# -*- coding: utf-8 -*-
"""
@author: Jason Zhang
@github: https://github.com/JasonZhang156/Sound-Recognition-Tutorial
"""
from keras.callbacks import TensorBoard, ModelCheckpoint, LearningRateScheduler
import tensorflow as tf
import keras.backend as K
import esc10_input
import numpy as np
import models
import os
def use_gpu():
"""Configuration for GPU"""
from keras.backend.tensorflow_backend import set_session
os.environ['CUDA_VISIBLE_DEVICES'] = str(0) # 使用第一台GPU
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5 # GPU使用率为50%
config.gpu_options.allow_growth = True # 允许容量增长
set_session(tf.InteractiveSession(config=config))
def CNN_train(test_fold, feat):
"""
Training CNN using extracted feature
:param test_fold: test fold of 5-fold cross validation
:param feat: which feature to use
"""
# 学习率衰减策略，每20个epoch衰减一次，变为0.1倍。
def scheduler(epoch):
if epoch in [20, 40]:
lr = K.get_value(model.optimizer.lr)
K.set_value(model.optimizer.lr, lr * 0.1)
print("lr changed to {}".format(lr * 0.1))
return K.get_value(model.optimizer.lr)
# 读取特征数据
train_features, train_labels, test_features, test_labels = esc10_input.get_data(test_fold, feat)
# 一些超参的配置
epoch = 60
num_class = 10
batch_size = 32
input_shape = (60, 65, 1)
# 构建CNN模型
model = models.CNN(input_shape, num_class)
# 回调函数
reduce_lr = LearningRateScheduler(scheduler) # 学习率衰减
logs = TensorBoard(log_dir='./log/fold{}/'.format(test_fold)) # 保存模型训练日志
checkpoint = ModelCheckpoint('./saved_model/cnn_{}_fold{}_best.h5'.format(feat, test_fold), # 保存在验证集上性能最好的模型
monitor='val_acc', verbose=1, save_best_only=True, mode='max', period=1)
# 训练模型
model.fit(train_features, train_labels, batch_size=batch_size, nb_epoch=epoch, verbose=1, validation_split=0.1,
callbacks=[checkpoint, reduce_lr, logs])
# 保存模型
model.save('./saved_model/cnn_{}_fold{}.h5'.format(feat, test_fold))
# 输出训练好的模型在测试集上的表现
score = model.evaluate(test_features, test_labels)
print('Test score:', score[0])
print('Test accuracy:', score[1])
return score[1]
if __name__ == '__main__':
use_gpu() # 使用GPU
dict_acc = {} # results of each fold
# 5-fold cross validation
print('### [Start] Test model for ESC10 dataset #####')
for fold in [1, 2, 3, 4, 5]:
print("## Start test fold{} model #####".format(fold))
acc = CNN_train(fold, 'mfcc')
dict_acc['fold{}'.format(fold)] = acc
print("## Finish test fold{} model #####".format(fold))
dict_acc['mean'] = np.mean(list(dict_acc.values()))
print(dict_acc)
print('### [Finish] Test model finished for ESC10 dataset #####')
