### END SETTINGS ###
# init
if not os.path.exists(work_dir):
os.makedirs(work_dir)
with open(corpus_file) as f:
sents = [[twp.split('|')[0].lower() for twp in line.split()] for line in f]
embeddings = helper.char_embeddings(sents)
if timestamp and start_epoch and start_iteration:
errors = helper.load_errors(
'%s-%d-%d.errors' % (timestamp, start_epoch, start_iteration),
work_dir)
load_weights = '%s-%d-%d.weights' % (timestamp, start_epoch,
start_iteration)
print('init previous states...')
print('timestamp: ', timestamp)
print('start_epoch: ', start_epoch)
print('start_iteration: ', start_iteration)
else:
errors = []
start_epoch = 0
start_iteration = 0
timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
load_weights = None
print('init new states...')
print('timestamp: ', timestamp)
confusion_set = ['than', 'then']
### END SETTINGS ###
# init
if not os.path.exists(work_dir):
os.makedirs(work_dir)
with open(corpus_file) as f:
sents = [[twp.split('|')[0].lower() for twp in line.split()] for line in f]
embeddings = helper.char_embeddings(sents)
if timestamp and start_epoch and start_iteration:
errors = helper.load_errors('%s-%d-%d.errors' % (timestamp, start_epoch, start_iteration), work_dir)
load_weights = '%s-%d-%d.weights' % (timestamp, start_epoch, start_iteration)
print('init previous states...')
print('timestamp: ', timestamp)
print('start_epoch: ', start_epoch)
print('start_iteration: ', start_iteration)
else:
errors = []
start_epoch = 0
start_iteration = 0
timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
load_weights = None
print('init new states...')
print('timestamp: ', timestamp)
print()
model.add(Dropout(0.5))
model.add(Dense(hidden_layer_size, 1))
model.add(Activation('sigmoid'))
# compile model
print('compile model...')
print()
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
class_mode="binary")
# load previous states or continue with random initialization
if timestamp and start_epoch:
errors = helper.load_errors('%s-%d.errors' % (timestamp, start_epoch), work_dir)
model.load_weights(os.path.join(work_dir, '%s-%d.weights' % (timestamp, start_epoch)))
print('init previous states...')
print('timestamp: ', timestamp)
print('start_epoch: ', start_epoch)
else:
errors = []
start_epoch = 0
timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
print('init new states...')
print('timestamp: ', timestamp)
print()
# start training
print('start training...')
