Spaces:
Running
Running
File size: 6,100 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 |
# 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.
# ==============================================================================
"""Classes for storing hyperparameters, data locations, etc."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
from os.path import join
import tensorflow as tf
class Config(object):
"""Stores everything needed to train a model."""
def __init__(self, **kwargs):
# general
self.data_dir = './data' # top directory for data (corpora, models, etc.)
self.model_name = 'default_model' # name identifying the current model
# mode
self.mode = 'train' # either "train" or "eval"
self.task_names = ['chunk'] # list of tasks this model will learn
# more than one trains a multi-task model
self.is_semisup = True # whether to use CVT or train purely supervised
self.for_preprocessing = False # is this for the preprocessing script
# embeddings
self.pretrained_embeddings = 'glove.6B.300d.txt' # which pretrained
# embeddings to use
self.word_embedding_size = 300 # size of each word embedding
# encoder
self.use_chars = True # whether to include a character-level cnn
self.char_embedding_size = 50 # size of character embeddings
self.char_cnn_filter_widths = [2, 3, 4] # filter widths for the char cnn
self.char_cnn_n_filters = 100 # number of filters for each filter width
self.unidirectional_sizes = [1024] # size of first Bi-LSTM
self.bidirectional_sizes = [512] # size of second Bi-LSTM
self.projection_size = 512 # projections size for LSTMs and hidden layers
# dependency parsing
self.depparse_projection_size = 128 # size of the representations used in
# the bilinear classifier for parsing
# tagging
self.label_encoding = 'BIOES' # label encoding scheme for entity-level
# tagging tasks
self.label_smoothing = 0.1 # label smoothing rate for tagging tasks
# optimization
self.lr = 0.5 # base learning rate
self.momentum = 0.9 # momentum
self.grad_clip = 1.0 # maximum gradient norm during optimization
self.warm_up_steps = 5000.0 # linearly ramp up the lr for this many steps
self.lr_decay = 0.005 # factor for gradually decaying the lr
# EMA
self.ema_decay = 0.998 # EMA coefficient for averaged model weights
self.ema_test = True # whether to use EMA weights at test time
self.ema_teacher = False # whether to use EMA weights for the teacher model
# regularization
self.labeled_keep_prob = 0.5 # 1 - dropout on labeled examples
self.unlabeled_keep_prob = 0.8 # 1 - dropout on unlabeled examples
# sizing
self.max_sentence_length = 100 # maximum length of unlabeled sentences
self.max_word_length = 20 # maximum length of words for char cnn
self.train_batch_size = 64 # train batch size
self.test_batch_size = 64 # test batch size
self.buckets = [(0, 15), (15, 40), (40, 1000)] # buckets for binning
# sentences by length
# training
self.print_every = 25 # how often to print out training progress
self.eval_dev_every = 500 # how often to evaluate on the dev set
self.eval_train_every = 2000 # how often to evaluate on the train set
self.save_model_every = 1000 # how often to checkpoint the model
# data set
self.train_set_percent = 100 # how much of the train set to use
for k, v in kwargs.iteritems():
if k not in self.__dict__:
raise ValueError("Unknown argument", k)
self.__dict__[k] = v
self.dev_set = self.mode == "train" # whether to evaluate on the dev or
# test set
# locations of various data files
self.raw_data_topdir = join(self.data_dir, 'raw_data')
self.unsupervised_data = join(
self.raw_data_topdir,
'unlabeled_data',
'1-billion-word-language-modeling-benchmark-r13output',
'training-monolingual.tokenized.shuffled')
self.pretrained_embeddings_file = join(
self.raw_data_topdir, 'pretrained_embeddings',
self.pretrained_embeddings)
self.preprocessed_data_topdir = join(self.data_dir, 'preprocessed_data')
self.embeddings_dir = join(self.preprocessed_data_topdir,
self.pretrained_embeddings.rsplit('.', 1)[0])
self.word_vocabulary = join(self.embeddings_dir, 'word_vocabulary.pkl')
self.word_embeddings = join(self.embeddings_dir, 'word_embeddings.pkl')
self.model_dir = join(self.data_dir, "models", self.model_name)
self.checkpoints_dir = join(self.model_dir, 'checkpoints')
self.checkpoint = join(self.checkpoints_dir, 'checkpoint.ckpt')
self.best_model_checkpoints_dir = join(
self.model_dir, 'best_model_checkpoints')
self.best_model_checkpoint = join(
self.best_model_checkpoints_dir, 'checkpoint.ckpt')
self.progress = join(self.checkpoints_dir, 'progress.pkl')
self.summaries_dir = join(self.model_dir, 'summaries')
self.history_file = join(self.model_dir, 'history.pkl')
def write(self):
tf.gfile.MakeDirs(self.model_dir)
with open(join(self.model_dir, 'config.json'), 'w') as f:
f.write(json.dumps(self.__dict__, sort_keys=True, indent=4,
separators=(',', ': ')))
|