TensorFlow2.0教程-词嵌入

TensorFlow2.0教程-词嵌入

TensorFlow2.0教程-词嵌入

最全Tensorflow 2.0 入门教程持续更新:

Doit:最全Tensorflow 2.0 入门教程持续更新zhuanlan.zhihu.com图标

完整tensorflow2.0教程代码请看https://github.com/czy36mengfei/tensorflow2_tutorials_chinese (欢迎star)

本教程主要由tensorflow2.0官方教程的个人学习复现笔记整理而来,中文讲解,方便喜欢阅读中文教程的朋友,官方教程:https://www.tensorflow.org

1.载入数据

vocab_size = 10000
(train_x, train_y), (test_x, text_y) = keras.datasets.imdb.load_data(num_words=vocab_size)
print(train_x[0])
print(train_x[1])
[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]
[1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 8255, 2, 349, 2637, 148, 605, 2, 8003, 15, 123, 125, 68, 2, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 2, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 2, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95]
word_index = keras.datasets.imdb.get_word_index()
word_index = {k:(v+3) for k,v in word_index.items()}
word_index['<PAD>'] = 0
word_index['<START>'] = 1
word_index['<UNK>'] = 2
word_index['<UNUSED>'] = 3
reverse_word_index = {v:k for k, v in word_index.items()}
def decode_review(text):
    return ' '.join([reverse_word_index.get(i, '?') for i in text])
print(decode_review(train_x[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
maxlen = 500
train_x = keras.preprocessing.sequence.pad_sequences(train_x,value=word_index['<PAD>'],
                                                    padding='post', maxlen=maxlen)
test_x = keras.preprocessing.sequence.pad_sequences(test_x,value=word_index['<PAD>'],
                                                    padding='post', maxlen=maxlen)

2.构建模型

embedding_dim = 16
model = keras.Sequential([
    layers.Embedding(vocab_size, embedding_dim, input_length=maxlen),
    layers.GlobalAveragePooling1D(),
    layers.Dense(16, activation='relu'),
    layers.Dense(1, activation='sigmoid')

])
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding (Embedding)        (None, 500, 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
_________________________________________________________________
model.compile(optimizer=keras.optimizers.Adam(),
             loss=keras.losses.BinaryCrossentropy(),
             metrics=['accuracy'])
history = model.fit(train_x, train_y, epochs=30, batch_size=512, validation_split=0.1)
Train on 22500 samples, validate on 2500 samples
22500/22500 [==============================] - 1s 66us/sample - loss: 0.1290 - accuracy: 0.9596 - val_loss: 0.3055 - val_accuracy: 0.8948
import matplotlib.pyplot as plt

acc = history.history['accuracy']
val_acc = history.history['val_accuracy']

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

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(loc='lower right')
plt.figure(figsize=(16,9))

plt.show()

3.导出词嵌入

e = model.layers[0]
weights = e.get_weights()[0]
print(weights.shape)
(10000, 16)
out_v = open('vecs.tsv', 'w')
out_m = open('meta.tsv', 'w')
for word_num in range(vocab_size):
    word = reverse_word_index[word_num]
    embeddings = weights[word_num]
    out_m.write(word + "\n")
    out_v.write('\t'.join([str(x) for x in embeddings]) + "\n")
out_v.close()
out_m.close()

放到 Embedding Projector:projector.tensorflow.org 上进行可视化

编辑于 2019-05-02

文章被以下专栏收录