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How to use _config method in molecule
Best Python code snippet using molecule_python
config.py
Source:config.py 
1from configparser import ConfigParser2import os3class myconf(ConfigParser):4 def __init__(self, defaults=None):5 ConfigParser.__init__(self, defaults=defaults)6 def optionxform(self, optionstr):7 return optionstr8class Configurable(myconf):9 def __init__(self, config_file):10 config = myconf()11 config.read(config_file)12 self._config = config13 print('Loaded config file sucessfully.')14 for section in config.sections():15 for k, v in config.items(section):16 print(k, ":", v)17 # if not os.path.isdir(self.save_dir):18 # os.mkdir(self.save_dir)19 config.write(open(config_file, 'w'))20 # Data21 @property22 def word_Embedding(self):23 return self._config.getboolean('Data', 'word_Embedding')24 @property25 def freq_1_unk(self):26 return self._config.getboolean('Data', 'freq_1_unk')27 @property28 def word_Embedding_Path(self):29 return self._config.get('Data', 'word_Embedding_Path')30 @property31 def datafile_path(self):32 return self._config.get('Data', 'datafile_path')33 @property34 def name_trainfile(self):35 return self._config.get('Data', 'name_trainfile')36 @property37 def name_devfile(self):38 return self._config.get('Data', 'name_devfile')39 @property40 def name_testfile(self):41 return self._config.get('Data', 'name_testfile')42 @property43 def min_freq(self):44 return self._config.getint('Data', 'min_freq')45 @property46 def word_data(self):47 return self._config.getboolean('Data', 'word_data')48 @property49 def char_data(self):50 return self._config.getboolean('Data', 'char_data')51 @property52 def shuffle(self):53 return self._config.getboolean('Data', 'shuffle')54 @property55 def epochs_shuffle(self):56 return self._config.getboolean('Data', 'epochs_shuffle')57 @property58 def FIVE_CLASS_TASK(self):59 return self._config.getboolean('Data', 'FIVE_CLASS_TASK') \60 @property61 def TWO_CLASS_TASK(self):62 return self._config.getboolean('Data', 'TWO_CLASS_TASK')63 # Save64 @property65 def snapshot(self):66 value = self._config.get('Save', 'snapshot')67 if value == "None" or value == "none":68 return None69 else:70 return value71 @property72 def predict(self):73 value = self._config.get('Save', 'predict')74 if value == "None" or value == "none":75 return None76 else:77 return value78 @property79 def test(self):80 return self._config.getboolean('Save', 'test')81 @property82 def save_dir(self):83 return self._config.get('Save', 'save_dir')84 @save_dir.setter85 def save_dir(self, value):86 self._config.set('Save', 'save_dir', str(value))87 @property88 def rm_model(self):89 return self._config.getboolean('Save', 'rm_model')90 # Model91 @property92 def static(self):93 return self._config.getboolean("Model", "static")94 @property95 def wide_conv(self):96 return self._config.getboolean("Model", "wide_conv")97 @property98 def CNN(self):99 return self._config.getboolean("Model", "CNN")100 @property101 def HighWay_CNN(self):102 return self._config.getboolean("Model", "HighWay_CNN")103 @property104 def CNN_MUI(self):105 return self._config.getboolean("Model", "CNN_MUI")106 @property107 def DEEP_CNN(self):108 return self._config.getboolean("Model", "DEEP_CNN")109 @property110 def DEEP_CNN_MUI(self):111 return self._config.getboolean("Model", "DEEP_CNN_MUI")112 @property113 def LSTM(self):114 return self._config.getboolean("Model", "LSTM")115 @property116 def GRU(self):117 return self._config.getboolean("Model", "GRU")118 @property119 def BiLSTM(self):120 return self._config.getboolean("Model", "BiLSTM")121 @property122 def BiLSTM_1(self):123 return self._config.getboolean("Model", "BiLSTM_1")124 @property125 def HighWay_BiLSTM_1(self):126 return self._config.getboolean("Model", "HighWay_BiLSTM_1")127 @property128 def CNN_LSTM(self):129 return self._config.getboolean("Model", "CNN_LSTM")130 @property131 def CNN_BiLSTM(self):132 return self._config.getboolean("Model", "CNN_BiLSTM")133 @property134 def CLSTM(self):135 return self._config.getboolean("Model", "CLSTM")136 @property137 def CBiLSTM(self):138 return self._config.getboolean("Model", "CBiLSTM")139 @property140 def CGRU(self):141 return self._config.getboolean("Model", "CGRU")142 @property143 def BiGRU(self):144 return self._config.getboolean("Model", "BiGRU")145 @property146 def CNN_BiGRU(self):147 return self._config.getboolean("Model", "CNN_BiGRU")148 @property149 def embed_dim(self):150 return self._config.getint("Model", "embed_dim")151 @property152 def lstm_hidden_dim(self):153 return self._config.getint("Model", "lstm_hidden_dim")154 @property155 def lstm_num_layers(self):156 return self._config.getint("Model", "lstm_num_layers")157 @property158 def batch_normalizations(self):159 return self._config.getboolean("Model", "batch_normalizations")160 @property161 def bath_norm_momentum(self):162 return self._config.getfloat("Model", "bath_norm_momentum")163 @property164 def batch_norm_affine(self):165 return self._config.getboolean("Model", "batch_norm_affine")166 @property167 def dropout(self):168 return self._config.getfloat("Model", "dropout")169 @property170 def dropout_embed(self):171 return self._config.getfloat("Model", "dropout_embed")172 @property173 def max_norm(self):174 value = self._config.get("Model", "max_norm")175 if value == "None" or value == "none":176 return None177 else:178 return value179 @property180 def clip_max_norm(self):181 return self._config.getint("Model", "clip_max_norm")182 @property183 def kernel_num(self):184 return self._config.getint("Model", "kernel_num")185 @property186 def kernel_sizes(self):187 value = self._config.get("Model", "kernel_sizes")188 # print(list(value))189 value = [int(k) for k in list(value) if k != ","]190 return value191 @kernel_sizes.setter192 def kernel_sizes(self, value):193 self._config.set("Model", "kernel_sizes", str(value))194 @property195 def init_weight(self):196 return self._config.getboolean("Model", "init_weight")197 @property198 def init_weight_value(self):199 return self._config.getfloat("Model", "init_weight_value")200 # Optimizer201 @property202 def learning_rate(self):203 return self._config.getfloat("Optimizer", "learning_rate")204 @property205 def Adam(self):206 return self._config.getboolean("Optimizer", "Adam")207 @property208 def SGD(self):209 return self._config.getboolean("Optimizer", "SGD")210 @property211 def Adadelta(self):212 return self._config.getboolean("Optimizer", "Adadelta")213 @property214 def momentum_value(self):215 return self._config.getfloat("Optimizer", "optim_momentum_value")216 @property217 def weight_decay(self):218 return self._config.getfloat("Optimizer", "weight_decay")219 # Train220 @property221 def num_threads(self):222 return self._config.getint("Train", "num_threads")223 @property224 def device(self):225 return self._config.getint("Train", "device")226 @property227 def cuda(self):228 return self._config.getboolean("Train", "cuda")229 @property230 def epochs(self):231 return self._config.getint("Train", "epochs")232 @property233 def batch_size(self):234 return self._config.getint("Train", "batch_size")235 @property236 def log_interval(self):237 return self._config.getint("Train", "log_interval")238 @property239 def test_interval(self):240 return self._config.getint("Train", "test_interval")241 @property242 def save_interval(self):...
