本站内容均来自兴趣收集,如不慎侵害的您的相关权益,请留言告知,我们将尽快删除.谢谢.
Keras深度学习实战——基于VGG19模型实现性别分类
0. 前言
在《迁移学习》中，我们了解了利用迁移学习，只需要少量样本即可训练得到性能较好的模型；并基于迁移学习利用预训练的 VGG16 模型进行了性别分类的实战，进一步加深对迁移学习工作原理的理解。
1. VGG19 架构简介
本文，我们将介绍另一种常用的网络模型架构—— VGG19 ，并使用预训练的 VGG19 模型进行性别分类实战。 VGG19 是 VGG16 的改进版本，具有更多的卷积和池化操作， VGG19 模型的体系结构如下：
Model: "vgg19" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_1 (InputLayer) [(None, 256, 256, 3)] 0 _________________________________________________________________ block1_conv1 (Conv2D) (None, 256, 256, 64) 1792 _________________________________________________________________ block1_conv2 (Conv2D) (None, 256, 256, 64) 36928 _________________________________________________________________ block1_pool (MaxPooling2D) (None, 128, 128, 64) 0 _________________________________________________________________ block2_conv1 (Conv2D) (None, 128, 128, 128) 73856 _________________________________________________________________ block2_conv2 (Conv2D) (None, 128, 128, 128) 147584 _________________________________________________________________ block2_pool (MaxPooling2D) (None, 64, 64, 128) 0 _________________________________________________________________ block3_conv1 (Conv2D) (None, 64, 64, 256) 295168 _________________________________________________________________ block3_conv2 (Conv2D) (None, 64, 64, 256) 590080 _________________________________________________________________ block3_conv3 (Conv2D) (None, 64, 64, 256) 590080 _________________________________________________________________ block3_conv4 (Conv2D) (None, 64, 64, 256) 590080 _________________________________________________________________ block3_pool (MaxPooling2D) (None, 32, 32, 256) 0 _________________________________________________________________ block4_conv1 (Conv2D) (None, 32, 32, 512) 1180160 _________________________________________________________________ block4_conv2 (Conv2D) (None, 32, 32, 512) 2359808 _________________________________________________________________ block4_conv3 (Conv2D) (None, 32, 32, 512) 2359808 _________________________________________________________________ block4_conv4 (Conv2D) (None, 32, 32, 512) 2359808 _________________________________________________________________ block4_pool (MaxPooling2D) (None, 16, 16, 512) 0 _________________________________________________________________ block5_conv1 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________ block5_conv2 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________ block5_conv3 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________ block5_conv4 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________ block5_pool (MaxPooling2D) (None, 8, 8, 512) 0 ================================================================= Total params: 20,024,384 Trainable params: 20,024,384 Non-trainable params: 0 _________________________________________________________________
可以看到，上示的体系结构中具有更多的网络层以及更多的参数量。需要注意的是， VGG16 和 VGG19 体系结构中的 16 和 19 代表这些网络中的网络层数。
将每个图像通过 VGG19 网络后，提取到 8 x 8 x 512 输出后，该输出将成为微调模型的输入。接下来，创建输入和输出数据集，然后构建、编译和拟合模型的过程与 使用基于预训练的 VGG16 模型进行性别分类 的过程相同。
2. 使用预训练 VGG19 模型进行性别分类
在本节中，我们基于迁移学习使用预训练的 VGG19 模型进行性别分类。
2.1 构建输入与输出数据
首先，准备输入和输出数据，我们重用在 《卷积神经网络进行性别分类》 中使用的数据集以及数据加载代码：
from keras.applications import VGG19
from keras.applications.vgg19 import preprocess_input
from glob import glob
from skimage import io
import cv2
import numpy as np
model = VGG19(include_top=False, weights='imagenet', input_shape=(256, 256, 3))
x = []
y = []
for i in glob('man_woman/a_resized/*.jpg')[:800]:
try:
image = io.imread(i)
x.append(image)
