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main.py
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main.py
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# coding=utf-8
# @author: cer
import tensorflow as tf
from data import *
# from model import Model
from model import Model
from my_metrics import *
from tensorflow.python import debug as tf_debug
import numpy as np
input_steps = 50
embedding_size = 64
hidden_size = 100
n_layers = 2
batch_size = 16
vocab_size = 871
slot_size = 122
intent_size = 22
epoch_num = 50
def get_model():
model = Model(input_steps, embedding_size, hidden_size, vocab_size, slot_size,
intent_size, epoch_num, batch_size, n_layers)
model.build()
return model
def train(is_debug=False):
model = get_model()
sess = tf.Session()
if is_debug:
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)
sess.run(tf.global_variables_initializer())
# print(tf.trainable_variables())
train_data = open("dataset/atis-2.train.w-intent.iob", "r").readlines()
test_data = open("dataset/atis-2.dev.w-intent.iob", "r").readlines()
train_data_ed = data_pipeline(train_data)
test_data_ed = data_pipeline(test_data)
word2index, index2word, slot2index, index2slot, intent2index, index2intent = \
get_info_from_training_data(train_data_ed)
# print("slot2index: ", slot2index)
# print("index2slot: ", index2slot)
index_train = to_index(train_data_ed, word2index, slot2index, intent2index)
index_test = to_index(test_data_ed, word2index, slot2index, intent2index)
for epoch in range(epoch_num):
mean_loss = 0.0
train_loss = 0.0
for i, batch in enumerate(getBatch(batch_size, index_train)):
# 执行一个batch的训练
_, loss, decoder_prediction, intent, mask, slot_W = model.step(sess, "train", batch)
# if i == 0:
# index = 0
# print("training debug:")
# print("input:", list(zip(*batch))[0][index])
# print("length:", list(zip(*batch))[1][index])
# print("mask:", mask[index])
# print("target:", list(zip(*batch))[2][index])
# # print("decoder_targets_one_hot:")
# # for one in decoder_targets_one_hot[index]:
# # print(" ".join(map(str, one)))
# print("decoder_logits: ")
# for one in decoder_logits[index]:
# print(" ".join(map(str, one)))
# print("slot_W:", slot_W)
# print("decoder_prediction:", decoder_prediction[index])
# print("intent:", list(zip(*batch))[3][index])
# mean_loss += loss
# train_loss += loss
# if i % 10 == 0:
# if i > 0:
# mean_loss = mean_loss / 10.0
# print('Average train loss at epoch %d, step %d: %f' % (epoch, i, mean_loss))
# mean_loss = 0
train_loss /= (i + 1)
print("[Epoch {}] Average train loss: {}".format(epoch, train_loss))
# 每训一个epoch，测试一次
pred_slots = []
slot_accs = []
intent_accs = []
for j, batch in enumerate(getBatch(batch_size, index_test)):
decoder_prediction, intent = model.step(sess, "test", batch)
decoder_prediction = np.transpose(decoder_prediction, [1, 0])
if j == 0:
index = random.choice(range(len(batch)))
# index = 0
sen_len = batch[index][1]
print("Input Sentence : ", index_seq2word(batch[index][0], index2word)[:sen_len])
print("Slot Truth : ", index_seq2slot(batch[index][2], index2slot)[:sen_len])
print("Slot Prediction : ", index_seq2slot(decoder_prediction[index], index2slot)[:sen_len])
print("Intent Truth : ", index2intent[batch[index][3]])
print("Intent Prediction : ", index2intent[intent[index]])
slot_pred_length = list(np.shape(decoder_prediction))[1]
pred_padded = np.lib.pad(decoder_prediction, ((0, 0), (0, input_steps-slot_pred_length)),
mode="constant", constant_values=0)
pred_slots.append(pred_padded)
# print("slot_pred_length: ", slot_pred_length)
true_slot = np.array((list(zip(*batch))[2]))
true_length = np.array((list(zip(*batch))[1]))
true_slot = true_slot[:, :slot_pred_length]
# print(np.shape(true_slot), np.shape(decoder_prediction))
# print(true_slot, decoder_prediction)
slot_acc = accuracy_score(true_slot, decoder_prediction, true_length)
intent_acc = accuracy_score(list(zip(*batch))[3], intent)
# print("slot accuracy: {}, intent accuracy: {}".format(slot_acc, intent_acc))
slot_accs.append(slot_acc)
intent_accs.append(intent_acc)
pred_slots_a = np.vstack(pred_slots)
# print("pred_slots_a: ", pred_slots_a.shape)
true_slots_a = np.array(list(zip(*index_test))[2])[:pred_slots_a.shape[0]]
# print("true_slots_a: ", true_slots_a.shape)
print("Intent accuracy for epoch {}: {}".format(epoch, np.average(intent_accs)))
print("Slot accuracy for epoch {}: {}".format(epoch, np.average(slot_accs)))
print("Slot F1 score for epoch {}: {}".format(epoch, f1_for_sequence_batch(true_slots_a, pred_slots_a)))
def test_data():
train_data = open("dataset/atis-2.train.w-intent.iob", "r").readlines()
test_data = open("dataset/atis-2.dev.w-intent.iob", "r").readlines()
train_data_ed = data_pipeline(train_data)
test_data_ed = data_pipeline(test_data)
word2index, index2word, slot2index, index2slot, intent2index, index2intent = \
get_info_from_training_data(train_data_ed)
# print("slot2index: ", slot2index)
# print("index2slot: ", index2slot)
index_train = to_index(train_data_ed, word2index, slot2index, intent2index)
index_test = to_index(test_data_ed, word2index, slot2index, intent2index)
batch = next(getBatch(batch_size, index_test))
unziped = list(zip(*batch))
print("word num: ", len(word2index.keys()), "slot num: ", len(slot2index.keys()), "intent num: ",
len(intent2index.keys()))
print(np.shape(unziped[0]), np.shape(unziped[1]), np.shape(unziped[2]), np.shape(unziped[3]))
print(np.transpose(unziped[0], [1, 0]))
print(unziped[1])
print(np.shape(list(zip(*index_test))[2]))
if __name__ == '__main__':
# train(is_debug=True)
# test_data()
train()