自然语言处理-情感分析 深度学习 sklearn代码实现_自然语言处理 中文情感分析 sklearn 掘金

import pandas as pd
import jieba
import re
from collections import Counter
from itertools import chain
import tensorflow as tf
from tensorflow import keras
from sklearn.model_selection import train_test_split
import numpy as np




def get_stopword():
    stopword = pd.read_csv(r"stopword.txt", header=None, quoting=3, sep="a")
    return set(stopword[0].tolist())

def clear(text):
    return re.sub("[\s\.\!\/_,$%^*(+\"\']+|[+——！，。？、~@#￥%……&*（）]+", "", text)
    
def cut_word(text):
    return jieba.cut(text)

def remove_stopword(words):
    stopword = get_stopword()
    return [word for word in words if word not in stopword]

def preprocess(text):
    text = clear(text)
    word_iter = cut_word(text)
    word_list = remove_stopword(word_iter)
    return word_list

# 根据提供的词汇列表（语料库中所有的词汇），返回词汇-索引字典，索引的顺序根据词汇的频率，
# 降序排列（词汇出现的频率越高，词汇的索引就越小）。
# 该方法返回一个字典，字典的key为词汇，字典的value为词汇对应的索引。索引根据词频决定，
# 词频越高，索引越小。
def get_word_index_dict(words):
    # Counter类可以用来统计词频数量。
    # most_common方法用来返回最常（多）出现的n个的词汇（n由参数来指定）。如果参数为空，
    # 则返回所有词汇，词频按降序排列。
    
    # 因为一些低频词汇可能仅在一个文档中出现过，因此，这些词汇对训练的帮助作用不大，故，我们
    # 可以仅对前n个常用词汇进行编号，对于不常用的低频词，统一编为一个索引即可。
    count = Counter(words).most_common(10000 - 2)
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary) + 2
    return dictionary

def word2index(li, dic):
    # 根据dic字典（词汇-索引）对data["comment"]中每个元素的每个词汇进行转化，
    # 对于低频词，字典中没有，则使用固定的编号1。
    return [dic.get(word, 1) for word in li]


data = pd.read_csv(r"movie.csv")
data["label"] = data["label"].map({"pos": 1, "neg": 0})
# 将每条评论文本进行清洗，分词，去停用词，转换为单词列表。
data["comment"] = data["comment"].apply(lambda text: preprocess(text))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60

data["comment"].head()
1

0            [英雄, 自给自足, 超能力, 造就, 酷, 科技, 以人为本, 发展, 硬道理]
1                        [男人, 嫌, 女人, 太, 聪明, 是因为, 牛, 逼]
2    [这是, 搞笑, 爱, 臭屁, 喋喋不休, 英雄, 噢, 结尾, 那句, IamIronMa...
3      [没想到, 这侠, 那侠, 要好看, 小时, 片子, 不觉, 长, 紧凑, 爽快, 意犹未尽]
4                                        [想起, 铁臂, 阿木童]
Name: comment, dtype: object
1
2
3
4
5
6

# data["comment"].tolist()返回的是一个二维列表，我们通过
# chain.from_iterable将二维列表转换成一维列表（相当于是扁平化）
c = chain.from_iterable(data["comment"].tolist())
# from_iterable返回的是一个迭代类型，而不是列表，我们通过list转换为列表。
words = list(c)
dictionary = get_word_index_dict(words)
# 将每条评论单词列表转换为数值（索引）
data["comment"] = data["comment"].apply(lambda li: word2index(li, dictionary))
1
2
3
4
5
6
7
8

data["comment"].head()
1

0          [4, 689, 194, 732, 315, 22, 1428, 290, 535]
1                [38, 1429, 235, 17, 205, 276, 91, 54]
2    [187, 128, 24, 101, 518, 4, 553, 102, 291, 243...
3    [330, 857, 898, 1310, 270, 21, 2697, 434, 129,...
4                                     [316, 476, 3177]
Name: comment, dtype: object
1
2
3
4
5
6

