卷积神经网络交通标志识别
2024-05-15 05:05:31
这节采用德国交通标志数据集实现交通标志识别。本节步骤分为数据导入，探索和可视化数据集，数据预处理，交通标志识别模型搭建。
一、加载数据
# 加载数据
import pickle# 数据集所在的文件位置
training_file = "/content/drive/traffic-signs-data/train.p"
validation_file = "/content/drive/traffic-signs-data/valid.p"
testing_file = "/content/drive/traffic-signs-data/test.p"# 打开文件
with open(training_file,mode="rb") as f:train = pickle.load(f)
with open(validation_file,mode="rb") as f:valid = pickle.load(f)
with open(testing_file,mode="rb") as f:test = pickle.load(f)# 获取数据集的特征及标签数据
X_train,y_train = train["features"],train["labels"]
X_valid,y_valid = valid["features"],valid["labels"]
X_test,y_test = test["features"],test["labels"]
# 查看数据量
print("Number of training examples =",X_train.shape[0])
print("Number of validtion examples =",X_valid.shape[0])
print("Number of testing examples=",X_test.shape[0])# 查看数据格式
print("Image data shape =",X_train.shape[1:])
Number of training examples = 34799
Number of validtion examples = 4410
Number of testing examples= 12630
Image data shape = (32, 32, 3)
# 查看数据的标签的数量
import numpy as npsum = np.unique(y_train)
print("number of classes =",len(sum))
number of classes = 43
# 查看标签数据
import pandas as pdsign_names_file = "/content/drive/traffic-signs-data/signnames.csv"
sign_names = pd.read_csv(sign_names_file)
print(sign_names)
ClassId SignName
0 0 Speed limit (20km/h)
1 1 Speed limit (30km/h)
2 2 Speed limit (50km/h)
3 3 Speed limit (60km/h)
4 4 Speed limit (70km/h)
5 5 Speed limit (80km/h)
6 6 End of speed limit (80km/h)
7 7 Speed limit (100km/h)
8 8 Speed limit (120km/h)
9 9 No passing
10 10 No passing for vehicles over 3.5 metric tons
11 11 Right-of-way at the next intersection
12 12 Priority road
13 13 Yield
14 14 Stop
15 15 No vehicles
16 16 Vehicles over 3.5 metric tons prohibited
17 17 No entry
18 18 General caution
19 19 Dangerous curve to the left
20 20 Dangerous curve to the right
21 21 Double curve
22 22 Bumpy road
23 23 Slippery road
24 24 Road narrows on the right
25 25 Road work
26 26 Traffic signals
27 27 Pedestrians
28 28 Children crossing
29 29 Bicycles crossing
30 30 Beware of ice/snow
31 31 Wild animals crossing
32 32 End of all speed and passing limits
33 33 Turn right ahead
34 34 Turn left ahead
35 35 Ahead only
36 36 Go straight or right
37 37 Go straight or left
38 38 Keep right
39 39 Keep left
40 40 Roundabout mandatory
41 41 End of no passing
42 42 End of no passing by vehicles over 3.5 metric ...
