end_compatibility
|
|
"""
|
|
|
|
def __init__(self, learning_rate, initial_accumulator_value=0.1,
|
|
use_locking=False, name="Adagrad"):
|
|
"""Construct a new Adagrad optimizer.
|
|
|
|
Args:
|
|
learning_rate: A `Tensor` or a floating point value. The learning rate.
|
|
initial_accumulator_value: A floating point value.
|
|
Starting value for the accumulators, must be positive.
|
|
use_locking: If `True` use locks for update operations.
|
|
name: Optional name prefix for the operations created when applying
|
|
gradients. Defaults to "Adagrad".
|
|
|
|
Raises:
|
|
ValueError: If the `initial_accumulator_value` is invalid.
|
|
|
|
"""
|
|
if initial_accumulator_value <= 0.0:
|
|
raise ValueError("initial_accumulator_value must be positive: %s" %
|
|
initial_accumulator_value)
|
|
super(AdagradOptimizer, self).__init__(use_locking, name)
|
|
self._learning_rate = learning_rate
|
|
self._initial_accumulator_value = initial_accumulator_value
|
|
# Created in Initialize.
|
|
self._learning_rate_tensor = None
|
|
|
|
def _create_slots(self, var_list):
|
|
for v in var_list:
|
|
dtype = v.dtype.base_dtype
|
|
if v.get_shape().is_fully_defined():
|
|
init = init_ops.constant_initializer(self._initial_accumulator_value,
|
|
dtype=dtype)
|
|
else:
|
|
init = self._init_constant_op(v, dtype)
|
|
self._get_or_make_slot_with_initializer(v, init, v.get_shape(), dtype,
|
|
"accumulator", self._name)
|
|
|
|
def _init_constant_op(self, v, dtype):
|
|
def init():
|
|
# Use a Tensor instead of initializer if variable does not have
|
|
# static shape.
|
|
init_constant = gen_array_ops.fill(array_ops.shape(v),
|
|
self._initial_accumulator_value)
|
|
return math_ops.cast(init_constant, dtype)
|
|
return init
|
|
|
|
def _prepare(self):
|
|
learning_rate = self._call_if_callable(self._learning_rate)
|
|
self._learning_rate_tensor = ops.convert_to_tensor(
|
|
learning_rate, name="learning_rate")
|
|
|
|
def _apply_dense(self, grad, var):
|
|
acc = self.get_slot(var, "accumulator")
|
|
return training_ops.apply_adagrad(
|
|
var,
|
|
acc,
|
|
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
|
|
grad,
|
|
use_locking=self._use_locking)
|
|
|
|
def _resource_apply_dense(self, grad, var):
|
|
acc = self.get_slot(var, "accumulator")
|
|
return training_ops.resource_apply_adagrad(
|
|
var.handle,
|
|
acc.handle,
|
|
math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
|
|
grad,
|
|
use_locking=self._use_locking)
|
|
|
|
def _apply_sparse(self, grad, var):
|
|
acc = self.get_slot(var, "accumulator")
|
|
return training_ops.sparse_apply_adagrad(
|
|
var,
|
|
acc,
|
|
math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
|
|
grad.values,
|
|
grad.indices,
|
|
use_locking=self._use_locking)
|
|
|
|
def _resource_apply_sparse(self, grad, var, indices):
|
|
acc = self.get_slot(var, "accumulator")
|
|
return training_ops.resource_sparse_apply_adagrad(
|
|
var.handle,
|
|
acc.handle,
|
|
math_ops.cast(self._learning_rate_tensor, grad.dtype),
|
|
grad,
|
|
indices,
|
|
use_locking=self._use_locking)
|