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电影评论分类：二分类问题
使用IMDB 数据集，它包含来自互联网电影数据库（IMDB）的50 000 条严重两极分
化的评论。数据集被分为用于训练的25 000 条评论与用于测试的25 000 条评论，训练集和测试集都包含50% 的正面评论和50% 的负面评论。
加载IMDB数据集
IMDB 数据集内置于Keras 库。它已经过预处理：评论（单词序列）已经被转换为整数序列，其中每个整数代表字典中的某个单词。
imdb = keras.datasets.imdb
(train_data,train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
参数num_words=10000 的意思是仅保留训练数据中前10 000 个最常出现的单词。低频单词将被舍弃。这样得到的向量数据不会太大，便于处理。
探索数据
train_data 和test_data 这两个变量都是评论组成的列表，每条评论又是单词索引组成
的列表（表示一系列单词）。train_labels 和test_labels 都是0 和1 组成的列表，其中0
代表负面（negative），1 代表正面（positive）。
print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
>>Training entries: 25000, labels: 25000
print(train_data[0])
>>[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 5952, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]
print(train_labels[0])
>>1
len(train_data[0]), len(train_data[1])
>>(218, 189)
将整数转换回字词
了解如何将整数转换回文本可能很有用。在以下代码中，将创建一个辅助函数来查询包含整数到字符串映射的字典对象。
# A dictionary mapping words to an integer index
word_index = imdb.get_word_index()
# The first indices are reserved
word_index = {k:(v+3) for k,v in word_index.items()}
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2 # unknown
word_index["<UNUSED>"] = 3
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
decode_review(train_data[0])
>>"<START> this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert <UNK> is an amazing actor and now the same being director <UNK> father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for <UNK> and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also <UNK> to the two little boy's that played the <UNK> of norman and paul they were just brilliant children are often left out of the <UNK> list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all"
准备数据
不能将整数序列直接输入神经网络。需要将列表转换为张量。转换方法有以下两种。
- 填充列表，使其具有相同的长度，再将列表转换成形状为 (samples, word_indices)
的整数张量，然后网络第一层使用能处理这种整数张量的层（即Embedding 层，本书后面会详细介绍）。 - 对列表进行 one-hot 编码，将其转换为 0 和 1 组成的向量。举个例子，序列[3, 5]将会被转换为10 000 维向量，只有索引为3 和5 的元素是1，其余元素都是0。然后网络第一层可以用Dense 层，它能够处理浮点数向量数据。
# 影评转换为张量。由于影评的长度必须相同，使用 pad_sequences 函数将长度标准化
train_data = keras.preprocessing.sequence.pad_sequences(train_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data,
value=word_index["<PAD>"],
padding='post',
maxlen=256)
len(train_data[0]), len(train_data[1])
>>(256, 256)
# 第一条影评
print(train_data[0])
>>[ 1 14 22 16 43 530 973 1622 1385 65 458 4468 66 3941
4 173 36 256 5 25 100 43 838 112 50 670 2 9
35 480 284 5 150 4 172 112 167 2 336 385 39 4
172 4536 1111 17 546 38 13 447 4 192 50 16 6 147
2025 19 14 22 4 1920 4613 469 4 22 71 87 12 16
43 530 38 76 15 13 1247 4 22 17 515 17 12 16
626 18 2 5 62 386 12 8 316 8 106 5 4 2223
5244 16 480 66 3785 33 4 130 12 16 38 619 5 25
124 51 36 135 48 25 1415 33 6 22 12 215 28 77
52 5 14 407 16 82 2 8 4 107 117 5952 15 256
4 2 7 3766 5 723 36 71 43 530 476 26 400 317
46 7 4 2 1029 13 104 88 4 381 15 297 98 32
2071 56 26 141 6 194 7486 18 4 226 22 21 134 476
26 480 5 144 30 5535 18 51 36 28 224 92 25 104
4 226 65 16 38 1334 88 12 16 283 5 16 4472 113
103 32 15 16 5345 19 178 32 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0]
构建网络
输入数据是向量，而标签是标量（1和0），这是最简单的情况。有一类网络在这种问题上表现很好，就是带有relu激活的全连接层（Dense）的简单堆叠。
# input shape is the vocabulary count used for the movie reviews (10,000 words)
vocab_size = 10000
model = keras.Sequential()
model.add(keras.layers.Embedding(vocab_size, 16))
model.add(keras.layers.GlobalAveragePooling1D())
model.add(keras.layers.Dense(16, activation=tf.nn.relu))
model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
model.summary()
