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- # -*- coding: utf-8 -*-
- """单向RNN、双向RNN-embedding.ipynb
- Automatically generated by Colaboratory.
- Original file is located at
- https://colab.research.google.com/drive/18T6WUWX_fdG23ufTD6CkelZnQVYnaU3w
- """
- import matplotlib as mpl
- import matplotlib.pyplot as plt
- import numpy as np
- import pandas as pd
- import tensorflow as tf
- from tensorflow import keras
- import sklearn
- import os
- import sys
- import time
- print(tf.__version__)
- print(sys.version_info)
- for module in mpl,np,pd,sklearn,tf,keras:
- print(module.__name__,module.__version__)
- imdb=keras.datasets.imdb
- vocab_size=10000
- index_from=3
- (train_data,train_labels),(test_data,test_labels)=imdb.load_data(num_words=vocab_size,index_from=index_from)
- word_index=imdb.get_word_index()
- print(len(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['<END>']=3
- reverse_word_index=dict([
- (value,key) for key,value in word_index.items()
- ])
- def decode_review(text_ids):
- return ' '.join([reverse_word_index.get(word_id,'<UNK>') for word_id in text_ids])
- decode_review(train_data[0])
- max_length=500
- train_data=keras.preprocessing.sequence.pad_sequences(
- train_data,value=word_index['<PAD>'],
- padding='post',maxlen=max_length
- )
- test_data=keras.preprocessing.sequence.pad_sequences(
- test_data,value=word_index['<PAD>'],
- padding='post',maxlen=max_length
- )
- print(train_data[0])
- embedding_dim=16
- batch_size=512
- # 把DNN的全局平均换成单向RNN,时间变长,不断修改效果变好
- # return_sequences:Boolean. Whether to return the last output in the output sequence, or the full sequence 文本生成、机器翻译是要返回所有序列的True,只要最后一个序列False
- single_rnn_model=keras.models.Sequential([
- keras.layers.Embedding(vocab_size,embedding_dim,input_length=max_length),
- keras.layers.SimpleRNN(units=64,return_sequences=False),
- # w=64,b=64
- keras.layers.Dense(64,activation='relu'),
- keras.layers.Dense(1,activation='sigmoid'),
- ])
- single_rnn_model.summary()
- single_rnn_model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])
- # 全连接层参数是4160 wx+b:x是一维的64
- single_rnn_model.variables
- history_single_rnn=single_rnn_model.fit(
- train_data,train_labels,
- epochs=30,
- batch_size=batch_size,
- validation_split=0.2
- )
- def plot_learning_curves(history,label,epochs,min_value,max_value):
- data={}
- data[label]=history.history[label]
- data['val_'+label]=history.history['val_'+label]
- pd.DataFrame(data).plot(figsize=(8,5))
- plt.grid(True)
- plt.axis([0,epochs,min_value,max_value])
- plt.show()
- # 训练集、验证集上的准确率
- plot_learning_curves(history_single_rnn,'accuracy',30,0,1)
- # 训练集、验证集上的损失
- plot_learning_curves(history_single_rnn,'loss',30,0,1)
- single_rnn_model.evaluate(
- test_data,test_labels,
- batch_size=batch_size,
- verbose=0
- )
- """损失接近70%,准确率是50%—单向RNN没啥用"""
- # !nvidia-smi
- # 改成双向RNN
- embedding_dim=16
- batch_size=512
- model=keras.models.Sequential([
- keras.layers.Embedding(vocab_size,embedding_dim,input_length=max_length),
- # 增加数据,2层双向RNN
- keras.layers.Bidirectional(keras.layers.SimpleRNN(units=64,return_sequences=True)),
- keras.layers.Bidirectional(keras.layers.SimpleRNN(units=64,return_sequences=False)),
- keras.layers.Dense(64,activation='relu'),
- keras.layers.Dense(1,activation='sigmoid'),
- ])
- model.summary()
- model.compile(optimizer='adam',
- loss='binary_crossentropy',
- metrics=['accuracy'])
- history=model.fit(train_data,train_labels,epochs=30,batch_size=batch_size,validation_split=0.2)
- """在训练集上准确率能达到100%,就足够说明模型强大了"""
- plot_learning_curves(history,'accuracy',30,0,1)
- plot_learning_curves(history,'loss',30,0,4)
- """过拟合了,可能是模型太复杂,改为单层的RNN"""
- # 改成双向RNN
- embedding_dim=16
- batch_size=512
- model=keras.models.Sequential([
- keras.layers.Embedding(vocab_size,embedding_dim,input_length=max_length),
- # 增加数据,2层双向RNN
- keras.layers.Bidirectional(keras.layers.SimpleRNN(units=64,return_sequences=True)),
- # keras.layers.Bidirectional(keras.layers.SimpleRNN(units=64,return_sequences=False)),
- keras.layers.Dense(64,activation='relu'),
- keras.layers.Dense(1,activation='sigmoid'),
- ])
- model.summary()
- model.compile(optimizer='adam',
- loss='binary_crossentropy',
- metrics=['accuracy'])
- history=model.fit(train_data,train_labels,epochs=30,batch_size=batch_size,validation_split=0.2)
- plot_learning_curves(history,'accuracy',30,0,1)
- plot_learning_curves(history,'loss',30,0,4)
- model.evaluate(test_data,test_labels,batch_size=batch_size,verbose=0)
- """与单向RNN相比loss减少,accuracy上升,效果变好;但是仍然是过拟合的,可以看作模型强大"""
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