TensorFlow2.0教程24：使用RNN实现文本分类

　　1.使用tensorflow_datasets 构造输入数据

　　!pip install -q tensorflow_datasets

　　spacy 2.0.18 has requirement numpy>=1.15.0, but you'll have numpy 1.14.3 which is incompatible.

　　plotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 2.2.2 which is incompatible.

　　plotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.23.0 which is incompatible.

　　neo4j-driver 1.6.2 has requirement neotime==1.0.0, but you'll have neotime 1.7.2 which is incompatible.

　　mizani 0.5.3 has requirement pandas>=0.23.4, but you'll have pandas 0.23.0 which is incompatible.

　　fastai 0.7.0 has requirement torch<0.4, but you'll have torch 0.4.1 which is incompatible.

　　You are using pip version 10.0.1, however version 19.1 is available.

　　You should consider upgrading via the 'pip install --upgrade pip' command.

　　import tensorflow_datasets as tfds

　　dataset, info = tfds.load('imdb_reviews/subwords8k', with_info=True,

　　as_supervised=True)

　　获取训练集、测试集

　　train_dataset, test_dataset = dataset['train'], dataset['test']

　　获取tokenizer对象，用进行字符处理级id转换(这里先转换成subword，再转换为id)等操作

　　tokenizer = info.features['text'].encoder

　　print('vocabulary size: ', tokenizer.vocab_size)

　　vocabulary size: 8185

　　token对象测试

　　sample_string = 'Hello word , Tensorflow'

　　tokenized_string = tokenizer.encode(sample_string)

　　print('tokened id: ', tokenized_string)

　　# 解码会原字符串

　　src_string = tokenizer.decode(tokenized_string)

　　print('original string: ', src_string)

　　tokened id: [4025, 222, 2621, 1199, 6307, 2327, 2934]

　　original string: Hello word , Tensorflow

　　解出每个subword

　　for t in tokenized_string:

　　print(str(t)+'->['+tokenizer.decode([t])+ ']')

　　4025->[Hell]

　　222->[o ]

　　2621->[word]

　　1199->[ , ]

　　6307->[Ten]

　　2327->[sor]

　　2934->[flow]

　　构建批次训练集

　　BUFFER_SIZE=10000

　　BATCH_SIZE = 64

　　train_dataset = train_dataset.shuffle(BUFFER_SIZE)

　　train_dataset = train_dataset.padded_batch(BATCH_SIZE, train_dataset.output_shapes)

　　test_dataset = test_dataset.padded_batch(BATCH_SIZE, test_dataset.output_shapes)

　　模型构建

　　下面，因为此处的句子是变长的，所以只能使用序列模型，而不能使用keras的函数api

　　# def get_model():

　　# inputs = tf.keras.Input((1240,))

　　# emb = tf.keras.layers.Embedding(tokenizer.vocab_size, 64)(inputs)

　　# h1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64))(emb)

　　# h1 = tf.keras.layers.Dense(64, activation='relu')(h1)

　　# outputs = tf.keras.layers.Dense(1, activation='sigmoid')(h1)

　　# model = tf.keras.Model(inputs, outputs)

　　# return model

　　def get_model():

　　model = tf.keras.Sequential([

　　tf.keras.layers.Embedding(tokenizer.vocab_size, 64),

　　tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),

　　tf.keras.layers.Dense(64, activation='relu'),

　　tf.keras.layers.Dense(1, activation='sigmoid')

　　])

　　return model

　　model = get_model()

　　model.compile(loss='binary_crossentropy',

　　optimizer='adam',

　　metrics=['accuracy'])

　　模型训练

　　history = model.fit(train_dataset, epochs=10,

　　validation_data=test_dataset)

　　Epoch 1/10

　　391/391 [==============================] - 827s 2s/step - loss: 0.5606 - accuracy: 0.7068 - val_loss: 0.0000e+00 - val_accuracy:

　　....

　　Epoch 10/10

　　391/391 [==============================] - 791s 2s/step - loss: 0.1333 - accuracy: 0.9548 - val_loss: 0.6117 - val_accuracy: 0.8199

　　# 查看训练过程

　　import matplotlib.pyplot as plt

　　def plot_graphs(history, string):

　　plt.plot(history.history[string])

　　plt.plot(history.history['val_'+string])

　　plt.xlabel('epochs')

　　plt.ylabel(string)

　　plt.legend([string, 'val_'+string])

　　plt.show()

　　plot_graphs(history, 'accuracy')

　　plot_graphs(history, 'loss')

　　

 

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　　test_loss, test_acc = model.evaluate(test_dataset)

　　print('test loss: ', test_loss)

　　print('test acc: ', test_acc)

　　391/Unknown - 68s 174ms/step - loss: 0.6117 - accuracy: 0.8199test loss: 0.6117385012262008

　　test acc: 0.81988

　　上述模型不会mask掉序列的padding，所以如果在有padding的寻列上训练，测试没有padding的序列时可能有所偏差。

　　def pad_to_size(vec, size):

　　zeros = [0] * (size-len(vec))

　　vec.extend(zeros)

　　return vec

　　def sample_predict(sentence, pad=False):

　　tokened_sent = tokenizer.encode(sentence)

　　if pad:

　　tokened_sent = pad_to_size(tokened_sent, 64)

　　pred = model.predict(tf.expand_dims(tokened_sent, 0))

　　return pred

　　# 没有padding的情况

　　sample_pred_text = ('The movie was cool. The animation and the graphics '

　　'were out of this world. I would recommend this movie.')

　　predictions = sample_predict(sample_pred_text, pad=False)

　　print(predictions)

　　[[0.2938048]]

　　# 有paddin的情况

　　sample_pred_text = ('The movie was cool. The animation and the graphics '

　　'were out of this world. I would recommend this movie.')

　　predictions = sample_predict(sample_pred_text, pad=True)

　　print (predictions)

　　[[0.42541984]]

　　堆叠更多的lstm层

　　from tensorflow.keras import layers

　　model = keras.Sequential(

　　[

　　layers.Embedding(tokenizer.vocab_size, 64),

　　layers.Bidirectional(layers.LSTM(64, return_sequences=True)),

　　layers.Bidirectional(layers.LSTM(32)),

　　layers.Dense(64, activation='relu'),

　　layers.Dense(1, activation='sigmoid')

　　])

　　model.compile(loss=tf.keras.losses.binary_crossentropy,

　　optimizer=tf.keras.optimizers.Adam(),

　　metrics=['accuracy'])

　　history=model.fit(train_dataset, epochs=6, validation_data=test_dataset)

　　Epoch 1/6

　　391/391 [==============================] - 1646s 4s/step - loss: 0.5270 - accuracy: 0.7414 - val_loss: 0.0000e+00 - val_accuracy:

　　....

　　Epoch 6/6

　　391/391 [==============================] - 1622s 4s/step - loss: 0.1619 - accuracy: 0.9430 - val_loss: 0.5484 - val_accuracy: 0.7808

　　plot_graphs(history, 'accuracy')

　　

 

　　plot_graphs(history, 'loss')

　　

 

　　res = model.evaluate(test_dataset)

　　print(res)

　　391/Unknown - 125s 320ms/step - loss: 0.5484 - accuracy: 0.7808[0.5484032468570162, 0.78084]