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RecurrentNN.py
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RecurrentNN.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2023/1/5 22:57
# @Author : 2012289 王麒翔
# @File : RecurrentNN.py
# 循环连接网络
import warnings
import tensorflow as tf
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.datasets import cifar10
from keras.utils import np_utils
# 保存模型路径
MODEL_FILEPATH = 'saved_models/RNN_cifar10.h5'
# 训练epoch
TRAIN_EPOCH = 65
# 批大小
BATCH_SIZE = 256
# 获取CIFAR-10数据集的数据
def get_data():
# x_train_original和y_train_original代表训练集的图像与标签, x_test_original与y_test_original代表测试集的图像与标签
(x_train_original, y_train_original), (x_test_original, y_test_original) = cifar10.load_data()
# 数据集分配：val代表验证集 test代表测试集 train代表训练集
x_val = x_test_original[:5000]
y_val = y_test_original[:5000]
x_test = x_test_original[5000:]
y_test = y_test_original[5000:]
x_train = x_train_original
y_train = y_train_original
# 这里把数据从uint类型转化为float32类型, 提高训练精度。
x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
x_test = x_test.astype('float32')
# 原始图像的像素灰度值为0-255，为了提高模型的训练精度，将数值归一化映射到0-1。
x_train = x_train / 255.0
x_val = x_val / 255.0
x_test = x_test / 255.0
# 图像标签一共有10个类别即0-9，这里将其转化为独热编码（One-hot）向量
y_train = np_utils.to_categorical(y_train)
y_val = np_utils.to_categorical(y_val)
y_test = np_utils.to_categorical(y_test)
return x_train, y_train, x_val, y_val, x_test, y_test
# 建立神经网络模型
def create_model():
model = Sequential()
# 网络结构 模型图可见于报告对应部分
model.add(tf.keras.layers.Reshape((32, 32 * 3), input_shape=(32, 32, 3)))
model.add(tf.keras.layers.SimpleRNN(128, return_sequences=True))
model.add(tf.keras.layers.SimpleRNN(256, return_sequences=True))
model.add(tf.keras.layers.SimpleRNN(128))
model.add((tf.keras.layers.Dropout(0.25)))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation='relu'))
model.add(tf.keras.layers.Dense(10, activation='softmax'))
# 打印网络结构
print(model.summary())
return model
# 训练模型
def train_model(model):
# 获取所需数据集
x_train, y_train, x_val, y_val, x_test, y_test = get_data()
# 编译网络（定义损失函数、优化器、评估指标）
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 开始网络训练（定义训练数据与验证数据、定义训练代数，定义训练批大小）
train_history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=TRAIN_EPOCH,
batch_size=BATCH_SIZE, verbose=1)
# 模型保存
model.save(MODEL_FILEPATH)
# 返回训练历史
return train_history
# 可视化展示训练历史
def show_train_history(train_history, train, validation):
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel(train)
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='best')
plt.show()
# 在测试集上做预测并计算测试集准确率
def show_res(model):
x_train, y_train, x_val, y_val, x_test, y_test = get_data()
score = model.evaluate(x_test, y_test)
print('循环连接网络Loss:', score[0])
print('循环连接网络Accuracy:', score[1])
if __name__ == "__main__":
warnings.filterwarnings(action='ignore')
# 根据模型是否创建采取不同方法
try:
model = tf.keras.models.load_model(MODEL_FILEPATH)
print("循环连接网络模型已创建，直接载入...")
except:
model = create_model()
print("循环连接网络模型未创建，首先创建...")
# 训练模型并获取训练历史
train_history = train_model(model)
# 展示训练历史
show_train_history(train_history, 'accuracy', 'val_accuracy')
show_train_history(train_history, 'loss', 'val_loss')
# 展示模型结果
show_res(model)