Tensorflow-keras实战（十一）：Embedding实战(embedding+padding+pooling)

一时失言乱红尘 2023-08-17 15:59 69阅读 0赞

import matplotlib as mpl
    import matplotlib.pyplot as plt
    %matplotlib inline
    import numpy as np
    import pandas as pd
    import os
    import sklearn
    import sys
    import time
    import tensorflow as tf
    
    from tensorflow import keras
    
    print(tf.__version__)
    print(sys.version_info)
    for module in mpl,np,pd,sklearn,tf,keras:
        print(module.__name__, module.__version__)
    
    
    
    # 数据集载入
    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)
    
    print(train_data[0],train_labels[0])
    print(train_data.shape,train_labels.shape)
    print(len(train_data[0]),len(train_data[1]))
    print(test_data.shape,test_labels.shape)
    
    
    
    # 构建词表索引
    word_index = imdb.get_word_index()
    print(len(word_index))
    print(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])
    
    
    
    # 数据padding
    max_length = 500
    
    train_data = keras.preprocessing.sequence.pad_sequences(
        train_data, # list of list
        value = word_index['<PAD>'],
        padding = 'post',# post, pre
        maxlen = max_length)
    
    test_data = keras.preprocessing.sequence.pad_sequences(
        test_data, # list of list
        value = word_index['<PAD>'],
        padding = 'post',# post, pre
        maxlen = max_length)
    
    print(train_data[0])
    
    
    
    # 模型构建与训练
    embedding_dim = 16
    batch_size = 128
    
    model = keras.models.Sequential([
        # 1. define matrix: [vocab_size, embedding_dim]
        # 2. [1,2,3,4..], max_length * embedding_dim
        # 3. batch_size * max_length * embedding_dim
        keras.layers.Embedding(vocab_size, embedding_dim,
                               input_length = max_length),
        # batch_size * max_length * embedding_dim -> batch_size * embedding_dim
        keras.layers.GlobalAveragePooling1D(),
        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)
    
    
    
    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()
        
    plot_learning_curves(history,'accuracy',30,0,1)
    plot_learning_curves(history,'loss',30,0,1)
    
    
    model.evaluate(test_data,test_labels,
                  batch_size = batch_size)