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Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/optimizers/legacy/adam.py
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2023-06-19 00:49:18 +02:00

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# Copyright 2020 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.
# ==============================================================================
"""Adam optimizer implementation."""
import tensorflow.compat.v2 as tf
from keras import backend_config
from keras.optimizers.legacy import optimizer_v2
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export(
"keras.optimizers.legacy.Adam",
v1=["keras.optimizers.Adam", "keras.optimizers.legacy.Adam"],
)
class Adam(optimizer_v2.OptimizerV2):
r"""Optimizer that implements the Adam algorithm.
Adam optimization is a stochastic gradient descent method that is based on
adaptive estimation of first-order and second-order moments.
According to
[Kingma et al., 2014](http://arxiv.org/abs/1412.6980),
the method is "*computationally
efficient, has little memory requirement, invariant to diagonal rescaling of
gradients, and is well suited for problems that are large in terms of
data/parameters*".
Args:
learning_rate: A `Tensor`, floating point value, or a schedule that is a
`tf.keras.optimizers.schedules.LearningRateSchedule`, or a callable
that takes no arguments and returns the actual value to use, The
learning rate. Defaults to 0.001.
beta_1: A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use. The
exponential decay rate for the 1st moment estimates. Defaults to 0.9.
beta_2: A float value or a constant float tensor, or a callable
that takes no arguments and returns the actual value to use, The
exponential decay rate for the 2nd moment estimates. Defaults to 0.999.
epsilon: A small constant for numerical stability. This epsilon is
"epsilon hat" in the Kingma and Ba paper (in the formula just before
Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults to
1e-7.
amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm from
the paper "On the Convergence of Adam and beyond". Defaults to `False`.
name: Optional name for the operations created when applying gradients.
Defaults to `"Adam"`.
**kwargs: keyword arguments. Allowed arguments are `clipvalue`,
`clipnorm`, `global_clipnorm`.
If `clipvalue` (float) is set, the gradient of each weight
is clipped to be no higher than this value.
If `clipnorm` (float) is set, the gradient of each weight
is individually clipped so that its norm is no higher than this value.
If `global_clipnorm` (float) is set the gradient of all weights is
clipped so that their global norm is no higher than this value.
Usage:
>>> opt = tf.keras.optimizers.legacy.Adam(learning_rate=0.1)
>>> var1 = tf.Variable(10.0)
>>> loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1
>>> step_count = opt.minimize(loss, [var1]).numpy()
>>> # The first step is `-learning_rate*sign(grad)`
>>> var1.numpy()
9.9
Reference:
- [Kingma et al., 2014](http://arxiv.org/abs/1412.6980)
- [Reddi et al., 2018](
https://openreview.net/pdf?id=ryQu7f-RZ) for `amsgrad`.
Notes:
The default value of 1e-7 for epsilon might not be a good default in
general. For example, when training an Inception network on ImageNet a
current good choice is 1.0 or 0.1. Note that since Adam uses the
formulation just before Section 2.1 of the Kingma and Ba paper rather than
the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
hat" in the paper.
The sparse implementation of this algorithm (used when the gradient is an
IndexedSlices object, typically because of `tf.gather` or an embedding
lookup in the forward pass) does apply momentum to variable slices even if
they were not used in the forward pass (meaning they have a gradient equal
to zero). Momentum decay (beta1) is also applied to the entire momentum
accumulator. This means that the sparse behavior is equivalent to the dense
behavior (in contrast to some momentum implementations which ignore momentum
unless a variable slice was actually used).
"""
_HAS_AGGREGATE_GRAD = True
def __init__(
self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
amsgrad=False,
name="Adam",
**kwargs
):
super().__init__(name, **kwargs)
self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
self._set_hyper("decay", self._initial_decay)
self._set_hyper("beta_1", beta_1)
self._set_hyper("beta_2", beta_2)
self.epsilon = epsilon or backend_config.epsilon()
self.amsgrad = amsgrad
def _create_slots(self, var_list):