# 第一层是 Embedding 层。该层会在整数编码的词汇表中查找每个字词-索引的嵌入向量。模型在接受训练时会学习这些向量。这些向量会向输出数组添加一个维度。
# 生成的维度为：(batch, sequence, embedding)
# 接下来，一个 GlobalAveragePooling1D 层通过对序列维度求平均值，针对每个样本返回一个长度固定的输出向量。
# 这样，模型便能够以尽可能简单的方式处理各种长度的输入
# 该长度固定的输出向量会传入一个全连接 (Dense) 层（包含 16 个隐藏单元）
# 最后一层与单个输出节点密集连接。应用 sigmoid 激活函数后，结果是介于 0 到 1 之间的浮点值，表示概率或置信水平。
>>
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding (Embedding) (None, None, 16) 160000
_________________________________________________________________
global_average_pooling1d (Gl (None, 16) 0
_________________________________________________________________
dense (Dense) (None, 16) 272
_________________________________________________________________
dense_1 (Dense) (None, 1) 17
=================================================================
Total params: 160,289
Trainable params: 160,289
Non-trainable params: 0
_________________________________________________________________
配置模型使用优化器和损失函数
由于面对的是一个二分类问题，网络输出是一个概率值（网络最后一层使用sigmoid 激活函数，仅包含一个单元），那么最好使用binary_crossentropy（二元交叉熵）损失。这并不是唯一可行的选择，比如你还可以使用mean_squared_error（均方误差）。但对于输出概率值的模型，交叉熵（crossentropy）往往是最好的选择。交叉熵是来自于信息论领域的概念，用于衡量概率分布之间的距离，在这个例子中就是真实分布与预测值之间的距离。
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='binary_crossentropy',
metrics=['accuracy'])
留出验证集
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
训练模型
# 用有 512 个样本的小批次训练模型 40 个周期。这将对 x_train 和 y_train 张量中的所有样本进行 40 次迭代。
# 在训练期间，监控模型在验证集的 10000 个样本上的损失和准确率：
history = model.fit(partial_x_train,
partial_y_train,
epochs=40,
batch_size=512,
validation_data=(x_val, y_val),
verbose=1)
>>
Train on 15000 samples, validate on 10000 samples
WARNING:tensorflow:From E:\Anaconda3\Anaconda3_install\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 1/40
15000/15000 [==============================] - 1s 50us/sample - loss: 0.6912 - acc: 0.6431 - val_loss: 0.6881 - val_acc: 0.7217
Epoch 2/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.6828 - acc: 0.7466 - val_loss: 0.6772 - val_acc: 0.7467
Epoch 3/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.6660 - acc: 0.7658 - val_loss: 0.6565 - val_acc: 0.7680
Epoch 4/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.6377 - acc: 0.7761 - val_loss: 0.6250 - val_acc: 0.7729
Epoch 5/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.5980 - acc: 0.8033 - val_loss: 0.5849 - val_acc: 0.7972
Epoch 6/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.5505 - acc: 0.8249 - val_loss: 0.5409 - val_acc: 0.8117
Epoch 7/40
15000/15000 [==============================] - 1s 44us/sample - loss: 0.5003 - acc: 0.8397 - val_loss: 0.4951 - val_acc: 0.8294
Epoch 8/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.4523 - acc: 0.8573 - val_loss: 0.4549 - val_acc: 0.8410
Epoch 9/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.4096 - acc: 0.8701 - val_loss: 0.4203 - val_acc: 0.8516
Epoch 10/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.3729 - acc: 0.8810 - val_loss: 0.3927 - val_acc: 0.8556
Epoch 11/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.3424 - acc: 0.8876 - val_loss: 0.3703 - val_acc: 0.8642
Epoch 12/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.3169 - acc: 0.8959 - val_loss: 0.3531 - val_acc: 0.8669
Epoch 13/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.2959 - acc: 0.9011 - val_loss: 0.3383 - val_acc: 0.8718
Epoch 14/40
15000/15000 [==============================] - 1s 44us/sample - loss: 0.2771 - acc: 0.9070 - val_loss: 0.3273 - val_acc: 0.8761
Epoch 15/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.2611 - acc: 0.9111 - val_loss: 0.3183 - val_acc: 0.8766
Epoch 16/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.2468 - acc: 0.9168 - val_loss: 0.3109 - val_acc: 0.8766
Epoch 17/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.2334 - acc: 0.9210 - val_loss: 0.3048 - val_acc: 0.8806
