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get_pred_prob.py
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get_pred_prob.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author: xiwen zhao
@created: 2016.12.3
"""
from optparse import OptionParser
from trainer import BaseTrainer
from keras.models import model_from_json
from keras.optimizers import RMSprop, SGD
class Trainer(BaseTrainer):
def get_model_name(self):
return __file__.split('/')[-1].split('.')[0]
def set_merge_num(self, merge_num):
self.merge_num = merge_num
def post_prepare_X(self, x):
return [x for i in range(self.merge_num)]
def set_model_config(self, options):
self.config = dict(
optimizer = options.optimizer,
model_name = options.model_name,
)
def get_optimizer(self, key_optimizer):
if key_optimizer == 'rmsprop':
return RMSprop(lr=0.001, rho=0.9, epsilon=1e-06)
else: # 'sgd'
return SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=False)
def build_model(self, config, weights):
fname = '../data/model/subtask%s_%s_config_new.json' % (self.key_subtask, config['model_name'])
json_string = open(fname, 'r').read()
model = model_from_json(json_string)
if config['nb_classes'] > 2:
loss_type = 'categorical_crossentropy'
else:
loss_type = 'binary_crossentropy'
model.compile(loss=loss_type,
optimizer=self.get_optimizer(config['optimizer']),
metrics=['accuracy'])
return model
def main():
optparser = OptionParser()
optparser.add_option("-t", "--task", dest="key_subtask", default="D")
optparser.add_option("-p", "--nb_epoch", dest="nb_epoch", type="int", default=50)
optparser.add_option("-e", "--embedding", dest="fname_Wemb", default="glove.twitter.27B.25d.txt.trim")
optparser.add_option("-o", "--optimizer", dest="optimizer", default="rmsprop")
optparser.add_option("-m", "--model_name", dest="model_name", default="finki")
optparser.add_option("-n", "--merge_num", dest="merge_num", type="int", default=2)
opts, args = optparser.parse_args()
trainer = Trainer(opts)
trainer.set_merge_num(opts.merge_num)
trainer.pred_prob()
if __name__ == '__main__':
main()