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DenseNet.py
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DenseNet.py
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# -*- coding:utf-8 -*-
# author: Huang Zilong
# 训练模型
import time
start = time.time()
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Dense , Dropout , Input , Flatten
from keras.layers import Conv2D , MaxPooling2D , Activation , regularizers , concatenate,AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.optimizers import SGD , Adadelta , Adam
from keras.utils import to_categorical
from DCASE2018_1 import same_step
from DCASE2016 import confusion_matrix
# 导入数据集，划分训练集、验证集、测试集
train_x, train_y,test_x, test_y = same_step.same_step()
# 将特征转换成三维数据,将标签转为需要的格式（one-hot 编码）
train_x = train_x.reshape(train_x.shape[0],train_x.shape[1],train_x.shape[2],1)
test_x = test_x.reshape(test_x.shape[0],test_x.shape[1],test_x.shape[2],1)
train_y = to_categorical(train_y)
test_y = to_categorical(test_y)
def dense_block(input_tensor, channels):
conv1 = Conv2D(filters=3 * channels, kernel_size=[3, 3], padding='same', kernel_initializer='he_normal')(input_tensor)
bn1 = BatchNormalization()(conv1)
relu = Activation(activation='relu')(bn1)
conv2 = Conv2D(filters=1 * channels, kernel_size=[3, 3], padding='same', kernel_initializer='he_normal')(relu)
bn2 = BatchNormalization()(conv2)
relu2 = Activation(activation='relu')(bn2)
return relu2
def transition_layer(input_tensor , channels):
conv = Conv2D(filters=channels , kernel_size=[1, 1])(input_tensor)
pool = MaxPooling2D(pool_size=[2, 2], strides=[2, 2])(conv)
return pool
def creat_model():
k = 24
input_data = Input(shape=[216, 174, 1] , name='Input')
conv1 = Conv2D(filters=k * 1 , kernel_size=[5 , 5] )(input_data)
# pool1 = MaxPooling2D(pool_size=[2 , 2] , strides=[2 , 1])(conv1)
# pool1 =Dropout(0.1)(pool1)
# conv2 = Conv2D(filters=k * 2, kernel_size=[4, 1])(pool1)
# pool2 = MaxPooling2D(pool_size=[2, 2], strides=[2, 1])(conv2)
# pool2 = Dropout(0.1)(pool2)
x = MaxPooling2D(pool_size=[3, 3], strides=[3, 3])(conv1)
b1_1 = dense_block(x, k)
b1_1_conc = concatenate([x, b1_1], axis=-1)
b1_1_conc = Dropout(0.1)(b1_1_conc)
b1_2 = dense_block(b1_1_conc, k,)
b1_2_conc = concatenate([x, b1_1, b1_2], axis=-1)
# b1_2_conc = Dropout(0.1)(b1_2_conc)
# b1_3 = dense_block(b1_2_conc, k)
# b1_3_conc = concatenate([x, b1_1, b1_2, b1_3], axis=-1)
# b1_4 = dense_block(b1_3_conc, k)
# b1_4_conc = concatenate([x, b1_1, b1_2, b1_3, b1_4], axis=-1)
# b1_5 = dense_block(b1_4_conc, k)
# b1_5_conc = concatenate([b1_1, b1_2, b1_3, b1_4, b1_5], axis=-1)
transion_1 = transition_layer(b1_2_conc, k)
b2_1 = dense_block(transion_1, k)
b2_1_conc = concatenate([transion_1, b2_1], axis=-1)
b2_1_conc = Dropout(0.1)(b2_1_conc)
b2_2 = dense_block(b2_1_conc, k)
b2_2_conc = concatenate([transion_1, b2_1, b2_2], axis=-1)
# b2_3 = dense_block(b2_2_conc, k)
# b2_3_conc = concatenate([transion_1, b2_1, b2_2, b2_3], axis=-1)
# b2_4 = dense_block(b2_3_conc, k)
# b2_4_conc = concatenate([transion_1, b2_1, b2_2, b2_3, b2_4], axis=-1)
# b2_5 = dense_block(b2_4_conc, k)
# b2_5_conc = concatenate([b2_1, b2_2, b2_3, b2_4, b2_5], axis=-1)
transion_2 = transition_layer(b2_2_conc, k)
b3_1 = dense_block(transion_2, k)
b3_1_conc = concatenate([transion_2, b3_1], axis=-1)
b3_1_conc = Dropout(0.1)(b3_1_conc)
b3_2 = dense_block(b3_1_conc, k)
b3_2_conc = concatenate([transion_2, b3_1, b3_2], axis=-1)
# b3_3 = dense_block(b3_2_conc, k)
# b3_3_conc = concatenate([transion_2, b3_1, b3_2, b3_3], axis=-1)
# # # b3_4 = dense_block(b3_3_conc, k)
# # # b3_4_conc = concatenate([transion_2, b2_1, b2_2, b2_3, b2_4], axis=-1)
# # # b3_5 = dense_block(b3_4_conc, k)
# # # b3_5_conc = concatenate([b3_1, b3_2, b3_3, b3_4, b3_5], axis=-1)
transion_3 = transition_layer(b3_2_conc, k)
dense = Flatten()(transion_3)
dense2 = Dense(units=512 , use_bias=True , kernel_initializer='he_normal' ,
activation='relu' , kernel_regularizer=regularizers.l2(1e-7))(dense)
dense3 = Dropout(0.6)(dense2)
dense4 = Dense(units=10)(dense3)
prediction = Activation(activation='softmax')(dense4)
model = Model(inputs=input_data , outputs=prediction)
model.summary(line_length=150)
# plot_model(model , 'DenseNet frame.png' , show_shapes=True)
from keras.callbacks import ModelCheckpoint
filepath = "D:\\TUT\\DCASE2018_1\\Model\\weights-improvement-{epoch:02d}-{val_acc:.4f}.h5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True,
mode='max')
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=train_x, y=train_y, batch_size=32, epochs=50, validation_data=(test_x, test_y), callbacks=[checkpoint])
# loss, evaluate = model.evaluate(x=test_x, y=test_y)
# print('Test Accuracy : ', evaluate)
# model.save('DenseNet_model1.h5')
# loss_acc.show_history(history)
# y = model.predict(x=test_x)
# confusion_matrix.confusion_matrix_1(y, test_y)
if __name__ == '__main__':
creat_model()
# 计算程序运行时间
end = time.time()
print("Running time(min)：", (end - start) // 60)