304 lines
12 KiB
Python
304 lines
12 KiB
Python
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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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"""Adam for TensorFlow."""
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from tensorflow.python.eager import context
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import gen_training_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import resource_variable_ops
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from tensorflow.python.ops import state_ops
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from tensorflow.python.training import optimizer
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from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=["train.AdamOptimizer"])
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class AdamOptimizer(optimizer.Optimizer):
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"""Optimizer that implements the Adam algorithm.
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References:
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Adam - A Method for Stochastic Optimization:
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[Kingma et al., 2015](https://arxiv.org/abs/1412.6980)
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([pdf](https://arxiv.org/pdf/1412.6980.pdf))
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@compatibility(TF2)
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tf.compat.v1.train.AdamOptimizer is compatible with eager mode and
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`tf.function`.
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When eager execution is enabled, `learning_rate`, `beta1`, `beta2`, and
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`epsilon` can each be a callable that takes no arguments and returns the
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actual value to use. This can be useful for changing these values across
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different invocations of optimizer functions.
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To switch to native TF2 style, use [`tf.keras.optimizers.Adam`]
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(https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam)
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instead. Please notice that due to the implementation differences,
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`tf.keras.optimizers.Adam` and
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`tf.compat.v1.train.AdamOptimizer` may have slight differences in
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floating point numerics even though the formula used for the variable
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updates still matches.
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#### Structural Mapping to Native TF2
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Before:
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```python
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optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001)
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```
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After:
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```python
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optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
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```
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#### How to Map Arguments
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|TF1 Arg Name |TF2 Arg Name |Note |
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|----------------------|-------------|----------------------|
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|learning_rate |learning_rate|Be careful of setting learning_rate as a
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: : : tensor value computed from the global
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: : : step. In TF1 this was usually meant to
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: : : imply a dynamic learning rate and would
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: : : recompute in each step. In TF2 (eager +
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: : : function) it will treat it as a scalar
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: : : value that only gets computed once
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: : : instead of a symbolic placeholder to be
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: : : computed each time. :
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|beta1 |beta_1 | |
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|beta2 |beta_2 | |
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|epsilon |epsilon | Default value is 1e-08 in TF1, but
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: : : 1e-07 in TF2. :
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|use_locking |N/A |Not applicable in TF2. |
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#### Before & After Usage Example
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Before:
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```python
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x = tf.Variable([1,2,3], dtype=tf.float32)
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grad = tf.constant([0.1, 0.2, 0.3])
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optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001)
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optimizer.apply_gradients(zip([grad], [x]))
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```
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After:
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```python
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x = tf.Variable([1,2,3], dtype=tf.float32)
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grad = tf.constant([0.1, 0.2, 0.3])
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optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
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optimizer.apply_gradients(zip([grad], [x]))
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```
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@end_compatibility
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"""
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def __init__(self,
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learning_rate=0.001,
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beta1=0.9,
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beta2=0.999,
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epsilon=1e-8,
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use_locking=False,
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name="Adam"):
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r"""Construct a new Adam optimizer.
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Initialization:
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$$m_0 := 0 \text{(Initialize initial 1st moment vector)}$$
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$$v_0 := 0 \text{(Initialize initial 2nd moment vector)}$$
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$$t := 0 \text{(Initialize timestep)}$$
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The update rule for `variable` with gradient `g` uses an optimization
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described at the end of section 2 of the paper:
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$$t := t + 1$$
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$$\text{lr}_t := \mathrm{learning_rate} *
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\sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$
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$$m_t := \beta_1 * m_{t-1} + (1 - \beta_1) * g$$
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$$v_t := \beta_2 * v_{t-1} + (1 - \beta_2) * g * g$$
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$$\text{variable} := \text{variable} -
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\text{lr}_t * m_t / (\sqrt{v_t} + \epsilon)$$
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The default value of 1e-8 for epsilon might not be a good default in
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general. For example, when training an Inception network on ImageNet a
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current good choice is 1.0 or 0.1. Note that since AdamOptimizer uses the
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formulation just before Section 2.1 of the Kingma and Ba paper rather than
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the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
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hat" in the paper.
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The sparse implementation of this algorithm (used when the gradient is an
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IndexedSlices object, typically because of `tf.gather` or an embedding
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lookup in the forward pass) does apply momentum to variable slices even if
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they were not used in the forward pass (meaning they have a gradient equal
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to zero). Momentum decay (beta1) is also applied to the entire momentum
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accumulator. This means that the sparse behavior is equivalent to the dense
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behavior (in contrast to some momentum implementations which ignore momentum
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unless a variable slice was actually used).
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Args:
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learning_rate: A Tensor or a floating point value. The learning rate.
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beta1: A float value or a constant float tensor. The exponential decay
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rate for the 1st moment estimates.
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beta2: A float value or a constant float tensor. The exponential decay
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rate for the 2nd moment estimates.
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epsilon: A small constant for numerical stability. This epsilon is
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"epsilon hat" in the Kingma and Ba paper (in the formula just before
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Section 2.1), not the epsilon in Algorithm 1 of the paper.
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use_locking: If True use locks for update operations.
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name: Optional name for the operations created when applying gradients.
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Defaults to "Adam".
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"""
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super(AdamOptimizer, self).__init__(use_locking, name)
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self._lr = learning_rate
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self._beta1 = beta1
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self._beta2 = beta2
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self._epsilon = epsilon
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# Tensor versions of the constructor arguments, created in _prepare().