configdict_test.py
Source:configdict_test.py
1# Copyright 2018 The TensorFlow Authors.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14"""Tests for config_util.configdict."""15from __future__ import absolute_import16from __future__ import division17from __future__ import print_function18from absl.testing import absltest19from tf_util import configdict20class ConfigDictTest(absltest.TestCase):21 def setUp(self):22 super(ConfigDictTest, self).setUp()23 self._config = configdict.ConfigDict({24 "int": 1,25 "float": 2.0,26 "bool": True,27 "str": "hello",28 "nested": {29 "int": 3,30 },31 "double_nested": {32 "a": {33 "int": 3,34 },35 "b": {36 "float": 4.0,37 }38 }39 })40 def testAccess(self):41 # Simple types.42 self.assertEqual(1, self._config.int)43 self.assertEqual(1, self._config["int"])44 self.assertEqual(2.0, self._config.float)45 self.assertEqual(2.0, self._config["float"])46 self.assertTrue(self._config.bool)47 self.assertTrue(self._config["bool"])48 self.assertEqual("hello", self._config.str)49 self.assertEqual("hello", self._config["str"])50 # Single nested config.51 self.assertEqual(3, self._config.nested.int)52 self.assertEqual(3, self._config["nested"].int)53 self.assertEqual(3, self._config.nested["int"])54 self.assertEqual(3, self._config["nested"]["int"])55 # Double nested config.56 self.assertEqual(3, self._config["double_nested"].a.int)57 self.assertEqual(3, self._config["double_nested"]["a"].int)58 self.assertEqual(3, self._config["double_nested"].a["int"])59 self.assertEqual(3, self._config["double_nested"]["a"]["int"])60 self.assertEqual(4.0, self._config.double_nested.b.float)61 self.assertEqual(4.0, self._config.double_nested["b"].float)62 self.assertEqual(4.0, self._config.double_nested.b["float"])63 self.assertEqual(4.0, self._config.double_nested["b"]["float"])64 # Nonexistent parameters.65 with self.assertRaises(AttributeError):66 _ = self._config.nonexistent67 with self.assertRaises(KeyError):68 _ = self._config["nonexistent"]69 def testSetAttribut(self):70 # Overwrite existing simple type.71 self._config.int = 4072 self.assertEqual(40, self._config.int)73 # Overwrite existing nested simple type.74 self._config.nested.int = 4075 self.assertEqual(40, self._config.nested.int)76 # Overwrite existing nested config.77 self._config.double_nested.a = {"float": 50.0}78 self.assertIsInstance(self._config.double_nested.a, configdict.ConfigDict)79 self.assertEqual(50.0, self._config.double_nested.a.float)80 self.assertNotIn("int", self._config.double_nested.a)81 # Set new simple type.82 self._config.int_2 = 1083 self.assertEqual(10, self._config.int_2)84 # Set new nested simple type.85 self._config.nested.int_2 = 2086 self.assertEqual(20, self._config.nested.int_2)87 # Set new nested config.88 self._config.double_nested.c = {"int": 30}89 self.assertIsInstance(self._config.double_nested.c, configdict.ConfigDict)90 self.assertEqual(30, self._config.double_nested.c.int)91 def testSetItem(self):92 # Overwrite existing simple type.93 self._config["int"] = 4094 self.assertEqual(40, self._config.int)95 # Overwrite existing nested simple type.96 self._config["nested"].int = 4097 self.assertEqual(40, self._config.nested.int)98 self._config.nested["int"] = 5099 self.assertEqual(50, self._config.nested.int)100 # Overwrite existing nested config.101 self._config.double_nested["a"] = {"float": 50.0}102 self.assertIsInstance(self._config.double_nested.a, configdict.ConfigDict)103 self.assertEqual(50.0, self._config.double_nested.a.float)104 self.assertNotIn("int", self._config.double_nested.a)105 # Set new simple type.106 self._config["int_2"] = 10107 self.assertEqual(10, self._config.int_2)108 # Set new nested simple type.109 self._config.nested["int_2"] = 20110 self.assertEqual(20, self._config.nested.int_2)111 self._config.nested["int_3"] = 30112 self.assertEqual(30, self._config.nested.int_3)113 # Set new nested config.114 self._config.double_nested["c"] = {"int": 30}115 self.assertIsInstance(self._config.double_nested.c, configdict.ConfigDict)116 self.assertEqual(30, self._config.double_nested.c.int)117 def testDelete(self):118 # Simple types.119 self.assertEqual(1, self._config.int)120 del self._config.int121 with self.assertRaises(AttributeError):122 _ = self._config.int123 with self.assertRaises(KeyError):124 _ = self._config["int"]125 self.assertEqual(2.0, self._config["float"])126 del self._config["float"]127 with self.assertRaises(AttributeError):128 _ = self._config.float129 with self.assertRaises(KeyError):130 _ = self._config["float"]131 # Nested config.132 self.assertEqual(3, self._config.nested.int)133 del self._config.nested134 with self.assertRaises(AttributeError):135 _ = self._config.nested136 with self.assertRaises(KeyError):137 _ = self._config["nested"]138if __name__ == "__main__":...
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