# Create slots for the first and second moments.
# Separate for-loops to respect the ordering of slot variables from v1.
for var in var_list:
self.add_slot(var, "m")
for var in var_list:
self.add_slot(var, "v")
if self.amsgrad:
for var in var_list:
self.add_slot(var, "vhat")
def _prepare_local(self, var_device, var_dtype, apply_state):
super()._prepare_local(var_device, var_dtype, apply_state)
local_step = tf.cast(self.iterations + 1, var_dtype)
beta_1_t = tf.identity(self._get_hyper("beta_1", var_dtype))
beta_2_t = tf.identity(self._get_hyper("beta_2", var_dtype))
beta_1_power = tf.pow(beta_1_t, local_step)
beta_2_power = tf.pow(beta_2_t, local_step)
lr = apply_state[(var_device, var_dtype)]["lr_t"] * (
tf.sqrt(1 - beta_2_power) / (1 - beta_1_power)
)
apply_state[(var_device, var_dtype)].update(
dict(
lr=lr,
epsilon=tf.convert_to_tensor(self.epsilon, var_dtype),
beta_1_t=beta_1_t,
beta_1_power=beta_1_power,
one_minus_beta_1_t=1 - beta_1_t,
beta_2_t=beta_2_t,
beta_2_power=beta_2_power,
one_minus_beta_2_t=1 - beta_2_t,
)
)
def set_weights(self, weights):
params = self.weights
# If the weights are generated by Keras V1 optimizer, it includes vhats
# even without amsgrad, i.e, V1 optimizer has 3x + 1 variables, while V2
# optimizer has 2x + 1 variables. Filter vhats out for compatibility.
num_vars = int((len(params) - 1) / 2)
if len(weights) == 3 * num_vars + 1:
weights = weights[: len(params)]
super().set_weights(weights)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (apply_state or {}).get(
(var_device, var_dtype)
) or self._fallback_apply_state(var_device, var_dtype)
m = self.get_slot(var, "m")
v = self.get_slot(var, "v")
if not self.amsgrad:
return tf.raw_ops.ResourceApplyAdam(
var=var.handle,
m=m.handle,
v=v.handle,
beta1_power=coefficients["beta_1_power"],
beta2_power=coefficients["beta_2_power"],
lr=coefficients["lr_t"],
beta1=coefficients["beta_1_t"],
beta2=coefficients["beta_2_t"],
epsilon=coefficients["epsilon"],
grad=grad,
use_locking=self._use_locking,
)
else:
vhat = self.get_slot(var, "vhat")
return tf.raw_ops.ResourceApplyAdamWithAmsgrad(
var=var.handle,
m=m.handle,
v=v.handle,
vhat=vhat.handle,
beta1_power=coefficients["beta_1_power"],
beta2_power=coefficients["beta_2_power"],
lr=coefficients["lr_t"],
beta1=coefficients["beta_1_t"],
beta2=coefficients["beta_2_t"],
epsilon=coefficients["epsilon"],
grad=grad,
use_locking=self._use_locking,
)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (apply_state or {}).get(
(var_device, var_dtype)
) or self._fallback_apply_state(var_device, var_dtype)
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * coefficients["one_minus_beta_1_t"]
m_t = tf.compat.v1.assign(
m, m * coefficients["beta_1_t"], use_locking=self._use_locking
)
with tf.control_dependencies([m_t]):
m_t = self._resource_scatter_add(m, indices, m_scaled_g_values)
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * coefficients["one_minus_beta_2_t"]
v_t = tf.compat.v1.assign(
v, v * coefficients["beta_2_t"], use_locking=self._use_locking
)
with tf.control_dependencies([v_t]):
v_t = self._resource_scatter_add(v, indices, v_scaled_g_values)
if not self.amsgrad:
v_sqrt = tf.sqrt(v_t)
var_update = tf.compat.v1.assign_sub(
var,
coefficients["lr"] * m_t / (v_sqrt + coefficients["epsilon"]),
use_locking=self._use_locking,
)
return tf.group(*[var_update, m_t, v_t])
else:
v_hat = self.get_slot(var, "vhat")
v_hat_t = tf.maximum(v_hat, v_t)
with tf.control_dependencies([v_hat_t]):
v_hat_t = tf.compat.v1.assign(
v_hat, v_hat_t, use_locking=self._use_locking
)
v_hat_sqrt = tf.sqrt(v_hat_t)
var_update = tf.compat.v1.assign_sub(
var,
coefficients["lr"]
* m_t
/ (v_hat_sqrt + coefficients["epsilon"]),
use_locking=self._use_locking,
)
return tf.group(*[var_update, m_t, v_t, v_hat_t])
def get_config(self):
config = super().get_config()
config.update(
{
"learning_rate": self._serialize_hyperparameter(
"learning_rate"
),
"decay": self._initial_decay,
"beta_1": self._serialize_hyperparameter("beta_1"),
"beta_2": self._serialize_hyperparameter("beta_2"),
"epsilon": self.epsilon,
"amsgrad": self.amsgrad,
}
)
return config
class NonFusedAdam(optimizer_v2.OptimizerV2):
r"""Optimizer that implements the Adam algorithm without fused kernels.
Adam optimization is a stochastic gradient descent method that is based on
adaptive estimation of first-order and second-order moments.