y.append(0)
except:
continue
for i in glob('man_woman/b_resized/*.jpg')[:800]:
try:
image = io.imread(i)
x.append(image)
y.append(1)
except:
continue
x_vgg19 = []
for i in range(len(x)):
img = x[i]
img = preprocess_input(img.reshape((1, 256, 256, 3)))
img_feature = model.predict(img)
x_vgg19.append(img_feature)
将输入和输出转换为其相应的数组，并创建训练和测试数据集：
x_vgg19 = np.array(x_vgg19) x_vgg19 = x_vgg19.reshape(x_vgg19.shape[0], x_vgg19.shape[2], x_vgg19.shape[3], x_vgg19.shape[4]) y = np.array(y) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x_vgg19, y, test_size=0.2)
2.2 模型构建与训练
构建微调模型：
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dropout, Dense
model_fine_tuning = Sequential()
model_fine_tuning.add(Conv2D(512,
kernel_size=(3, 3),
activation='relu',
input_shape=(x_train.shape[1], x_train.shape[2], x_train.shape[3])))
model_fine_tuning.add(MaxPooling2D(pool_size=(2, 2)))
model_fine_tuning.add(Flatten())
model_fine_tuning.add(Dense(1024, activation='relu'))
model_fine_tuning.add(Dropout(0.6))
model_fine_tuning.add(Dense(1, activation='sigmoid'))
model_fine_tuning.summary()
该模型架构的简要信息输入如下：
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d (Conv2D) (None, 6, 6, 512) 2359808 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 3, 3, 512) 0 _________________________________________________________________ flatten (Flatten) (None, 4608) 0 _________________________________________________________________ dense (Dense) (None, 1024) 4719616 _________________________________________________________________ dropout (Dropout) (None, 1024) 0 _________________________________________________________________ dense_1 (Dense) (None, 1) 1025 ================================================================= Total params: 7,080,449 Trainable params: 7,080,449 Non-trainable params: 0 _________________________________________________________________
接下来，编译并拟合模型：
model_fine_tuning.compile(loss='binary_crossentropy',optimizer='adam',metrics=['acc'])
history = model_fine_tuning.fit(x_train, y_train,
batch_size=32,
epochs=20,
verbose=1,
validation_data = (x_test, y_test))
最后，我们绘制在训练期间，模型在训练和测试数据集的损失和准确率的变化。可以看到，当我们使用 VGG19 架构时，能够在测试数据集上达到约 95％ 的准确率，结果与使用 VGG16 架构时的性能相似：
2.3 模型错误分类示例
一些错误分类的图像示例如下：
x = np.array(x)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
x_test_vgg19 = []
for i in range(len(x_test)):
img = x_test[i]
img = preprocess_input(img.reshape((1, 256, 256, 3)))
img_feature = model.predict(img)
x_test_vgg19.append(img_feature)
x_test_vgg19 = np.array(x_test_vgg19)
x_test_vgg19 = x_test_vgg19.reshape(x_test_vgg19.shape[0], x_test_vgg19.shape[2], x_test_vgg19.shape[3], x_test_vgg19.shape[4])
y_pred = model_fine_tuning.predict(x_test_vgg19)
wrong = np.argsort(np.abs(y_pred.flatten()-y_test))
print(wrong)
y_test_char = np.where(y_test==0,'M','F')
y_pred_char = np.where(y_pred>0.5,'F','M')
plt.subplot(221)
plt.imshow(x_test[wrong[-1]])
plt.title('Actual: '+str(y_test_char[wrong[-1]])+', '+'Predicted: '+str((y_pred_char[wrong[-1]][0])))
plt.subplot(222)
plt.imshow(x_test[wrong[-2]])
plt.title('Actual: '+str(y_test_char[wrong[-2]])+', '+'Predicted: '+str((y_pred_char[wrong[-2]][0])))
plt.subplot(223)
plt.imshow(x_test[wrong[-3]])
plt.title('Actual: '+str(y_test_char[wrong[-3]])+', '+'Predicted: '+str((y_pred_char[wrong[-3]][0])))
plt.subplot(224)
plt.imshow(x_test[wrong[-4]])
plt.title('Actual: '+str(y_test_char[wrong[-4]])+', '+'Predicted: '+str((y_pred_char[wrong[-4]][0])))
plt.show()
从图中，可以看出， VGG19 类似于 VGG16 除了由于人物在图像中占据的空间较小造成错误分类外，倾向于根据头发来判断人物究竟是男性还是女性。
Be First to Comment