Epoch 18/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.2216 - acc: 0.9249 - val_loss: 0.2997 - val_acc: 0.8825
Epoch 19/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.2109 - acc: 0.9263 - val_loss: 0.2953 - val_acc: 0.8831
Epoch 20/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.2013 - acc: 0.9311 - val_loss: 0.2925 - val_acc: 0.8831
Epoch 21/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.1913 - acc: 0.9369 - val_loss: 0.2900 - val_acc: 0.8844
Epoch 22/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1831 - acc: 0.9399 - val_loss: 0.2880 - val_acc: 0.8849
Epoch 23/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.1747 - acc: 0.9437 - val_loss: 0.2876 - val_acc: 0.8847
Epoch 24/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1675 - acc: 0.9466 - val_loss: 0.2865 - val_acc: 0.8848
Epoch 25/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.1601 - acc: 0.9500 - val_loss: 0.2855 - val_acc: 0.8856
Epoch 26/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1535 - acc: 0.9529 - val_loss: 0.2866 - val_acc: 0.8840
Epoch 27/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.1472 - acc: 0.9550 - val_loss: 0.2864 - val_acc: 0.8856
Epoch 28/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1412 - acc: 0.9570 - val_loss: 0.2875 - val_acc: 0.8851
Epoch 29/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1359 - acc: 0.9601 - val_loss: 0.2893 - val_acc: 0.8851
Epoch 30/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1304 - acc: 0.9613 - val_loss: 0.2891 - val_acc: 0.8864
Epoch 31/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1248 - acc: 0.9644 - val_loss: 0.2906 - val_acc: 0.8861
Epoch 32/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1198 - acc: 0.9671 - val_loss: 0.2924 - val_acc: 0.8855
Epoch 33/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1150 - acc: 0.9682 - val_loss: 0.2951 - val_acc: 0.8846
Epoch 34/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1106 - acc: 0.9693 - val_loss: 0.2976 - val_acc: 0.8851
Epoch 35/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.1065 - acc: 0.9704 - val_loss: 0.3005 - val_acc: 0.8840
Epoch 36/40
15000/15000 [==============================] - 1s 43us/sample - loss: 0.1023 - acc: 0.9723 - val_loss: 0.3024 - val_acc: 0.8833
Epoch 37/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.0980 - acc: 0.9737 - val_loss: 0.3054 - val_acc: 0.8828
Epoch 38/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.0942 - acc: 0.9755 - val_loss: 0.3093 - val_acc: 0.8816
Epoch 39/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.0911 - acc: 0.9767 - val_loss: 0.3135 - val_acc: 0.8816
Epoch 40/40
15000/15000 [==============================] - 1s 42us/sample - loss: 0.0871 - acc: 0.9781 - val_loss: 0.3166 - val_acc: 0.8824
评估模型
results = model.evaluate(test_data, test_labels)
print(results)
25000/25000 [==============================] - 0s 16us/sample - loss: 0.3390 - acc: 0.8702
[0.338996932888031, 0.87016]
绘制准确率和损失的变化图
# model.fit() 返回一个 History 对象，该对象包含一个字典，其中包括训练期间发生的所有情况：
history_dict = history.history
history_dict.keys()
>>dict_keys(['loss', 'acc', 'val_loss', 'val_acc'])
import matplotlib.pyplot as plt
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)
# "bo" is for "blue dot"
plt.plot(epochs, loss, 'bo', label='Training loss')
# b is for "solid blue line"
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()
plt.clf() # clear figure
acc_values = history_dict['acc']
val_acc_values = history_dict['val_acc']
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()
参考资料
影评文本分类
Python深度学习
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