新闻分类(多分类问题)_python深度学习-原例

新闻分类-多分类问题

加载路透社数据集

from keras.datasets import reuters
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

参数 num_words=10000 将数据限定为前 10 000 个最常出现的单词。我们有 8982 个训练样本和 2246 个测试样本。

len(train_data)

len(test_data)

每个样本都是一个整数列表（表示单词索引）

train_data[10]

可以用下列代码将索引解码为单词

word_index = reuters.get_word_index()
reverse_word_index = dict([(value, key) for (key, value) in word_index.items()]) 
decoded_newswire = ' '.join([reverse_word_index.get(i - 3, '?') for i in train_data[0]])

样本对应的标签是一个 0~45 范围内的整数，即话题索引编号。

编码数据

import numpy as np
def vectorize_sequences(sequences, dimension=10000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results
x_train = vectorize_sequences(train_data) #将训练数据向量化
x_test = vectorize_sequences(test_data) #将测试数据向量化

在这个例子中，标签的 one-hot 编码就是将每个标签表示为全零向量，只有标签索引对应的元素为 1。（Keras 内置方法可以实现这个操作）

def to_one_hot(labels, dimension=46):
    results = np.zeros((len(labels), dimension))
    for i, label in enumerate(labels):
        results[i, label] = 1.
    return results
one_hot_train_labels = to_one_hot(train_labels) 
one_hot_test_labels = to_one_hot(test_labels)

Keras 内置方法

one_hot_train_labels = to_categorical(train_labels)
one_hot_test_labels = to_categorical(test_labels)

构建网络

模型定义

from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))

编译模型

model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])

留出验证集

x_val = x_train[:1000]
partial_x_train = x_train[1000:]
y_val = one_hot_train_labels[:1000]
partial_y_train = one_hot_train_labels[1000:]

训练模型,20个轮次

history = model.fit(partial_x_train,partial_y_train,epochs=20,batch_size=512,validation_data=(x_val, y_val))

绘制训练损失和验证损失

import matplotlib.pyplot as plt
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(loss) + 1)
plt.plot(epochs, loss, 'bo', label='Training loss')
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()

绘制训练精度和验证精度

plt.clf() 
acc = history.history['acc']
val_acc = history.history['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()

查看过拟合轮次，从头开始重新训练一个模型

model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(10000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))
model.compile(optimizer='rmsprop',
 loss='categorical_crossentropy',
 metrics=['accuracy'])
model.fit(partial_x_train,
 partial_y_train,
 epochs=9,
 batch_size=512,
 validation_data=(x_val, y_val))
results = model.evaluate(x_test, one_hot_test_labels)

在新数据上生成预测结果

predictions = model.predict(x_test)