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train.py
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train.py
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#! -*- coding: utf-8 -*-
# https://github.com/MiuLab/SlotGated-SLU/blob/master/train.py
import os
import argparse
import keras
from keras.models import Model
from keras import backend as K
import tensorflow as tf
import numpy as np
from utils import createVocabulary
from utils import loadVocabulary
from utils import DataProcessor
from model import SlotGatedSLU
import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
# os.environ["CUDA_VISIBLE_DEVICES"] = ""
parser = argparse.ArgumentParser(allow_abbrev=False)
#Training Environment
parser.add_argument("--batch_size", type=int, default=32, help="Batch size.")
parser.add_argument("--max_epochs", type=int, default=50, help="Max epochs to train.")
parser.add_argument("--maxlen", type=int, default=20, help="Max epochs to train.")
parser.add_argument("--max_features", type=int, default=750, help="Max epochs to train.")
parser.add_argument("--full_attention", type=bool, default=True, help="Max epochs to train.")
#Model and Vocab
parser.add_argument("--dataset", type=str, default='atis', help="""Type 'atis' or 'snips' to use dataset provided by us or enter what ever you named your own dataset.
Note, if you don't want to use this part, enter --dataset=''. It can not be None""")
parser.add_argument("--model_path", type=str, default='./model_file', help="Path to save model.")
parser.add_argument("--vocab_path", type=str, default='./vocab', help="Path to vocabulary files.")
#Data
parser.add_argument("--train_data_path", type=str, default='train', help="Path to training data files.")
parser.add_argument("--test_data_path", type=str, default='test', help="Path to testing data files.")
parser.add_argument("--valid_data_path", type=str, default='valid', help="Path to validation data files.")
parser.add_argument("--input_file", type=str, default='seq.in', help="Input file name.")
parser.add_argument("--slot_file", type=str, default='seq.out', help="Slot file name.")
parser.add_argument("--intent_file", type=str, default='label', help="Intent file name.")
arg=parser.parse_args()
model_param = {
'maxlen':arg.maxlen,
'char_max_features':arg.max_features,
'char_embed_size':200,
'word_max_features':750,
'word_embed_size':200,
'char_embedding_matrix':None,
'word_embedding_matrix':None,
'lstm_units':128,
'lstm_dropout_rate':0.1,
'intent_dense_size':256,
'intent_nums':23,
'full_attention':arg.full_attention,
'slot_dense_size':256,
'slot_label_nums':122,
}
full_train_path = os.path.join('./data',arg.dataset,arg.train_data_path)
full_test_path = os.path.join('./data',arg.dataset,arg.test_data_path)
full_valid_path = os.path.join('./data',arg.dataset,arg.valid_data_path)
createVocabulary(os.path.join(full_train_path, arg.input_file), os.path.join(arg.vocab_path, 'in_vocab'))
createVocabulary(os.path.join(full_train_path, arg.slot_file), os.path.join(arg.vocab_path, 'slot_vocab'))
createVocabulary(os.path.join(full_train_path, arg.intent_file), os.path.join(arg.vocab_path, 'intent_vocab'))
in_vocab = loadVocabulary(os.path.join(arg.vocab_path, 'in_vocab'))
slot_vocab = loadVocabulary(os.path.join(arg.vocab_path, 'slot_vocab'))
intent_vocab = loadVocabulary(os.path.join(arg.vocab_path, 'intent_vocab'))
train_processor = DataProcessor(
os.path.join(full_train_path, arg.input_file),
os.path.join(full_train_path, arg.slot_file),
os.path.join(full_train_path, arg.intent_file),
in_vocab, slot_vocab, intent_vocab,
arg.maxlen
)
valid_processor = DataProcessor(
os.path.join(full_valid_path, arg.input_file),
os.path.join(full_valid_path, arg.slot_file),
os.path.join(full_valid_path, arg.intent_file),
in_vocab, slot_vocab, intent_vocab,
arg.maxlen
)
test_processor = DataProcessor(
os.path.join(full_test_path, arg.input_file),
os.path.join(full_test_path, arg.slot_file),
os.path.join(full_test_path, arg.intent_file),
in_vocab, slot_vocab, intent_vocab,
arg.maxlen
)
if __name__ == '__main__':
train_X, train_slot_y, train_intent_y = train_processor.get_data()
model_param['intent_nums'] = len(set(train_intent_y.flatten())) + 2
model_param['slot_label_nums'] = len(set(train_slot_y.flatten())) + 2
train_slot_y = keras.utils.to_categorical(train_slot_y,num_classes=model_param['slot_label_nums'])
train_intent_y = keras.utils.to_categorical(train_intent_y,num_classes=model_param['intent_nums'])
valid_X, valid_slot_y, valid_intent_y = valid_processor.get_data()
valid_slot_y = keras.utils.to_categorical(valid_slot_y,num_classes=model_param['slot_label_nums'])
valid_intent_y = keras.utils.to_categorical(valid_intent_y,num_classes=model_param['intent_nums'])
model = SlotGatedSLU(model_param).build()
model.compile(
optimizer='adam',
loss={'slot_out':'categorical_crossentropy', 'intent_out':'categorical_crossentropy'},
loss_weights={'slot_out': 1.0, 'intent_out': 0.5},
metrics={'intent_out':'accuracy'}
)
print(model.summary())
reduce_lr = keras.callbacks.ReduceLROnPlateau(
monitor='val_slot_out_loss',
factor=0.5,
patience=4,
verbose=1)
earlystop = keras.callbacks.EarlyStopping(
monitor='val_slot_out_loss',
patience=8,
verbose=2,
mode='min'
)
bast_model_filepath = './model_file/slotgate_model.h5'
checkpoint = keras.callbacks.ModelCheckpoint(
bast_model_filepath,
monitor='val_slot_out_loss',
verbose=1,
save_best_only=True,
mode='min'
)
H = model.fit(
x=train_X,
y={"slot_out": train_slot_y, "intent_out": train_intent_y},
validation_data=(
valid_X,
{"slot_out": valid_slot_y, "intent_out": valid_intent_y}
),
batch_size=arg.batch_size,
epochs=arg.max_epochs,
callbacks=[reduce_lr,earlystop,checkpoint]
)
# model.load_weights(bast_model_filepath)
test_X, test_slot_y, test_intent_y = test_processor.get_data()
intent_pred,slot_pred = model.predict(test_X)
# 意图准确率
intent_pred = np.argmax(intent_pred,axis=1)
intent_accuracy = (intent_pred==test_intent_y)
intent_accuracy = np.mean(intent_accuracy)*100.0
print("\n\n%s 数据集意图准确率：" % arg.dataset,intent_accuracy)
# 槽位
from metrics import *
tag2id = slot_vocab['vocab']
id2tag = {v:k for k,v in tag2id.items()}
y_true, y_pred = [],[]
for t_oh,p_oh in zip(test_slot_y,slot_pred):
t_oh = [id2tag[i] for i in t_oh if i!=0]
p_oh = np.argmax(p_oh,axis=1)
p_oh = [id2tag[i] for i in p_oh if i!=0]
y_true.append(t_oh)
y_pred.append(p_oh)
f1 = f1_score(y_true,y_pred,suffix=False)
p = precision_score(y_true,y_pred,suffix=False)
r = recall_score(y_true,y_pred,suffix=False)
acc = accuracy_score(y_true,y_pred)
print("\nf1_score: {:.4f}, precision_score: {:.4f}, recall_score: {:.4f}, accuracy_score: {:.4f}".format(f1,p,r,acc))
print(classification_report(y_true, y_pred, digits=4, suffix=False))