# 定义将标签id转换成name的函数
sign_names = np.array(sign_names)
def id_to_name(id):return sign_names[id][1]
# 验证是否id_to_name函数
id_to_name(0)
'Speed limit (20km/h)'
# 绘制交通标志图
import matplotlib.pyplot as plt
%matplotlib inlinefig,axes = plt.subplots(2,5,figsize=(18,5))
ax_array = axes.ravel()
for ax in ax_array:index = np.random.randint(0,len(X_train))ax.imshow(X_train[index])ax.axis("off")ax.set_title(id_to_name(y_train[index]))
plt.show()
# 直方图来展示图像训练集的各个类别的分布情况
n_classes = len(sum)
def plot_y_train_hist():fig = plt.figure(figsize=(15,5))ax = fig.add_subplot(1,1,1)hist = ax.hist(y_train,bins = n_classes)ax.set_title("the frequency of each category sign")ax.set_xlabel("signs")ax.set_ylabel("frequency")plt.show()return histprint(X_train.shape)
print(y_train.shape)
hist = plot_y_train_hist()
(34799, 32, 32, 3)
(34799,)
# 数据重采样，使样本个数分配均匀
bin_edges = hist[1]
bin_centers = (bin_edges[1:] + bin_edges[0:len(bin_edges)-1])/2
for i in range(len(bin_centers)):if hist[0][i] < 1000:train_data = [X_train[j] for j in range(len(y_train)) if y_train[j] == i]need_resample_num = int(1000 - hist[0][i])new_data_x = [np.copy(train_data[np.random.randint(len(train_data))]) for k in range(need_resample_num)]new_data_y = [i for x in range(need_resample_num)]X_train = np.vstack((X_train, np.array(new_data_x)))y_train = np.hstack((y_train, np.array(new_data_y)))
print(X_train.shape)
print(y_train.shape)
plot_y_train_hist()
(51690, 32, 32, 3)
(51690,)
(array([1000., 1980., 2010., 1260., 1770., 1650., 1000., 1290., 1260.,1320., 1800., 1170., 1890., 1920., 1000., 1000., 1000., 1000.,1080., 1000., 1000., 1000., 1000., 1000., 1000., 1350., 1000.,1000., 1000., 1000., 1000., 1000., 1000., 1000., 1000., 1080.,1000., 1000., 1860., 1000., 1000., 1000., 1000.]),array([ 0. , 0.97674419, 1.95348837, 2.93023256, 3.90697674,4.88372093, 5.86046512, 6.8372093 , 7.81395349, 8.79069767,9.76744186, 10.74418605, 11.72093023, 12.69767442, 13.6744186 ,14.65116279, 15.62790698, 16.60465116, 17.58139535, 18.55813953,19.53488372, 20.51162791, 21.48837209, 22.46511628, 23.44186047,24.41860465, 25.39534884, 26.37209302, 27.34883721, 28.3255814 ,29.30232558, 30.27906977, 31.25581395, 32.23255814, 33.20930233,34.18604651, 35.1627907 , 36.13953488, 37.11627907, 38.09302326,39.06976744, 40.04651163, 41.02325581, 42. ]),<a list of 43 Patch objects>)
二、数据预处理
# 源图像
src = X_train[4000]
plt.imshow(src)
print(src.shape)
(32, 32, 3)
# 转为灰度图像
import cv2dst = cv2.cvtColor(src,cv2.COLOR_RGB2GRAY)
plt.imshow(dst)
print(dst.shape)
(32, 32)
# 灰度图的直方图
plt.hist(dst.ravel(),256,[0,256],color="r")
plt.show()
# 灰度图的直方图均衡化
dst2 = cv2.equalizeHist(dst)
plt.hist(dst2.ravel(),256,[0,256],color="r")
plt.show()
# 查看直方图均衡化的图片
plt.imshow(dst2)
<matplotlib.image.AxesImage at 0x7f97bb3a86a0>
# 增加维度
print(dst2.shape)
dst3 = np.expand_dims(dst2,2)
print(dst3.shape)
(32, 32)
(32, 32, 1)
# 归一化处理
dst4 = np.array(dst3,dtype=np.float32)
dst4 = (dst4-128)/128
# 查看一下归一化处理后的图像数据
dst4[0]
array([[-0.859375 ],[-0.9609375],[-0.9765625],[-0.9296875],[-0.859375 ],[-0.859375 ],[-0.625 ],[-0.484375 ],[-0.625 ],[-0.625 ],[-0.375 ],[-0.375 ],[-0.2890625],[ 0.171875 ],[ 0.6171875],[ 0.3359375],[-0.1171875],[ 0.171875 ],[ 0.3359375],[ 0.171875 ],[-0.0546875],[ 0.125 ],[ 0.125 ],[ 0.0078125],[ 0.0078125],[ 0.125 ],[-0.1171875],[-0.1875 ],[-0.1875 ],[ 0.0625 ],[ 0.125 ],[ 0.171875 ]], dtype=float32)
# 封装数据预处理的方法
import cv2def preprocess_features(X, equalize_hist=True):normalized_X = []for i in range(len(X)):# Convert from RGB to YUVyuv_img = cv2.cvtColor(X[i], cv2.COLOR_RGB2YUV)yuv_img_v = X[i][:, :, 0]# equalizeHistyuv_img_v = cv2.equalizeHist(yuv_img_v)# expand_disyuv_img_v = np.expand_dims(yuv_img_v, 2)normalized_X.append(yuv_img_v)# normalizenormalized_X = np.array(normalized_X, dtype=np.float32)normalized_X = (normalized_X-128)/128# normalized_X /= (np.std(normalized_X, axis=0) + np.finfo('float32').eps)return normalized_X