# 对样本进行填充，使得每个样本还有的单词数量（特征数量）相同。
# value: 要填充的值。
# padding: 要填充的位置。pre：前向填充。 post：后向填充。
# maxlen: 最大长度。
X = keras.preprocessing.sequence.pad_sequences(data["comment"],
        value=0, padding='post', maxlen=70)
y = data["label"].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)



from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(class_weight="balanced")
lr.fit(X_train, y_train)
print(lr.score(X_train, y_train))
print(lr.score(X_test, y_test))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

C:\anaconda\envs\tensorflow\lib\site-packages\sklearn\linear_model\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
  FutureWarning)
0.5768967288919336
0.5615206781402517
C:\anaconda\envs\tensorflow\lib\site-packages\sklearn\svm\base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
  "the number of iterations.", ConvergenceWarning)
1
2
3
4
5
6

# 创建模型。然后，向模型中加入网络层次。
model = keras.Sequential()
# 10000 词汇的数量。
# 16 词嵌入输出的维度。
# 输入： (N，70), 通过Embedding层，=>  (N, 70, 16)
model.add(keras.layers.Embedding(10000, 16))
# 加入平均池化层。经过池化后，
# (N, 70, 16) =》  (N, 16)
model.add(keras.layers.GlobalAveragePooling1D())
# model.add(keras.layers.GlobalMaxPooling1D())
# 加入全连接层。16为输出数量（神经元数量）。
# activation 激活函数。
# N, 16 => N, 16
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
# N, 16 => N, 1
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
# 输出网络层次的相关信息。
model.summary()
# 编译模型，对模型训练进行相关的配置。
model.compile(optimizer=tf.train.AdamOptimizer(),
        loss='binary_crossentropy', metrics=['accuracy'])
# 进行模型训练。
# epochs 训练的轮数，一个完整的训练集为一轮。
# batch_size：批次的数量。
# validation_data：指定验证集，模型在训练时，就可以使用该验证集进行验证。
# verbose：指定训练集的信息显示。
# fit 方法返回一个对象。该对象会保存每轮（epoch）训练后的正确率与损失值。
history = model.fit(X_train, y_train, epochs=100, batch_size=512,
        validation_data=(X_test, y_test), verbose=1)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29

