<commit_before># Copyright 2017 <NAME> @awav
#
# 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.

import sys
import warnings
import tensorflow as tf

from ..models.model import Model
from . import optimizer


_REGISTERED_TENSORFLOW_OPTIMIZERS = {}


class _TensorFlowOptimizer(optimizer.Optimizer):
    def __init__(self, *args, **kwargs):
        name = self.__class__.__name__
        tf_optimizer = _get_registered_optimizer(name)
        if tf_optimizer is None:
            raise ValueError('Optimizer not found.')
        self._model = None
        super(_TensorFlowOptimizer, self).__init__()
        self._optimizer = tf_optimizer(*args, **kwargs)
        self._minimize_operation = None

    def minimize(self, model, **kwargs):
        if model is None or not isinstance(model, Model):
            raise ValueError('Unknown type passed for optimization.')

        session = self._pop_session(model, kwargs)
        self._model = model

        var_list = self._pop_var_list(model, kwargs)
        self._create_minimize_operation(model, var_list, session, **kwargs)

        feed_dict = self._pop_feed_dict(kwargs)
        maxiter = self._pop_maxiter(kwargs)

        try:
            for _i in range(maxiter):
                session.run(self.minimize_operation, feed_dict=feed_dict)
        except KeyboardInterrupt:
            warnings.warn('Optimization interrupted.')

    def _create_minimize_operation(self, model, var_list, session, **kwargs):
        objective = model.objective
        with session.graph.as_default():
            self._minimize_operation = self.optimizer.minimize(
                objective, var_list=var_list, **kwargs)
            self._initialize_optimizer(var_list, session)

    def _initialize_optimizer(self, var_list, session):
        optimizer_vars = [self.optimizer.get_slot(var, name)
                          for name in self.optimizer.get_slot_names()
                          for var in var_list]
        print(optimizer_vars)
        session.run(tf.variables_initializer(optimizer_vars))

    @property
    def minimize_operation(self):<commit_msg>Address an issue with initializing AdamOptimizer.<commit_after># Copyright 2017 <NAME> @awav
#
# 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.

import sys
import warnings
import tensorflow as tf

from ..models.model import Model
from . import optimizer


_REGISTERED_TENSORFLOW_OPTIMIZERS = {}


class _TensorFlowOptimizer(optimizer.Optimizer):
    def __init__(self, *args, **kwargs):
        name = self.__class__.__name__
        tf_optimizer = _get_registered_optimizer(name)
        if tf_optimizer is None:
            raise ValueError('Optimizer not found.')
        self._model = None
        super(_TensorFlowOptimizer, self).__init__()
        self._optimizer = tf_optimizer(*args, **kwargs)
        self._minimize_operation = None

    def minimize(self, model, **kwargs):
        if model is None or not isinstance(model, Model):
            raise ValueError('Unknown type passed for optimization.')

        session = self._pop_session(model, kwargs)
        self._model = model

        var_list = self._pop_var_list(model, kwargs)
        self._create_minimize_operation(model, var_list, session, **kwargs)

        feed_dict = self._pop_feed_dict(kwargs)
        maxiter = self._pop_maxiter(kwargs)

        try:
            for _i in range(maxiter):
                session.run(self.minimize_operation, feed_dict=feed_dict)
        except KeyboardInterrupt:
            warnings.warn('Optimization interrupted.')

    def _create_minimize_operation(self, model, var_list, session, **kwargs):
        objective = model.objective
        with session.graph.as_default():
            self._minimize_operation = self.optimizer.minimize(
                objective, var_list=var_list, **kwargs)
            self._initialize_optimizer(var_list, session)

    def _initialize_optimizer(self, var_list, session):
        # TODO(awav): AdamOptimizer creates beta1 and beta2 variables which are
        #             not included in slots.
        extra_vars = [v for v in self.optimizer.__dict__.values() if isinstance(v, tf.Variable)]
        optimizer_vars = [self.optimizer.get_slot(var, name)
                          for name in self.optimizer.get_slot_names()
                          for var in var_list]
        var_list = list(set(optimizer_vars + extra_vars))
        session.run(tf.variables_initializer(var_list))

    @property
    def minimize_operation(self):
{'fraction_non_alphanumeric': 0.08208409506398537, 'fraction_numerical': 0.0032906764168190127, 'mean_word_length': 3.002194586686174, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '4997599', 'n_tokens_mistral': 1538, 'n_tokens_neox': 1499, 'n_words': 444}