Tensorflow 基本文本分類

導入工具庫

import tensorflow as tf
from tensorflow import keras

import numpy as np

print(tf.__version__)

1.10.0

導入數(shù)據(jù)

導入數(shù)據(jù)集赵刑，仍然是采用國內(nèi)特色的導入方式玲躯，先自己下載嚼锄，然后再導入减拭。

imdb = keras.datasets.imdb

(train_data, train_labels), (test_data, test_labels) = imdb.load_data(path = 'H:/tf_project/imdb.npz',num_words=10000)

npz格式的數(shù)據(jù)也可以直接使用np.load()導入，導入格式為類似字典的格式区丑，可以使用dict()將之轉(zhuǎn)化為字典拧粪。

npy格式的數(shù)據(jù)文件使用np.load()導入之后直接就是array()格式。

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]

len(train_data[0]), len(train_data[1])

(218, 189)

將整型數(shù)組重新轉(zhuǎn)化為單詞

# A dictionary mapping words to an integer index
# 直接下載
# 地址：  https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json
word_index = imdb.get_word_index(r"H:\tf_project\imdb_word_index.json")
word_index

{'fawn': 34701,
 'tsukino': 52006,
 'nunnery': 52007,
 'sonja': 16816,
 'vani': 63951,
 'woods': 1408,
 ...}

# 所有word的編碼往后移三位
word_index = {k:(v+3) for k,v in word_index.items()} 
# 添加其他標記符
word_index["<PAD>"] = 0
word_index["<START>"] = 1
word_index["<UNK>"] = 2  # unknown
word_index["<UNUSED>"] = 3

# 翻轉(zhuǎn)key和value
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])

將訓練數(shù)據(jù)轉(zhuǎn)化為文字

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"

array格式的電影評論需要轉(zhuǎn)化為張量才能傳入網(wǎng)絡沧侥，可以通過如下兩種方式實現(xiàn)：

獨熱編碼可霎，即轉(zhuǎn)化為只包含0和1的向量，如列表[3,5]可以被轉(zhuǎn)化為一個10000維向量正什，該向量中除了下標為3和5的位置為1啥纸，其他位置均為0。這種方式對內(nèi)存要求比較高婴氮。
我們可以填充數(shù)組斯棒，使所有的數(shù)組具備相同的長度，然后傳入到網(wǎng)絡中主经。

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)

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]

搭建模型

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()

_________________________________________________________________
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
_________________________________________________________________

第一層是Embedding層
第二層是GlobalAveragePooling1D層
第三四層是全連接層
輸出層只有一個節(jié)點钻洒，使用sigmoid激活函數(shù)將結(jié)果約束到0-1之間超全。

損失函數(shù)和優(yōu)化器

model.compile(optimizer=tf.train.AdamOptimizer(),
              loss='binary_crossentropy',
              metrics=['accuracy'])

創(chuàng)建驗證集

x_val = train_data[:10000]
partial_x_train = train_data[10000:]

y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]

