"2: Only during validation\n"
"3: Only during last validation, after training is completed\n")
# Parse arguments.
FLAGS, unparsed = parser.parse_known_args()
# Check if config file was selected.
if FLAGS.config == '':
print('Please pass configuration file(s) as --c parameter')
exit(-1)
# Get list of configs that need to be loaded.
configs_to_load = recurrent_config_parse(FLAGS.config, [])
# Create param interface object.
param_interface = ParamInterface()
# Read the YAML files one by one - but in reverse order!
for config in reversed(configs_to_load):
# Open file and try to add that to list of parameter dictionaries.
with open(config, 'r') as stream:
# Load param dictionaries in reverse order.
param_interface.add_custom_params(yaml.load(stream))
print('Loaded configuration from file {}'.format(config))
# Add to list of loaded configs.
configs_to_load.append(config)
# Done. In here Param Registry contains configuration loaded (and
# overwritten) from several files.
# Get problem and model names.
try:
aux_tuple = AlgSeqAuxTuple(mask, seq_length, 1)
return data_tuple, aux_tuple
# method for changing the maximum length, used mainly during curriculum
# learning
def set_max_length(self, max_length):
self.max_sequence_length = max_length
if __name__ == "__main__":
""" Tests sequence generator - generates and displays a random sample"""
# "Loaded parameters".
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_custom_params({
'control_bits': 4,
'data_bits': 8,
'batch_size': 2,
# 'randomize_control_lines': True,
'min_sequence_length': 1,
'max_sequence_length': 10
})
# Create problem object.
problem = DualSerialReverseRecallCommandLines(params)
# Get generator
generator = problem.return_generator()
# Get batch.
data_tuple, aux_tuple = next(generator)
# Display single sample (0) from batch.
# TK: ok, what is going on in here...?
# add problem folder to path for import
sys.path.insert(
0,
os.path.normpath(
os.path.join(os.getcwd(), '../../problems/seq_to_seq/text2text')))
import translation as pb
# instantiate problem
eng_prefixes = ("i am ", "i m ", "he is", "he s ", "she is", "she s",
"you are", "you re ", "we are", "we re ", "they are",
"they re ")
from utils.param_interface import ParamInterface
params = ParamInterface()
params.add_custom_params({
'batch_size': 64,
'training_size': 0.90,
'output_lang_name': 'fra',
'max_sequence_length': 15,
'eng_prefixes': eng_prefixes,
'use_train_data': True,
'data_folder': '~/data/language',
'reverse': False
})
problem = pb.Translation(params)
print('Problem successfully created.\n')
# get size of vocabulary for input & output language