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| # 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. | |
| # ============================================================================== | |
| """Defines hyperparameter configuration for ConvolutionalNet models. | |
| Specifically, provides methods for defining and initializing TensorFlow | |
| hyperparameters objects for a convolutional model as defined in: | |
| seq2species.build_model | |
| """ | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import tensorflow as tf | |
| def parse_hparams(hparam_values='', num_filters=1): | |
| """Initializes TensorFlow hyperparameters object with default values. | |
| In addition, default hyperparameter values are overwritten with the specified | |
| ones, where necessary. | |
| Args: | |
| hparam_values: comma-separated string of name=value pairs for setting | |
| particular hyperparameters. | |
| num_filters: int; number of filters in the model. | |
| Must be fixed outside of hyperparameter/study object as Vizier does not | |
| support having inter-hyperparameter dependencies. | |
| Returns: | |
| tf.contrib.training.Hparams object containing the model's hyperparameters. | |
| """ | |
| hparams = tf.contrib.training.HParams() | |
| # Specify model architecture option. | |
| hparams.add_hparam('use_depthwise_separable', True) | |
| # Specify number of model parameters. | |
| hparams.add_hparam('filter_widths', [3] * num_filters) | |
| hparams.add_hparam('filter_depths', [1] * num_filters) | |
| hparams.add_hparam('pointwise_depths', [64] * num_filters) | |
| hparams.add_hparam('num_fc_layers', 2) | |
| hparams.add_hparam('num_fc_units', 455) | |
| hparams.add_hparam('min_read_length', 100) | |
| hparams.add_hparam('pooling_type', 'avg') | |
| # Specify activation options. | |
| hparams.add_hparam('lrelu_slope', 0.0) # Negative slope for leaky relu. | |
| # Specify training options. | |
| hparams.add_hparam('keep_prob', 1.0) | |
| hparams.add_hparam('weight_scale', 1.0) | |
| hparams.add_hparam('grad_clip_norm', 20.0) | |
| hparams.add_hparam('lr_init', 0.001) | |
| hparams.add_hparam('lr_decay', 0.1) | |
| hparams.add_hparam('optimizer', 'adam') | |
| # optimizer_hp is decay rate for 1st moment estimates for ADAM, and | |
| # momentum for SGD. | |
| hparams.add_hparam('optimizer_hp', 0.9) | |
| hparams.add_hparam('train_steps', 400000) | |
| # Overwrite defaults with specified values. | |
| hparams.parse(hparam_values) | |
| return hparams | |