訓練模型

這里的history是fit的返回值，包括了訓練過程中指標變化信息

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
Epoch 1/40
15000/15000 [==============================] - 4s 249us/step - loss: 0.7391 - acc: 0.5035 - val_loss: 0.7010 - val_acc: 0.4947
Epoch 2/40
15000/15000 [==============================] - 1s 52us/step - loss: 0.6931 - acc: 0.5251 - val_loss: 0.6912 - val_acc: 0.5338
Epoch 3/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.6903 - acc: 0.5801 - val_loss: 0.6897 - val_acc: 0.5656
Epoch 4/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.6884 - acc: 0.6543 - val_loss: 0.6879 - val_acc: 0.6747
Epoch 5/40
15000/15000 [==============================] - 1s 48us/step - loss: 0.6864 - acc: 0.6421 - val_loss: 0.6860 - val_acc: 0.7004
Epoch 6/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.6841 - acc: 0.7283 - val_loss: 0.6837 - val_acc: 0.7259
Epoch 7/40
15000/15000 [==============================] - 1s 53us/step - loss: 0.6810 - acc: 0.7203 - val_loss: 0.6805 - val_acc: 0.6978
Epoch 8/40
15000/15000 [==============================] - 1s 53us/step - loss: 0.6769 - acc: 0.7057 - val_loss: 0.6759 - val_acc: 0.6885
Epoch 9/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.6707 - acc: 0.7150 - val_loss: 0.6695 - val_acc: 0.7142
Epoch 10/40
15000/15000 [==============================] - 1s 56us/step - loss: 0.6628 - acc: 0.7443 - val_loss: 0.6610 - val_acc: 0.7356
Epoch 11/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.6529 - acc: 0.7487 - val_loss: 0.6503 - val_acc: 0.7497
Epoch 12/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.6387 - acc: 0.7843 - val_loss: 0.6345 - val_acc: 0.7720
Epoch 13/40
15000/15000 [==============================] - 1s 56us/step - loss: 0.6182 - acc: 0.7861 - val_loss: 0.6157 - val_acc: 0.7727
Epoch 14/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.5933 - acc: 0.7986 - val_loss: 0.5889 - val_acc: 0.7900
Epoch 15/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.5614 - acc: 0.8103 - val_loss: 0.5584 - val_acc: 0.7956
Epoch 16/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.5295 - acc: 0.8157 - val_loss: 0.5293 - val_acc: 0.8052
Epoch 17/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.4963 - acc: 0.8327 - val_loss: 0.5008 - val_acc: 0.8192
Epoch 18/40
15000/15000 [==============================] - 1s 52us/step - loss: 0.4647 - acc: 0.8423 - val_loss: 0.4726 - val_acc: 0.8273
Epoch 19/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.4349 - acc: 0.8519 - val_loss: 0.4471 - val_acc: 0.8363
Epoch 20/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.4076 - acc: 0.8607 - val_loss: 0.4243 - val_acc: 0.8434
Epoch 21/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.3829 - acc: 0.8707 - val_loss: 0.4043 - val_acc: 0.8489
Epoch 22/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.3612 - acc: 0.8773 - val_loss: 0.3872 - val_acc: 0.8547
Epoch 23/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.3424 - acc: 0.8833 - val_loss: 0.3729 - val_acc: 0.8587
Epoch 24/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.3256 - acc: 0.8885 - val_loss: 0.3605 - val_acc: 0.8643
Epoch 25/40
15000/15000 [==============================] - 1s 48us/step - loss: 0.3111 - acc: 0.8935 - val_loss: 0.3500 - val_acc: 0.8673
Epoch 26/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2980 - acc: 0.8960 - val_loss: 0.3415 - val_acc: 0.8698
Epoch 27/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.2868 - acc: 0.8989 - val_loss: 0.3338 - val_acc: 0.8711
Epoch 28/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2758 - acc: 0.9039 - val_loss: 0.3268 - val_acc: 0.8746
Epoch 29/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2666 - acc: 0.9058 - val_loss: 0.3218 - val_acc: 0.8751
Epoch 30/40
15000/15000 [==============================] - 1s 53us/step - loss: 0.2588 - acc: 0.9079 - val_loss: 0.3164 - val_acc: 0.8768
Epoch 31/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.2498 - acc: 0.9125 - val_loss: 0.3124 - val_acc: 0.8769
Epoch 32/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.2431 - acc: 0.9135 - val_loss: 0.3086 - val_acc: 0.8793
Epoch 33/40
15000/15000 [==============================] - 1s 50us/step - loss: 0.2352 - acc: 0.9170 - val_loss: 0.3052 - val_acc: 0.8805
Epoch 34/40
15000/15000 [==============================] - 1s 47us/step - loss: 0.2288 - acc: 0.9183 - val_loss: 0.3030 - val_acc: 0.8807
Epoch 35/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.2231 - acc: 0.9195 - val_loss: 0.2998 - val_acc: 0.8802
Epoch 36/40
15000/15000 [==============================] - 1s 51us/step - loss: 0.2166 - acc: 0.9220 - val_loss: 0.2975 - val_acc: 0.8825
Epoch 37/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2111 - acc: 0.9247 - val_loss: 0.2956 - val_acc: 0.8831
Epoch 38/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2058 - acc: 0.9259 - val_loss: 0.2940 - val_acc: 0.8834
Epoch 39/40
15000/15000 [==============================] - 1s 49us/step - loss: 0.2003 - acc: 0.9294 - val_loss: 0.2922 - val_acc: 0.8846
Epoch 40/40
15000/15000 [==============================] - 1s 52us/step - loss: 0.1953 - acc: 0.9307 - val_loss: 0.2908 - val_acc: 0.8848

驗證模型

results = model.evaluate(test_data, test_labels)

print(results)

25000/25000 [==============================] - 2s 86us/step
[0.3060342230606079, 0.87492000000000003]

結(jié)果可視化

history_dict = history.history
history_dict.keys()

dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])

誤差率

import matplotlib.pyplot as plt
%matplotlib inline
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()

output_31_0.png

上圖中，大約20個epochs之后系宜，validation loss的下降變緩，模型開始出現(xiàn)過擬合現(xiàn)象机打。

準確率

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()

output_34_0.png

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