Train on 11678 samples, validate on 3893 samples
WARNING:tensorflow:From C:\anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\ops\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Epoch 1/100
11678/11678 [==============================] - 1s 46us/sample - loss: 0.6856 - acc: 0.6305 - val_loss: 0.6729 - val_acc: 0.7460
Epoch 2/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.6623 - acc: 0.7470 - val_loss: 0.6507 - val_acc: 0.7460
Epoch 3/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.6385 - acc: 0.7470 - val_loss: 0.6255 - val_acc: 0.7460
Epoch 4/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.6121 - acc: 0.7470 - val_loss: 0.5993 - val_acc: 0.7460
Epoch 5/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.5875 - acc: 0.7470 - val_loss: 0.5786 - val_acc: 0.7460
Epoch 6/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.5708 - acc: 0.7470 - val_loss: 0.5683 - val_acc: 0.7460
Epoch 7/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.5641 - acc: 0.7470 - val_loss: 0.5658 - val_acc: 0.7460
Epoch 8/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.5624 - acc: 0.7470 - val_loss: 0.5654 - val_acc: 0.7460
Epoch 9/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5616 - acc: 0.7470 - val_loss: 0.5651 - val_acc: 0.7460
Epoch 10/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5608 - acc: 0.7470 - val_loss: 0.5648 - val_acc: 0.7460
Epoch 11/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5601 - acc: 0.7470 - val_loss: 0.5644 - val_acc: 0.7460
Epoch 12/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5593 - acc: 0.7470 - val_loss: 0.5641 - val_acc: 0.7460
Epoch 13/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.5585 - acc: 0.7470 - val_loss: 0.5637 - val_acc: 0.7460
Epoch 14/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5575 - acc: 0.7470 - val_loss: 0.5633 - val_acc: 0.7460
Epoch 15/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5566 - acc: 0.7470 - val_loss: 0.5629 - val_acc: 0.7460
Epoch 16/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5555 - acc: 0.7470 - val_loss: 0.5624 - val_acc: 0.7460
Epoch 17/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.5543 - acc: 0.7470 - val_loss: 0.5618 - val_acc: 0.7460
Epoch 18/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.5527 - acc: 0.7470 - val_loss: 0.5610 - val_acc: 0.7460
Epoch 19/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.5503 - acc: 0.7470 - val_loss: 0.5597 - val_acc: 0.7460
Epoch 20/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.5470 - acc: 0.7470 - val_loss: 0.5580 - val_acc: 0.7460
Epoch 21/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.5416 - acc: 0.7470 - val_loss: 0.5554 - val_acc: 0.7460
Epoch 22/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5347 - acc: 0.7473 - val_loss: 0.5524 - val_acc: 0.7460
Epoch 23/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.5261 - acc: 0.7478 - val_loss: 0.5488 - val_acc: 0.7457
Epoch 24/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.5157 - acc: 0.7489 - val_loss: 0.5450 - val_acc: 0.7460
Epoch 25/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.5040 - acc: 0.7517 - val_loss: 0.5416 - val_acc: 0.7465
Epoch 26/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.4920 - acc: 0.7568 - val_loss: 0.5384 - val_acc: 0.7470
Epoch 27/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.4801 - acc: 0.7637 - val_loss: 0.5352 - val_acc: 0.7475
Epoch 28/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.4673 - acc: 0.7728 - val_loss: 0.5333 - val_acc: 0.7513
Epoch 29/100
11678/11678 [==============================] - 0s 28us/sample - loss: 0.4550 - acc: 0.7803 - val_loss: 0.5319 - val_acc: 0.7547
Epoch 30/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.4434 - acc: 0.7905 - val_loss: 0.5322 - val_acc: 0.7539
Epoch 31/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.4337 - acc: 0.7932 - val_loss: 0.5340 - val_acc: 0.7547
Epoch 32/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.4221 - acc: 0.8032 - val_loss: 0.5326 - val_acc: 0.7534
Epoch 33/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.4127 - acc: 0.8080 - val_loss: 0.5345 - val_acc: 0.7519
Epoch 34/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.4037 - acc: 0.8167 - val_loss: 0.5366 - val_acc: 0.7513
Epoch 35/100
11678/11678 [==============================] - 0s 22us/sample - loss: 0.3945 - acc: 0.8221 - val_loss: 0.5392 - val_acc: 0.7498