# 对数据集整体进行处理
X_train_normalized = preprocess_features(X_train)
X_valid_normalized = preprocess_features(X_valid)
X_test_normalized = preprocess_features(X_test)
# 将数据集打乱
from sklearn.utils import shuffle
X_train_normalized,y_train = shuffle(X_train_normalized,y_train)
# 数据增强
from keras.preprocessing.image import ImageDataGenerator# 图像数据生成器
image_datagen = ImageDataGenerator(rotation_range = 10.,zoom_range = 0.2,width_shift_range = 0.08,height_shift_range = 0.08)# 从训练集随意选取一张图片
index = np.random.randint(0, len(X_train_normalized))
img = X_train_normalized[index]# 展示原始图片
plt.figure(figsize=(1, 1))
plt.imshow(np.squeeze(img), cmap="gray")
plt.title('Example of GRAY image (name = {})'.format(id_to_name(y_train[index])))
plt.axis('off')
plt.show()# 展示数据增强生成的图片
fig, ax_array = plt.subplots(3, 10, figsize=(15, 5))
for ax in ax_array.ravel():images = np.expand_dims(img, 0)# np.expand_dims(img, 0) means add dimaugmented_img, _ = image_datagen.flow(np.expand_dims(img, 0), np.expand_dims(y_train[index], 0)).next()#augmented_img=preprocess_features(augmented_img)ax.imshow(augmented_img.squeeze(), cmap="gray")ax.axis('off')
plt.suptitle('Random examples of data augment (starting from the previous image)')
plt.show()
Using TensorFlow backend.
print("Number of training examples =",X_train.shape[0])
Number of training examples = 51690
# 对标签数据进行one-hot编码
from keras.utils import np_utils
print("Shape before one-hot encoding:",y_train.shape)
Y_train = np_utils.to_categorical(y_train,n_classes)
Y_valid = np_utils.to_categorical(y_valid,n_classes)
Y_test = np_utils.to_categorical(y_test,n_classes)
print("Shape after one-hot encoding:",Y_train.shape)
Shape before one-hot encoding: (51690,)
Shape after one-hot encoding: (51690, 43)
print(y_train[0])
print(Y_train[0])
24
[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
三、交通标志牌的识别模型搭建
1.2层Conv2D+1层MaxPooling2D+1层Drop(0.25)+1层Flatten+1层Dense+1层Dropout层(0.5)+1层Dense
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2Dmodel = Sequential()
## Feature Extraction
# 第1层卷积，32个3x3的卷积核 ，激活函数使用 relu
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu',input_shape=X_train_normalized.shape[1:]))# 第2层卷积，64个3x3的卷积核，激活函数使用 relu
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))# 最大池化层，池化窗口 2x2
model.add(MaxPooling2D(pool_size=(2, 2)))# Dropout 25% 的输入神经元
model.add(Dropout(0.25))# 将 Pooled feature map 摊平后输入全连接网络
model.add(Flatten())## Classification
# 全联接层
model.add(Dense(128, activation='relu'))# Dropout 50% 的输入神经元
model.add(Dropout(0.5))# 使用 softmax 激活函数做多分类，输出各类别的概率
model.add(Dense(n_classes, activation='softmax'))
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
# 查看模型的结构
model.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
conv2d_2 (Conv2D) (None, 28, 28, 64) 18496
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 64) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 14, 14, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 12544) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 1605760
_________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 43) 5547
=================================================================
Total params: 1,630,123
Trainable params: 1,630,123
Non-trainable params: 0
_________________________________________________________________
# 编译模型
model.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
# 训练模型
history = model.fit(X_train_normalized,Y_train,batch_size=256,epochs=120,verbose=2,validation_data=(X_valid_normalized,Y_valid))
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.cast instead.