According to the paper
[Adam: A Method for Stochastic Optimization. Kingma et al.,
2014](http://arxiv.org/abs/1412.6980), the method is "*computationally
efficient, has little memory requirement, invariant to diagonal rescaling of
gradients, and is well suited for problems that are large in terms of
data/parameters*".
For AMSGrad see [On The Convergence Of Adam And Beyond.
Reddi et al., 5-8](https://openreview.net/pdf?id=ryQu7f-RZ).
**If amsgrad = False**:
initialize $m_0$ as 1st moment vector
initialize $v_0$ as 2nd moment vector
The update rule for $\theta$ with gradient $g$ uses an optimization
described at the end of section 2 of the paper:
$$lr_t = \mathrm{learning\_rate} *
\sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$
$$m_t = \beta_1 * m_{t-1} + (1 - \beta_1) * g$$
$$v_t = \beta_2 * v_{t-1} + (1 - \beta_2) * g^2$$
$$\theta_t = \theta_{t-1} - lr_t * m_t / (\sqrt{v_t} + \epsilon)$$
**If amsgrad = True**:
initialize $m_0$ as 1st moment vector
initialize $v_0$ as 2nd moment vector
initialize $\hat{v}_0$ as 2nd moment vector
The update rule for $\theta$ with gradient $g$ uses an optimization
described at the end of section 2 of the paper:
$$lr_t = \mathrm{learning\_rate} *
\sqrt{1 - \beta_2^t} / (1 - \beta_1^t)$$
$$m_t = \beta_1 * m_{t-1} + (1 - \beta_1) * g$$
$$v_t = \beta_2 * v_{t-1} + (1 - \beta_2) * g^2$$
$$\hat{v}_t = \max(\hat{v}_{t-1}, v_t)$$
$$\theta_t = \theta_{t-1} - lr_t * m_t / (\sqrt{\hat{v}_t} + \epsilon)$$
The default value of 1e-7 for epsilon might not be a good default in
general. For example, when training an Inception network on ImageNet a
current good choice is 1.0 or 0.1. Note that since Adam uses the
formulation just before Section 2.1 of the Kingma and Ba paper rather than
the formulation in Algorithm 1, the "epsilon" referred to here is "epsilon
hat" in the paper.
The sparse implementation of this algorithm (used when the gradient is an
IndexedSlices object, typically because of `tf.gather` or an embedding
lookup in the forward pass) does apply momentum to variable slices even if
they were not used in the forward pass (meaning they have a gradient equal
to zero). Momentum decay (beta1) is also applied to the entire momentum
accumulator. This means that the sparse behavior is equivalent to the dense
behavior (in contrast to some momentum implementations which ignore momentum
unless a variable slice was actually used).
Usage:
>>> opt = tf.keras.optimizers.legacy.Adam(learning_rate=0.1)
>>> var1 = tf.Variable(10.0)
>>> loss = lambda: (var1 ** 2)/2.0 # d(loss)/d(var1) == var1
>>> step_count = opt.minimize(loss, [var1]).numpy()
>>> # The first step is `-learning_rate*sign(grad)`
>>> var1.numpy()
9.9
"""
_HAS_AGGREGATE_GRAD = True
def __init__(
self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-7,
amsgrad=False,
name="Adam",
**kwargs
):
"""Construct a new Adam optimizer.
Args:
learning_rate: A `Tensor`, floating point value, or a schedule that is
a `tf.keras.optimizers.schedules.LearningRateSchedule`, or a
callable that takes no arguments and returns the actual value to
use, The learning rate. Defaults to 0.001.
beta_1: A float value or a constant float tensor, or a callable that
takes no arguments and returns the actual value to use. The
exponential decay rate for the 1st moment estimates. Defaults to
0.9.
beta_2: A float value or a constant float tensor, or a callable that
takes no arguments and returns the actual value to use, The
exponential decay rate for the 2nd moment estimates. Defaults to
0.999.
epsilon: A small constant for numerical stability. This epsilon is
"epsilon hat" in the Kingma and Ba paper (in the formula just before
Section 2.1), not the epsilon in Algorithm 1 of the paper. Defaults
to 1e-7.
amsgrad: Boolean. Whether to apply AMSGrad variant of this algorithm
from the paper "On the Convergence of Adam and beyond". Defaults to
`False`.
name: Optional name for the operations created when applying
gradients. Defaults to "Adam".
**kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`,
`lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is
clip gradients by value, `decay` is included for backward
compatibility to allow time inverse decay of learning rate. `lr` is
included for backward compatibility, recommended to use
`learning_rate` instead.