Epoch 36/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.3870 - acc: 0.8302 - val_loss: 0.5453 - val_acc: 0.7542
Epoch 37/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.3786 - acc: 0.8337 - val_loss: 0.5477 - val_acc: 0.7511
Epoch 38/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.3716 - acc: 0.8394 - val_loss: 0.5498 - val_acc: 0.7449
Epoch 39/100
11678/11678 [==============================] - 0s 23us/sample - loss: 0.3642 - acc: 0.8431 - val_loss: 0.5537 - val_acc: 0.7439
Epoch 40/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.3574 - acc: 0.8471 - val_loss: 0.5588 - val_acc: 0.7465
Epoch 41/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.3517 - acc: 0.8495 - val_loss: 0.5648 - val_acc: 0.7465
Epoch 42/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.3451 - acc: 0.8533 - val_loss: 0.5678 - val_acc: 0.7421
Epoch 43/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.3393 - acc: 0.8572 - val_loss: 0.5737 - val_acc: 0.7429
Epoch 44/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.3334 - acc: 0.8593 - val_loss: 0.5794 - val_acc: 0.7424
Epoch 45/100
11678/11678 [==============================] - 0s 28us/sample - loss: 0.3283 - acc: 0.8605 - val_loss: 0.5852 - val_acc: 0.7411
Epoch 46/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.3233 - acc: 0.8639 - val_loss: 0.5901 - val_acc: 0.7370
Epoch 47/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.3188 - acc: 0.8640 - val_loss: 0.5953 - val_acc: 0.7344
Epoch 48/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.3135 - acc: 0.8682 - val_loss: 0.6011 - val_acc: 0.7321
Epoch 49/100
11678/11678 [==============================] - 0s 30us/sample - loss: 0.3091 - acc: 0.8733 - val_loss: 0.6075 - val_acc: 0.7336
Epoch 50/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.3044 - acc: 0.8739 - val_loss: 0.6139 - val_acc: 0.7316
Epoch 51/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.3003 - acc: 0.8763 - val_loss: 0.6196 - val_acc: 0.7290
Epoch 52/100
11678/11678 [==============================] - 0s 28us/sample - loss: 0.2973 - acc: 0.8778 - val_loss: 0.6252 - val_acc: 0.7223
Epoch 53/100
11678/11678 [==============================] - 0s 32us/sample - loss: 0.2928 - acc: 0.8808 - val_loss: 0.6345 - val_acc: 0.7316
Epoch 54/100
11678/11678 [==============================] - 0s 30us/sample - loss: 0.2902 - acc: 0.8801 - val_loss: 0.6378 - val_acc: 0.7200
Epoch 55/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2857 - acc: 0.8821 - val_loss: 0.6450 - val_acc: 0.7218
Epoch 56/100
11678/11678 [==============================] - 0s 33us/sample - loss: 0.2826 - acc: 0.8841 - val_loss: 0.6525 - val_acc: 0.7249
Epoch 57/100
11678/11678 [==============================] - 0s 32us/sample - loss: 0.2796 - acc: 0.8859 - val_loss: 0.6607 - val_acc: 0.7285
Epoch 58/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2759 - acc: 0.8871 - val_loss: 0.6635 - val_acc: 0.7169
Epoch 59/100
11678/11678 [==============================] - 0s 32us/sample - loss: 0.2724 - acc: 0.8888 - val_loss: 0.6706 - val_acc: 0.7190
Epoch 60/100
11678/11678 [==============================] - 0s 28us/sample - loss: 0.2691 - acc: 0.8905 - val_loss: 0.6766 - val_acc: 0.7154
Epoch 61/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2665 - acc: 0.8922 - val_loss: 0.6856 - val_acc: 0.7213
Epoch 62/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2649 - acc: 0.8901 - val_loss: 0.6912 - val_acc: 0.7082
Epoch 63/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2621 - acc: 0.8937 - val_loss: 0.6998 - val_acc: 0.7210
Epoch 64/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2596 - acc: 0.8948 - val_loss: 0.7032 - val_acc: 0.7180
Epoch 65/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2562 - acc: 0.8956 - val_loss: 0.7088 - val_acc: 0.7115
Epoch 66/100
11678/11678 [==============================] - 0s 32us/sample - loss: 0.2543 - acc: 0.8977 - val_loss: 0.7199 - val_acc: 0.7195
Epoch 67/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2509 - acc: 0.8963 - val_loss: 0.7245 - val_acc: 0.7180
Epoch 68/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.2485 - acc: 0.8978 - val_loss: 0.7284 - val_acc: 0.7144
Epoch 69/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2464 - acc: 0.8989 - val_loss: 0.7392 - val_acc: 0.7182
Epoch 70/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2450 - acc: 0.9005 - val_loss: 0.7417 - val_acc: 0.7090
Epoch 71/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2419 - acc: 0.9020 - val_loss: 0.7492 - val_acc: 0.7156