Train on 51690 samples, validate on 4410 samples
Epoch 1/120- 7s - loss: 1.2242 - acc: 0.6591 - val_loss: 0.3351 - val_acc: 0.8957
Epoch 2/120- 4s - loss: 0.2724 - acc: 0.9174 - val_loss: 0.2139 - val_acc: 0.9370
Epoch 3/120- 4s - loss: 0.1670 - acc: 0.9492 - val_loss: 0.1646 - val_acc: 0.9490
Epoch 4/120- 4s - loss: 0.1190 - acc: 0.9622 - val_loss: 0.1639 - val_acc: 0.9467
Epoch 5/120- 4s - loss: 0.0910 - acc: 0.9709 - val_loss: 0.1620 - val_acc: 0.9537
Epoch 6/120- 4s - loss: 0.0770 - acc: 0.9752 - val_loss: 0.1439 - val_acc: 0.9585
Epoch 7/120- 4s - loss: 0.0656 - acc: 0.9795 - val_loss: 0.1304 - val_acc: 0.9587
Epoch 8/120- 4s - loss: 0.0572 - acc: 0.9809 - val_loss: 0.1411 - val_acc: 0.9601
Epoch 9/120- 4s - loss: 0.0514 - acc: 0.9835 - val_loss: 0.1529 - val_acc: 0.9562
Epoch 10/120- 4s - loss: 0.0473 - acc: 0.9847 - val_loss: 0.1166 - val_acc: 0.9642
Epoch 11/120- 4s - loss: 0.0427 - acc: 0.9863 - val_loss: 0.1336 - val_acc: 0.9619
Epoch 12/120- 4s - loss: 0.0371 - acc: 0.9879 - val_loss: 0.1309 - val_acc: 0.9635
Epoch 13/120- 4s - loss: 0.0380 - acc: 0.9874 - val_loss: 0.1194 - val_acc: 0.9685
Epoch 14/120- 4s - loss: 0.0315 - acc: 0.9898 - val_loss: 0.1403 - val_acc: 0.9633
Epoch 15/120- 4s - loss: 0.0328 - acc: 0.9889 - val_loss: 0.1469 - val_acc: 0.9574
Epoch 16/120- 4s - loss: 0.0292 - acc: 0.9903 - val_loss: 0.1241 - val_acc: 0.9689
Epoch 17/120- 4s - loss: 0.0291 - acc: 0.9903 - val_loss: 0.1307 - val_acc: 0.9673
Epoch 18/120- 4s - loss: 0.0265 - acc: 0.9910 - val_loss: 0.1434 - val_acc: 0.9651
Epoch 19/120- 4s - loss: 0.0259 - acc: 0.9914 - val_loss: 0.1291 - val_acc: 0.9680
Epoch 20/120- 4s - loss: 0.0246 - acc: 0.9916 - val_loss: 0.1332 - val_acc: 0.9635
Epoch 21/120- 4s - loss: 0.0231 - acc: 0.9922 - val_loss: 0.1261 - val_acc: 0.9671
Epoch 22/120- 4s - loss: 0.0232 - acc: 0.9923 - val_loss: 0.1301 - val_acc: 0.9680
Epoch 23/120- 4s - loss: 0.0198 - acc: 0.9932 - val_loss: 0.1388 - val_acc: 0.9669
Epoch 24/120- 4s - loss: 0.0226 - acc: 0.9923 - val_loss: 0.1210 - val_acc: 0.9719
Epoch 25/120- 4s - loss: 0.0212 - acc: 0.9930 - val_loss: 0.1297 - val_acc: 0.9683
Epoch 26/120- 4s - loss: 0.0206 - acc: 0.9932 - val_loss: 0.1344 - val_acc: 0.9687
Epoch 27/120- 4s - loss: 0.0203 - acc: 0.9932 - val_loss: 0.1495 - val_acc: 0.9692
Epoch 28/120- 4s - loss: 0.0200 - acc: 0.9935 - val_loss: 0.1305 - val_acc: 0.9696
Epoch 29/120- 4s - loss: 0.0181 - acc: 0.9938 - val_loss: 0.1436 - val_acc: 0.9685
Epoch 30/120- 4s - loss: 0.0167 - acc: 0.9944 - val_loss: 0.1377 - val_acc: 0.9673- 7s - loss: 0.0072 - acc: 0.9977 - val_loss: 0.1146 - val_acc: 0.9823
Epoch 111/120- 7s - loss: 0.0059 - acc: 0.9981 - val_loss: 0.1531 - val_acc: 0.9741
Epoch 112/120- 7s - loss: 0.0065 - acc: 0.9977 - val_loss: 0.1157 - val_acc: 0.9805
Epoch 113/120- 7s - loss: 0.0061 - acc: 0.9980 - val_loss: 0.1638 - val_acc: 0.9717
Epoch 114/120- 7s - loss: 0.0055 - acc: 0.9983 - val_loss: 0.1301 - val_acc: 0.9794
Epoch 115/120- 7s - loss: 0.0041 - acc: 0.9985 - val_loss: 0.1501 - val_acc: 0.9773
Epoch 116/120- 5s - loss: 0.0066 - acc: 0.9976 - val_loss: 0.1437 - val_acc: 0.9757
Epoch 117/120- 4s - loss: 0.0064 - acc: 0.9979 - val_loss: 0.1508 - val_acc: 0.9780
Epoch 118/120- 4s - loss: 0.0066 - acc: 0.9978 - val_loss: 0.1489 - val_acc: 0.9769
Epoch 119/120- 4s - loss: 0.0065 - acc: 0.9980 - val_loss: 0.1378 - val_acc: 0.9766
Epoch 120/120- 4s - loss: 0.0059 - acc: 0.9982 - val_loss: 0.1255 - val_acc: 0.9789
# 可视化指标
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')plt.subplot(2,1,2)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.tight_layout()plt.show()
# 保存模型
import os
import tensorflow.gfile as gfilesave_dir = "/content/drive/model/v1/"if gfile.Exists(save_dir):gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)model_name = 'keras_traffic_1.h5'
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
Saved trained model at /content/drive/model/v1/keras_traffic_1.h5
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 统计模型在测试集上的分类结果
loss_and_metrics = traffic_model.evaluate(X_test_normalized, Y_test, verbose=2,batch_size=256)print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1]*100))predicted_classes = traffic_model.predict_classes(X_test_normalized)correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
print("Classified correctly count: {}".format(len(correct_indices)))
print("Classified incorrectly count: {}".format(len(incorrect_indices)))
Test Loss: 0.22923571624080058
Test Accuracy: 96.88836103380434%
Classified correctly count: 12237
Classified incorrectly count: 393
2.3层Con2D MaxPooling2D+1层Flatten层+2层Dense
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers import Conv2D,MaxPooling2Dmodel2 = Sequential()# layers1
model2.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=X_train_normalized.shape[1:],activation="relu"))
model2.add(MaxPooling2D(pool_size=(2,2)))# layers2
model2.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model2.add(MaxPooling2D(pool_size=(2,2)))# layers3
model2.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model2.add(MaxPooling2D(pool_size=(2,2)))# flattern
model2.add(Flatten())# Dense
model2.add(Dense(512,activation="relu"))# Dense
model2.add(Dense(n_classes,activation="softmax"))
# 查看模型的结构
model2.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_3 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
conv2d_5 (Conv2D) (None, 4, 4, 64) 36928
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 2, 2, 64) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 131584
_________________________________________________________________
dense_4 (Dense) (None, 43) 22059
=================================================================
Total params: 209,387
Trainable params: 209,387
Non-trainable params: 0
_________________________________________________________________
# 编译模型
model2.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
# 训练模型
history2 = model2.fit(X_train_normalized,Y_train,batch_size=256,epochs=120,verbose=2,validation_data=(X_valid_normalized,Y_valid))
Train on 51690 samples, validate on 4410 samples
Epoch 1/120- 2s - loss: 1.3340 - acc: 0.6319 - val_loss: 0.6667 - val_acc: 0.8041
Epoch 2/120- 2s - loss: 0.2375 - acc: 0.9308 - val_loss: 0.4388 - val_acc: 0.8762
Epoch 3/120- 2s - loss: 0.1122 - acc: 0.9669 - val_loss: 0.3823 - val_acc: 0.8964
Epoch 4/120- 2s - loss: 0.0769 - acc: 0.9775 - val_loss: 0.3509 - val_acc: 0.9036
Epoch 5/120- 2s - loss: 0.0508 - acc: 0.9857 - val_loss: 0.3434 - val_acc: 0.9088
Epoch 6/120- 2s - loss: 0.0420 - acc: 0.9874 - val_loss: 0.3413 - val_acc: 0.9204
Epoch 7/120- 2s - loss: 0.0297 - acc: 0.9912 - val_loss: 0.3266 - val_acc: 0.9243
Epoch 8/120- 2s - loss: 0.0198 - acc: 0.9943 - val_loss: 0.3150 - val_acc: 0.9256
Epoch 9/120- 2s - loss: 0.0205 - acc: 0.9940 - val_loss: 0.3463 - val_acc: 0.9200
Epoch 10/120- 2s - loss: 0.0174 - acc: 0.9947 - val_loss: 0.3045 - val_acc: 0.9236
Epoch 11/120- 2s - loss: 0.0194 - acc: 0.9943 - val_loss: 0.3561 - val_acc: 0.9184
Epoch 12/120- 2s - loss: 0.0109 - acc: 0.9968 - val_loss: 0.3475 - val_acc: 0.9259
Epoch 13/120- 2s - loss: 0.0116 - acc: 0.9964 - val_loss: 0.3646 - val_acc: 0.9270
Epoch 14/120- 2s - loss: 0.0127 - acc: 0.9958 - val_loss: 0.3486 - val_acc: 0.9295
Epoch 15/120- 2s - loss: 0.0090 - acc: 0.9975 - val_loss: 0.3234 - val_acc: 0.9297
Epoch 16/120- 2s - loss: 0.0118 - acc: 0.9962 - val_loss: 0.4056 - val_acc: 0.9236
Epoch 17/120- 2s - loss: 0.0082 - acc: 0.9976 - val_loss: 0.3282 - val_acc: 0.9345
Epoch 18/120- 2s - loss: 0.0066 - acc: 0.9982 - val_loss: 0.3327 - val_acc: 0.9381
Epoch 19/120- 2s - loss: 0.0089 - acc: 0.9971 - val_loss: 0.3708 - val_acc: 0.9338
Epoch 20/120- 2s - loss: 0.0109 - acc: 0.9967 - val_loss: 0.3011 - val_acc: 0.9397
Epoch 21/120- 2s - loss: 0.0030 - acc: 0.9993 - val_loss: 0.3431 - val_acc: 0.9406
Epoch 22/120- 2s - loss: 0.0121 - acc: 0.9965 - val_loss: 0.3113 - val_acc: 0.9413
Epoch 23/120- 2s - loss: 0.0068 - acc: 0.9981 - val_loss: 0.3195 - val_acc: 0.9431
Epoch 24/120- 2s - loss: 0.0021 - acc: 0.9995 - val_loss: 0.3079 - val_acc: 0.9447
Epoch 25/120- 2s - loss: 0.0080 - acc: 0.9973 - val_loss: 0.2953 - val_acc: 0.9408
Epoch 26/120- 2s - loss: 0.0086 - acc: 0.9971 - val_loss: 0.3595 - val_acc: 0.9315
Epoch 27/120- 2s - loss: 0.0105 - acc: 0.9967 - val_loss: 0.3286 - val_acc: 0.9342
Epoch 28/120- 2s - loss: 0.0039 - acc: 0.9990 - val_loss: 0.3357 - val_acc: 0.9363
Epoch 29/120- 2s - loss: 0.0038 - acc: 0.9992 - val_loss: 0.2930 - val_acc: 0.9440
Epoch 30/120- 2s - loss: 0.0053 - acc: 0.9985 - val_loss: 0.2465 - val_acc: 0.9537Epoch 110/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4051 - val_acc: 0.9519
Epoch 111/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4071 - val_acc: 0.9524
Epoch 112/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4111 - val_acc: 0.9524
Epoch 113/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4106 - val_acc: 0.9524
Epoch 114/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4115 - val_acc: 0.9522
Epoch 115/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4134 - val_acc: 0.9519
Epoch 116/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4157 - val_acc: 0.9510
Epoch 117/120- 2s - loss: 6.2377e-04 - acc: 1.0000 - val_loss: 0.4181 - val_acc: 0.9515
Epoch 118/120- 2s - loss: 6.2376e-04 - acc: 1.0000 - val_loss: 0.4188 - val_acc: 0.9517
Epoch 119/120- 2s - loss: 6.2376e-04 - acc: 1.0000 - val_loss: 0.4199 - val_acc: 0.9517
Epoch 120/120- 2s - loss: 6.2376e-04 - acc: 1.0000 - val_loss: 0.4196 - val_acc: 0.9510
# 可视化指标
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(history2.history['acc'])
plt.plot(history2.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')plt.subplot(2,1,2)
plt.plot(history2.history['loss'])
plt.plot(history2.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.tight_layout()plt.show()
# 保存模型
import os
import tensorflow.gfile as gfilesave_dir = "/content/drive/model/v2/"if gfile.Exists(save_dir):gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)model_name = 'keras_traffic_2.h5'