"""
super().__init__(name, **kwargs)
self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
self._set_hyper("decay", self._initial_decay)
self._set_hyper("beta_1", beta_1)
self._set_hyper("beta_2", beta_2)
self.epsilon = epsilon or backend_config.epsilon()
self.amsgrad = amsgrad
def _create_slots(self, var_list):
# Create slots for the first and second moments.
# Separate for-loops to respect the ordering of slot variables from v1.
for var in var_list:
self.add_slot(var, "m")
for var in var_list:
self.add_slot(var, "v")
if self.amsgrad:
for var in var_list:
self.add_slot(var, "vhat")
def _prepare_local(self, var_device, var_dtype, apply_state):
super()._prepare_local(var_device, var_dtype, apply_state)
local_step = tf.cast(self.iterations + 1, var_dtype)
beta_1_t = tf.identity(self._get_hyper("beta_1", var_dtype))
beta_2_t = tf.identity(self._get_hyper("beta_2", var_dtype))
beta_1_power = tf.pow(beta_1_t, local_step)
beta_2_power = tf.pow(beta_2_t, local_step)
lr = apply_state[(var_device, var_dtype)]["lr_t"] * (
tf.sqrt(1 - beta_2_power) / (1 - beta_1_power)
)
apply_state[(var_device, var_dtype)].update(
dict(
lr=lr,
epsilon=tf.convert_to_tensor(self.epsilon, var_dtype),
beta_1_t=beta_1_t,
beta_1_power=beta_1_power,
one_minus_beta_1_t=1 - beta_1_t,
beta_2_t=beta_2_t,
beta_2_power=beta_2_power,
one_minus_beta_2_t=1 - beta_2_t,
)
)
def set_weights(self, weights):
params = self.weights
# If the weights are generated by Keras V1 optimizer, it includes vhats
# even without amsgrad, i.e, V1 optimizer has 3x + 1 variables, while V2
# optimizer has 2x + 1 variables. Filter vhats out for compatibility.
num_vars = int((len(params) - 1) / 2)
if len(weights) == 3 * num_vars + 1:
weights = weights[: len(params)]
super().set_weights(weights)
@tf.function(jit_compile=True)
def _resource_apply_dense(self, grad, var, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (apply_state or {}).get(
(var_device, var_dtype)
) or self._fallback_apply_state(var_device, var_dtype)
m = self.get_slot(var, "m")
v = self.get_slot(var, "v")
alpha = (
coefficients["lr_t"]
* tf.sqrt(1 - coefficients["beta_2_power"])
/ (1 - coefficients["beta_1_power"])
)
m.assign_add((grad - m) * (1 - coefficients["beta_1_t"]))
v.assign_add((tf.square(grad) - v) * (1 - coefficients["beta_2_t"]))
if self.amsgrad:
vhat = self.get_slot(var, "vhat")
vhat.assign(tf.maximum(vhat, v))
v = vhat
var.assign_sub((m * alpha) / (tf.sqrt(v) - coefficients["epsilon"]))
@tf.function(jit_compile=True)
def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
var_device, var_dtype = var.device, var.dtype.base_dtype
coefficients = (apply_state or {}).get(
(var_device, var_dtype)
) or self._fallback_apply_state(var_device, var_dtype)
# m_t = beta1 * m + (1 - beta1) * g_t
m = self.get_slot(var, "m")
m_scaled_g_values = grad * coefficients["one_minus_beta_1_t"]
m.assign(m * coefficients["beta_1_t"])
m.scatter_add(tf.IndexedSlices(m_scaled_g_values, indices))
# v_t = beta2 * v + (1 - beta2) * (g_t * g_t)
v = self.get_slot(var, "v")
v_scaled_g_values = (grad * grad) * coefficients["one_minus_beta_2_t"]
v.assign(v * coefficients["beta_2_t"])
v.scatter_add(tf.IndexedSlices(v_scaled_g_values, indices))
if not self.amsgrad:
var.assign_sub(
coefficients["lr"] * m / (tf.sqrt(v) + coefficients["epsilon"])
)
else:
v_hat = self.get_slot(var, "vhat")
v_hat.assign(tf.maximum(v_hat, v))
var.assign_sub(
coefficients["lr"]
* m
/ (tf.sqrt(v_hat) + coefficients["epsilon"])
)
def get_config(self):
config = super().get_config()
config.update(
{
"learning_rate": self._serialize_hyperparameter(
"learning_rate"
),
"decay": self._initial_decay,
"beta_1": self._serialize_hyperparameter("beta_1"),
"beta_2": self._serialize_hyperparameter("beta_2"),
"epsilon": self.epsilon,
"amsgrad": self.amsgrad,
}
)
return config
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