Keras学习案例（2）

多分类问题：路透社数据集

路透社数据集是一个文本分类数据集，包含46个不同的而主题，每个主题中至少含有10个样本

import numpy as np
import matplotlib.pyplot as plt
from keras.utils.np_utils import to_categorical
from keras import models
from keras import layers
from keras.datasets import reuters

#加载路透社数据集
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(num_words=10000)

#将索引解码为文本
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]])

#数据向量化
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)
x_test = vectorize_sequences(test_data)

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)

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

#模型定义
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'])

#验证集
x_val = x_train[:1000]
partical_x_train = x_train[1000:]

y_val = one_hot_train_labels[:1000]
partical_y_train = one_hot_train_labels[1000:]

#训练模型
history = model.fit(partical_x_train, partical_y_train, epochs=20, batch_size=512, validation_data=(x_val, y_val))

#绘制训练损失和验证损失图像
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
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()

结果：