Epoch 72/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2400 - acc: 0.9035 - val_loss: 0.7631 - val_acc: 0.7208
Epoch 73/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2385 - acc: 0.9015 - val_loss: 0.7634 - val_acc: 0.7036
Epoch 74/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2370 - acc: 0.9044 - val_loss: 0.7696 - val_acc: 0.7156
Epoch 75/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2341 - acc: 0.9042 - val_loss: 0.7773 - val_acc: 0.7149
Epoch 76/100
11678/11678 [==============================] - 0s 30us/sample - loss: 0.2324 - acc: 0.9054 - val_loss: 0.7814 - val_acc: 0.7102
Epoch 77/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2302 - acc: 0.9073 - val_loss: 0.7898 - val_acc: 0.7151
Epoch 78/100
11678/11678 [==============================] - 0s 31us/sample - loss: 0.2285 - acc: 0.9070 - val_loss: 0.7946 - val_acc: 0.7079
Epoch 79/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.2270 - acc: 0.9095 - val_loss: 0.8019 - val_acc: 0.7118
Epoch 80/100
11678/11678 [==============================] - 0s 30us/sample - loss: 0.2253 - acc: 0.9094 - val_loss: 0.8113 - val_acc: 0.7154
Epoch 81/100
11678/11678 [==============================] - 0s 27us/sample - loss: 0.2245 - acc: 0.9109 - val_loss: 0.8192 - val_acc: 0.7159
Epoch 82/100
11678/11678 [==============================] - 0s 30us/sample - loss: 0.2215 - acc: 0.9125 - val_loss: 0.8211 - val_acc: 0.7074
Epoch 83/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2199 - acc: 0.9133 - val_loss: 0.8282 - val_acc: 0.7090
Epoch 84/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2191 - acc: 0.9136 - val_loss: 0.8340 - val_acc: 0.7051
Epoch 85/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2173 - acc: 0.9142 - val_loss: 0.8411 - val_acc: 0.7092
Epoch 86/100
11678/11678 [==============================] - 0s 24us/sample - loss: 0.2148 - acc: 0.9145 - val_loss: 0.8541 - val_acc: 0.7146
Epoch 87/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2137 - acc: 0.9152 - val_loss: 0.8540 - val_acc: 0.7085
Epoch 88/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2135 - acc: 0.9157 - val_loss: 0.8660 - val_acc: 0.7138
Epoch 89/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2117 - acc: 0.9178 - val_loss: 0.8668 - val_acc: 0.7041
Epoch 90/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2099 - acc: 0.9179 - val_loss: 0.8778 - val_acc: 0.7133
Epoch 91/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2102 - acc: 0.9176 - val_loss: 0.8854 - val_acc: 0.7136
Epoch 92/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2074 - acc: 0.9169 - val_loss: 0.8861 - val_acc: 0.7054
Epoch 93/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2057 - acc: 0.9186 - val_loss: 0.8926 - val_acc: 0.7015
Epoch 94/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2044 - acc: 0.9194 - val_loss: 0.8999 - val_acc: 0.7085
Epoch 95/100
11678/11678 [==============================] - 0s 25us/sample - loss: 0.2031 - acc: 0.9205 - val_loss: 0.9101 - val_acc: 0.7118
Epoch 96/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.2024 - acc: 0.9201 - val_loss: 0.9120 - val_acc: 0.7028
Epoch 97/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2010 - acc: 0.9219 - val_loss: 0.9180 - val_acc: 0.7023
Epoch 98/100
11678/11678 [==============================] - 0s 29us/sample - loss: 0.2002 - acc: 0.9234 - val_loss: 0.9249 - val_acc: 0.7033
Epoch 99/100
11678/11678 [==============================] - 0s 28us/sample - loss: 0.1984 - acc: 0.9234 - val_loss: 0.9320 - val_acc: 0.7067
Epoch 100/100
11678/11678 [==============================] - 0s 26us/sample - loss: 0.1977 - acc: 0.9221 - val_loss: 0.9374 - val_acc: 0.7028
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204

# 调用predict方法进行预测。
t = model.predict(X_test)
# print(t)
t = np.where(t >= 0.5, 1, 0)
print(pd.Series(t.flatten()).value_counts())
# print(classification_report(y_test, t))
1
2
3
4
5
6

1    3177
0     716
dtype: int64
1
2
3

import matplotlib.pyplot as plt

# 通过history可以获取：
# acc 训练集正确率
# val_acc 验证集正确率
# loss 训练集损失值
# val_loss 验证集损失值
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(1, len(acc) + 1)

plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.show()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

plt.show()
1
2
3
4
5
6
7
8

# 通过chain.from_iterable实现相当于是ndarray的扁平化操作。
x = chain.from_iterable([[1, 2, 3], [4, 5]])
list(x)
1
2
3

[1, 2, 3, 4, 5]
1