def main(unused_argv):
hparams = cn_hparams.create_hparams()
# model_fun=[2,3,4,5],30
model_fn = cn_model.create_model_fn(hparams,
model_impl=dual_encoder_model,
model_fun=model.RNN_MaxPooling,
RNNInit=tf.nn.rnn_cell.LSTMCell,
is_bidirection=True)
estimator = Estimator(model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig())
# tf.contrib.learn.RunConfig()
input_fn_train = cn_inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=hparams.batch_size,
num_epochs=FLAGS.num_epochs)
input_fn_eval = cn_inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=hparams.eval_batch_size,
num_epochs=1)
eval_metrics = cn_metrics.create_evaluation_metrics()
eval_monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=input_fn_eval,
every_n_steps=FLAGS.eval_every,
metrics=eval_metrics) # 喂数据
estimator.fit(input_fn=input_fn_train, steps=None, monitors=[eval_monitor])
def main(unused_argv):
hparams = cn_hparams.create_hparams()
# model_fun=[2,3,4,5],30
model_fn = cn_model.create_model_fn(hparams,
model_impl=encoder_model,
model_fun=model,
RNNInit=RNNInit,
is_bidirection=is_bidirection,
input_keep_prob=1.0,
output_keep_prob=1.0)
estimator = Estimator(model_fn=model_fn,
model_dir=MODEL_DIR,
config=tf.contrib.learn.RunConfig())
input_fn_train = cn_inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.TRAIN,
input_files=[TRAIN_FILE],
batch_size=hparams.batch_size,
num_epochs=FLAGS.num_epochs)
input_fn_eval = cn_inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[VALIDATION_FILE],
batch_size=hparams.eval_batch_size,
num_epochs=1)
# tf.contrib.learn.RunConfig()
eval_metrics = cn_metrics.create_evaluation_metrics()
eval_monitor = tf.contrib.learn.monitors.ValidationMonitor(
input_fn=input_fn_eval,
every_n_steps=FLAGS.eval_every,
metrics=eval_metrics,
early_stopping_metric="recall_at_1",
early_stopping_metric_minimize=True,
early_stopping_rounds=10000) # 喂数据
estimator.fit(input_fn=input_fn_train,
steps=10000,
monitors=[eval_monitor])
# features["context_len"]=tf.constant(context_len, shape=[len(context_len), 1], dtype=tf.int64)
return features, None
if __name__ == "__main__":
# get raw data
dataset = lyx.load_pkl('dataset')
raw_data = dataset.raw_data
# restore model & parameters
hparams = cn_hparams.create_hparams()
model_fn = cn_model.create_model_fn(hparams,
model_impl=encoder_model,
model_fun=model,
RNNInit=RNNInit,
is_bidirection=is_bidirection,
input_keep_prob=1.0,
output_keep_prob=1.0)
estimator = tf.contrib.learn.Estimator(model_fn=model_fn,
model_dir=MODEL_DIR)
while True:
input_question = input('欢迎咨询医疗问题，请描述您的问题: ')
# if input the index of question
if input_question.isdigit():
input_question_index = int(input_question)
input_question = raw_data[input_question_index]['question']
print('您的问题：%s' % input_question)
tf.flags.DEFINE_integer("test_batch_size", 16, "Batch size for testing")
tf.flags.DEFINE_boolean("customized_word_vector", True,
"choose random or customized word vectors")
FLAGS = tf.flags.FLAGS
if not FLAGS.model_dir:
# print("You must specify a model directory")
sys.exit(1)
tf.logging.set_verbosity(FLAGS.loglevel)
if __name__ == "__main__":
hparams = cn_hparams.create_hparams()
model_fn = cn_model.create_model_fn(hparams,
model_impl=encoder_model,
model_fun=model.RNN_CNN_MaxPooling,
RNNInit=tf.nn.rnn_cell.LSTMCell,
is_bidirection=True)
estimator = tf.contrib.learn.Estimator(model_fn=model_fn,
model_dir=FLAGS.model_dir,
config=tf.contrib.learn.RunConfig())
input_fn_test = cn_inputs.create_input_fn(
mode=tf.contrib.learn.ModeKeys.EVAL,
input_files=[FLAGS.test_file],
batch_size=FLAGS.test_batch_size,
num_epochs=1)
eval_metrics = cn_metrics.create_evaluation_metrics()
estimator.evaluate(input_fn=input_fn_test,
steps=None,