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self._lr_t = None
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self._beta1_t = None
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self._beta2_t = None
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self._epsilon_t = None
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def _get_beta_accumulators(self):
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with ops.init_scope():
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if context.executing_eagerly():
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graph = None
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else:
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graph = ops.get_default_graph()
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return (self._get_non_slot_variable("beta1_power", graph=graph),
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self._get_non_slot_variable("beta2_power", graph=graph))
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def _create_slots(self, var_list):
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# Create the beta1 and beta2 accumulators on the same device as the first
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# variable. Sort the var_list to make sure this device is consistent across
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# workers (these need to go on the same PS, otherwise some updates are
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# silently ignored).
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first_var = min(var_list, key=lambda x: x.name)
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self._create_non_slot_variable(
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initial_value=self._beta1, name="beta1_power", colocate_with=first_var)
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self._create_non_slot_variable(
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initial_value=self._beta2, name="beta2_power", colocate_with=first_var)
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# Create slots for the first and second moments.
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for v in var_list:
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self._zeros_slot(v, "m", self._name)
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self._zeros_slot(v, "v", self._name)
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def _prepare(self):
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lr = self._call_if_callable(self._lr)
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beta1 = self._call_if_callable(self._beta1)
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beta2 = self._call_if_callable(self._beta2)
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epsilon = self._call_if_callable(self._epsilon)
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self._lr_t = ops.convert_to_tensor(lr, name="learning_rate")
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self._beta1_t = ops.convert_to_tensor(beta1, name="beta1")
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self._beta2_t = ops.convert_to_tensor(beta2, name="beta2")
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self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon")
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def _apply_dense(self, grad, var):
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m = self.get_slot(var, "m")
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v = self.get_slot(var, "v")
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beta1_power, beta2_power = self._get_beta_accumulators()
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return gen_training_ops.apply_adam(
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var,
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m,
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v,
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math_ops.cast(beta1_power, var.dtype.base_dtype),
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math_ops.cast(beta2_power, var.dtype.base_dtype),
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math_ops.cast(self._lr_t, var.dtype.base_dtype),
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math_ops.cast(self._beta1_t, var.dtype.base_dtype),
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math_ops.cast(self._beta2_t, var.dtype.base_dtype),
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math_ops.cast(self._epsilon_t, var.dtype.base_dtype),
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grad,
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use_locking=self._use_locking).op
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def _resource_apply_dense(self, grad, var):
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m = self.get_slot(var, "m")
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v = self.get_slot(var, "v")
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beta1_power, beta2_power = self._get_beta_accumulators()
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return gen_training_ops.resource_apply_adam(
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var.handle,
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m.handle,
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v.handle,
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math_ops.cast(beta1_power, grad.dtype.base_dtype),
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math_ops.cast(beta2_power, grad.dtype.base_dtype),
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math_ops.cast(self._lr_t, grad.dtype.base_dtype),
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math_ops.cast(self._beta1_t, grad.dtype.base_dtype),
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math_ops.cast(self._beta2_t, grad.dtype.base_dtype),
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math_ops.cast(self._epsilon_t, grad.dtype.base_dtype),
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grad,
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use_locking=self._use_locking)
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def _apply_sparse_shared(self, grad, var, indices, scatter_add):
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beta1_power, beta2_power = self._get_beta_accumulators()
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beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype)
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beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype)
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lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype)
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beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype)
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beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype)
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epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype)
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lr = (lr_t * math_ops.sqrt(1 - beta2_power) / (1 - beta1_power))
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# m_t = beta1 * m + (1 - beta1) * g_t
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m = self.get_slot(var, "m")
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m_scaled_g_values = grad * (1 - beta1_t)
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m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking)
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with ops.control_dependencies([m_t]):
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m_t = scatter_add(m, indices, m_scaled_g_values)
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# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
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v = self.get_slot(var, "v")
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v_scaled_g_values = (grad * grad) * (1 - beta2_t)
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v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking)
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with ops.control_dependencies([v_t]):
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v_t = scatter_add(v, indices, v_scaled_g_values)
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v_sqrt = math_ops.sqrt(v_t)
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var_update = state_ops.assign_sub(
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var, lr * m_t / (v_sqrt + epsilon_t), use_locking=self._use_locking)
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return control_flow_ops.group(*[var_update, m_t, v_t])
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def _apply_sparse(self, grad, var):
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return self._apply_sparse_shared(
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grad.values,
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var,
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grad.indices,
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lambda x, i, v: state_ops.scatter_add( # pylint: disable=g-long-lambda
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x,
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i,
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v,
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use_locking=self._use_locking))
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def _resource_scatter_add(self, x, i, v):
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with ops.control_dependencies(
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[resource_variable_ops.resource_scatter_add(x.handle, i, v)]):
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return x.value()
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def _resource_apply_sparse(self, grad, var, indices):
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return self._apply_sparse_shared(grad, var, indices,
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self._resource_scatter_add)
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def _finish(self, update_ops, name_scope):
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# Update the power accumulators.
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with ops.control_dependencies(update_ops):
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beta1_power, beta2_power = self._get_beta_accumulators()
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with ops.colocate_with(beta1_power):
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update_beta1 = beta1_power.assign(
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beta1_power * self._beta1_t, use_locking=self._use_locking)
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update_beta2 = beta2_power.assign(
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beta2_power * self._beta2_t, use_locking=self._use_locking)
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return control_flow_ops.group(
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*update_ops + [update_beta1, update_beta2], name=name_scope)
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