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| # Copyright 2018 Google, Inc. 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. | |
| # ============================================================================== | |
| """Evaluation job. | |
| This sits on the side and performs evaluation on a saved model. | |
| This is a separate process for ease of use and stability of numbers. | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| from learning_unsupervised_learning import utils | |
| def construct_evaluation_graph(theta_process_fn=None, | |
| w_learner_fn=None, | |
| dataset_fn=None, | |
| meta_objectives=None, | |
| ): | |
| """Construct the evaluation graph. | |
| """ | |
| if meta_objectives is None: | |
| meta_objectives = [] | |
| tf.train.create_global_step() | |
| local_device = "" | |
| remote_device = "" | |
| meta_opt = theta_process_fn( | |
| remote_device=remote_device, local_device=local_device) | |
| base_model = w_learner_fn( | |
| remote_device=remote_device, local_device=local_device) | |
| train_dataset = dataset_fn(device=local_device) | |
| # construct variables | |
| x, outputs = base_model(train_dataset()) | |
| initial_state = base_model.initial_state(meta_opt, max_steps=10) | |
| next_state = base_model.compute_next_state(outputs, meta_opt, initial_state) | |
| with utils.state_barrier_context(next_state): | |
| train_one_step_op = meta_opt.assign_state(base_model, next_state) | |
| meta_objs = [] | |
| for meta_obj_fn in meta_objectives: | |
| meta_obj = meta_obj_fn(local_device="", remote_device="") | |
| meta_objs.append(meta_obj) | |
| J = meta_obj(train_dataset, lambda x: base_model(x)[0]) | |
| tf.summary.scalar(str(meta_obj.__class__.__name__)+"_J", tf.reduce_mean(J)) | |
| # TODO(lmetz) this is kinda error prone. | |
| # We should share the construction of the global variables across train and | |
| # make sure both sets of savable variables are the same | |
| checkpoint_vars = meta_opt.remote_variables() + [tf.train.get_global_step()] | |
| for meta_obj in meta_objs: | |
| checkpoint_vars.extend(meta_obj.remote_variables()) | |
| return checkpoint_vars, train_one_step_op, (base_model, train_dataset) | |