Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/optimizers/legacy/adagrad.py

# Copyright 2018 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 optimizer implementation."""

import numpy as np
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.Adagrad",
    v1=["keras.optimizers.Adagrad", "keras.optimizers.legacy.Adagrad"],
)
class Adagrad(optimizer_v2.OptimizerV2):
    r"""Optimizer that implements the Adagrad algorithm.

    Adagrad is an optimizer with parameter-specific learning rates,
    which are adapted relative to how frequently a parameter gets
    updated during training. The more updates a parameter receives,
    the smaller the updates.

    Args:
      learning_rate: Initial value for the learning rate:
        either a floating point value,
        or a `tf.keras.optimizers.schedules.LearningRateSchedule` instance.
        Defaults to 0.001.
        Note that `Adagrad` tends to benefit from higher initial learning rate
        values compared to other optimizers.
        To match the exact form in the original paper, use 1.0.
      initial_accumulator_value: Floating point value.
        Starting value for the accumulators (per-parameter momentum values).
        Must be non-negative.
      epsilon: Small floating point value used to maintain numerical stability.
      name: Optional name prefix for the operations created when applying
        gradients.  Defaults to `"Adagrad"`.
      **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..

    Reference:
      - [Duchi et al., 2011](
        http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
    """

    _HAS_AGGREGATE_GRAD = True

    def __init__(
        self,
        learning_rate=0.001,
        initial_accumulator_value=0.1,
        epsilon=1e-7,
        name="Adagrad",
        **kwargs
    ):
        if initial_accumulator_value < 0.0:
            raise ValueError(
                "initial_accumulator_value must be non-negative: %s"
                % initial_accumulator_value
            )
        if epsilon is None:
            epsilon = backend_config.epsilon()
        super().__init__(name, **kwargs)
        self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
        self._set_hyper("decay", self._initial_decay)
        self._initial_accumulator_value = initial_accumulator_value
        self.epsilon = epsilon or backend_config.epsilon()

    def _create_slots(self, var_list):
        for var in var_list:
            dtype = var.dtype.base_dtype
            init = tf.compat.v1.constant_initializer(
                self._initial_accumulator_value, dtype=dtype
            )
            self.add_slot(var, "accumulator", init)

    def _prepare_local(self, var_device, var_dtype, apply_state):
        super()._prepare_local(var_device, var_dtype, apply_state)
        apply_state[(var_device, var_dtype)].update(
            dict(
                epsilon=tf.convert_to_tensor(self.epsilon, var_dtype),
                neg_lr_t=-apply_state[(var_device, var_dtype)]["lr_t"],
                zero=tf.zeros((), dtype=tf.int64),
            )
        )

    def set_weights(self, weights):
        params = self.weights
        # Override set_weights for backward compatibility of Keras V1 optimizer
        # since it does not include iteration at head of the weight list. Set
        # iteration to 0.
        if len(params) == len(weights) + 1:
            weights = [np.array(0)] + weights
        super().set_weights(weights)

    @classmethod
    def from_config(cls, config, custom_objects=None):
        """Creates an optimizer from its config.

        This method is the reverse of `get_config`,
        capable of instantiating the same optimizer from the config
        dictionary.

        Args:
            config: A Python dictionary, typically the output of get_config.
            custom_objects: A Python dictionary mapping names to additional
              Python objects used to create this optimizer, such as a function
              used for a hyperparameter.

        Returns:
            An optimizer instance.
        """
        if "initial_accumulator_value" not in config:
            config["initial_accumulator_value"] = 0.1
        if "lr" in config:
            config["learning_rate"] = config.pop("lr")
        return cls(**config)

    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)

        acc = self.get_slot(var, "accumulator")
        return tf.raw_ops.ResourceApplyAdagradV2(
            var=var.handle,
            accum=acc.handle,
            lr=coefficients["lr_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)

        acc = self.get_slot(var, "accumulator")
        return tf.raw_ops.ResourceSparseApplyAdagradV2(
            var=var.handle,
            accum=acc.handle,
            lr=coefficients["lr_t"],
            epsilon=coefficients["epsilon"],
            grad=grad,
            indices=indices,
            use_locking=self._use_locking,
        )

    def get_config(self):
        config = super().get_config()
        config.update(
            {
                "learning_rate": self._serialize_hyperparameter(
                    "learning_rate"
                ),
                "decay": self._initial_decay,
                "initial_accumulator_value": self._initial_accumulator_value,
                "epsilon": self.epsilon,
            }
        )
        return config