一、实验目的:
理解循环神经网络的基本概念和原理;了解循环神经网络处理文本数据的基本方法;掌握循环神经网络处理文本数据的实践方法,并实现文本情感分析任务。
- 实验要求:
使用Keras框架定义并训练循环神经网络模型,并进行文本情感分析。
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
# 加载 IMDB 数据
imdb = keras.datasets.imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
print("训练记录数量:{},标签数量:{}".format(len(train_data), len(train_labels)))
print(train_data[0])
# 数据标准化
train_data = keras.preprocessing.sequence.pad_sequences(train_data, padding='post', maxlen=256)
test_data = keras.preprocessing.sequence.pad_sequences(test_data, padding='post', maxlen=256)
print(train_data[0])
# 构建模型
vocab_size = 10000
model = tf.keras.Sequential([tf.keras.layers.Embedding(vocab_size, 64),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)), tf.keras.
layers.Dense(64, activation='relu'), tf.keras.layers.Dense(1)
])
model.summary()
# 配置并训练模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
x_val = train_data[:10000]
partial_x_train = train_data[10000:]
y_val = train_labels[:10000]
partial_y_train = train_labels[10000:]
history = model.fit(partial_x_train, partial_y_train, epochs=10, batch_size=512, validation_data=(x_val, y_val),
verbose=1)
result = model.evaluate(test_data, test_labels, verbose=2)
print(result)
# 训练过程可视化
history_dict = history.history
print(history_dict.keys())
def plot_graphs(history, string):
plt.plot(history.history[string])
plt.plot(history.history['val_' + string])
plt.xlabel("Epochs")
plt.ylabel(string)
plt.legend([string, 'val_' + string])
plt.show()
plot_graphs(history, "accuracy")
plot_graphs(history, "loss")
运行结果可视化: