[人工智能]多层感知机、卷积神经网络、循环神经网络、循环卷积神经网络预测多元分类

#多层感知器神经网络模型
# 0. 调用要使用的包
from keras.datasets import reuters
from keras.utils import np_utils
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import Flatten
max_features = 15000
text_max_words = 120
# 1. 生成数据集
# 调用训练集和测试集
(x_train, y_train), (x_test, y_test) = reuters.load_data(num_words=max_features)

from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# 分离训练集和验证集
x_val = x_train[12000:]
y_val = y_train[12000:]
x_train = x_train[:12000]
y_train = y_train[:12000]
# 数据集预处理：统一句子长度
x_train = sequence.pad_sequences(x_train, maxlen=text_max_words)
x_val = sequence.pad_sequences(x_val, maxlen=text_max_words)
x_test = sequence.pad_sequences(x_test, maxlen=text_max_words)

# 2. 搭建模型
model = Sequential()
model.add(Embedding(max_features, 128, input_length=text_max_words))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(46, activation='softmax'))
# 3. 设置模型训练过程
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 4. 训练模型
hist = model.fit(x_train, y_train, epochs=20, batch_size=64, validation_data=(x_test, y_test))

# 5. 查看训练过程
%matplotlib inline
import matplotlib.pyplot as plt
fig, loss_ax = plt.subplots()
56
acc_ax = loss_ax.twinx()
loss_ax.plot(hist.history['loss'], 'b', label='train loss')
loss_ax.plot(hist.history['val_loss'], 'r', label='val loss')
loss_ax.set_ylim([0.0, 3.3])
acc_ax.plot(hist.history['accuracy'], 'black', label='train acc')
acc_ax.plot(hist.history['val_accuracy'], 'g', label='val acc')
acc_ax.set_ylim([0.0, 1.3])
loss_ax.set_xlabel('epoch')
loss_ax.set_ylabel('loss')
acc_ax.set_ylabel('accuracy')
loss_ax.legend(loc='upper left')
acc_ax.legend(loc='upper right')
plt.show()
# 6. 评价模型
loss_and_metrics = model.evaluate(x_test, y_test, batch_size=64)
print('## evaluation loss and_metrics ##')
print(loss_and_metrics)

# 循环神经网络模型
model = Sequential()
model.add(Embedding(max_features, 128))
model.add(LSTM(128))
model.add(Dense(46, activation='softmax'))

# 卷积神经网络模型
model = Sequential()
model.add(Embedding(max_features, 128, input_length=text_max_words))
model.add(Dropout(0.2))
model.add(Conv1D(256,3,padding='valid',activation='relu',strides=1))
model.add(GlobalMaxPooling1D())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(46, activation='softmax'))

#循环卷积神经网络模型
model = Sequential()
model.add(Embedding(max_features, 128, input_length=text_max_words))
model.add(Dropout(0.2))
model.add(Conv1D(256,3,padding='valid',activation='relu',strides=1))
model.add(MaxPooling1D(pool_size=4))
model.add(LSTM(128))
model.add(Dense(46, activation='softmax'))