步骤

1、加载数据集；
2、准备数据；
3、模型定义；
4、留出验证集；
5、训练模型（model.compile和model.fit）；
6、绘制训练损失和验证损失、绘制训练精度和验证精度
7、从头开始训练一个模型

# 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)

# 准备数据
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

'''
# 标签向量化
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) # 将训练标签向量化
one_hot_test_labels = to_one_hot(test_labels) # 将测试标签向量化
'''

# 另外，Keras内置方法也可实现这个操作
from keras.utils.np_utils import to_categorical

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

# 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'))

# 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:]

# 编译模型
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
# 训练模型
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() # 清空图像

acc = history.history['acc']
val_acc = history.history['val_acc']

plt.plot(epochs, acc, 'ro', label='Training acc')
plt.plot(epochs, val_acc, 'r', label='Validation acc')
plt.title('Training and validation accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
#plt.legend()

plt.show()

# 重新开始训练一个模型
model.fit(x_train,
          one_hot_train_labels,
          epochs=9,
          batch_size=512
          )
results = model.evaluate(x_test, one_hot_test_labels)

print(results)

# 在新数据上生成预测结果
predictions = model.predict(x_test)
print(predictions[0].shape)
print(np.sum(predictions[0]))
print(np.argmax(predictions[0]))

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