def cfg_from_file(filename):
"""
Load a config file and merge it into the default options.
Parameters
----------
filename : string
Path to filename.
"""
import yaml
with open(filename, 'r') as f:
return eDict(yaml.load(f))
def cfg_from_file(filename):
'''
Load a config file and merge it into the default options.
Parameters
----------
filename : string
Path to filename.
'''
import yaml
with open(filename, 'r') as f:
yaml_cfg = eDict(yaml.load(f))
_merge_a_into_b(yaml_cfg, __C)
""" The module storing the general configuration information for the code
"""
from easydict import EasyDict as eDict
import os
generalConf = eDict({})
# Set the paths required for setting up and preprocessing the data
generalConf.DATA_PATH = os.path.join(os.path.abspath("."), "../../Data")
generalConf.TRAIN_DATA = os.path.join(generalConf.DATA_PATH, "train.csv")
generalConf.TEST_DATA = os.path.join(generalConf.DATA_PATH, "test.csv")
generalConf.SAMPLE_SUBMISSION = os.path.join(generalConf.DATA_PATH,
"sample_submission.csv")
# Set the constants for the data
generalConf.MAX_WORD_LENGTH = 800 # derived from the common/visualizations/word_lengths_train.png plot
generalConf.MIN_WORD_FREQ = 40
generalConf.MAX_WORD_FREQ = 40000 # The above two values have been derived
generalConf.NUM_CLASSES = 6
# from the word_frequencies.png visualization
# Set the constant files
generalConf.PICKLE_FILE = os.path.join(generalConf.DATA_PATH,
"plug_and_play.pickle")
if __name__ == '__main__':
print("Loading the General Configuration ...")
print("\n\nGENERAL CONFIGURATION:\n")
for key, value in generalConf.items():
print(key, "->", value)
from __future__ import division
from __future__ import print_function
from easydict import EasyDict as eDict
import numpy as np
__C = eDict()
cfg = __C
__C.CONFIG_NAME = 'ConNet'
__C.DATASET_NAME = 'SVHN'
__C.SEED = 1234
# Training options
__C.TRAIN = eDict()
__C.TRAIN.DATASET_SPLIT = 'train'
__C.TRAIN.VALID_SPLIT = 0.8
__C.TRAIN.SAMPLE_SIZE = 100
__C.TRAIN.BATCH_SIZE = 32
__C.TRAIN.NUM_EPOCHS = 5
__C.TRAIN.LR = 0.001
__C.TRAIN.MOM = 0.9
def _merge_a_into_b(a, b):
'''
Merge config dictionary a into config dictionary b, clobbering the
options in b whenever they are also specified in a.
Parameters
""" The module storing the Model specific configuration
"""
from easydict import EasyDict as eDict
modelConf = eDict({})
# Set the constants for the Model
modelConf.FILTER_SIZE = 5 # all 1D convolutional filters have a width of 5
modelConf.TRAINING_PARTITION = 99 # percentage for training data. rest is the validation set
modelConf.EMB_SIZE = 16 # size of the embedded input sequences
if __name__ == '__main__':
print("Loading the General Configuration ...")
print("\n\nGENERAL CONFIGURATION:\n")
for key, value in modelConf.items():
print(key, "->", value)