model_path = os.path.join(save_dir, model_name)model2.save(model_path)
print('Saved trained model at %s ' % model_path)
Saved trained model at /content/drive/model/v2/keras_traffic_2.h5
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 统计模型在测试集上的分类结果
loss_and_metrics = traffic_model.evaluate(X_test_normalized, Y_test, verbose=2,batch_size=256)print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1]*100))predicted_classes = traffic_model.predict_classes(X_test_normalized)correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
print("Classified correctly count: {}".format(len(correct_indices)))
print("Classified incorrectly count: {}".format(len(incorrect_indices)))
Test Loss: 0.6938359957692742
Test Accuracy: 93.08788600368152%
Classified correctly count: 11757
Classified incorrectly count: 873
3.对上述模型进行改进 增加2层Dropout层
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers import Conv2D,MaxPooling2Dmodel3 = Sequential()# layers1
model3.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=X_train_normalized.shape[1:],activation="relu"))
model3.add(MaxPooling2D(pool_size=(2,2)))# layers2
model3.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model3.add(MaxPooling2D(pool_size=(2,2)))# Dropout
model3.add(Dropout(0.25))# layers3
model3.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model3.add(MaxPooling2D(pool_size=(2,2)))# flattern
model3.add(Flatten())# Dense
model3.add(Dense(512,activation="relu"))# Dropout
model3.add(Dropout(0.5))# Dense
model3.add(Dense(n_classes,activation="softmax"))
model3.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_6 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_7 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 6, 6, 64) 0
_________________________________________________________________
conv2d_8 (Conv2D) (None, 4, 4, 64) 36928
_________________________________________________________________
max_pooling2d_7 (MaxPooling2 (None, 2, 2, 64) 0
_________________________________________________________________
flatten_3 (Flatten) (None, 256) 0
_________________________________________________________________
dense_5 (Dense) (None, 512) 131584
_________________________________________________________________
dropout_4 (Dropout) (None, 512) 0
_________________________________________________________________
dense_6 (Dense) (None, 43) 22059
=================================================================
Total params: 209,387
Trainable params: 209,387
Non-trainable params: 0
_________________________________________________________________
# 编译模型
model3.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
# 训练模型
history3 = model3.fit(X_train_normalized,Y_train,batch_size=256,epochs=120,verbose=2,validation_data=(X_valid_normalized,Y_valid))
Train on 51690 samples, validate on 4410 samples
Epoch 1/120- 2s - loss: 1.8536 - acc: 0.4729 - val_loss: 0.6255 - val_acc: 0.8197
Epoch 2/120- 2s - loss: 0.4513 - acc: 0.8609 - val_loss: 0.3030 - val_acc: 0.9082
Epoch 3/120- 2s - loss: 0.2391 - acc: 0.9261 - val_loss: 0.2413 - val_acc: 0.9272
Epoch 4/120- 2s - loss: 0.1676 - acc: 0.9480 - val_loss: 0.2010 - val_acc: 0.9401
Epoch 5/120- 2s - loss: 0.1248 - acc: 0.9610 - val_loss: 0.1635 - val_acc: 0.9519
Epoch 6/120- 2s - loss: 0.1006 - acc: 0.9692 - val_loss: 0.1346 - val_acc: 0.9619
Epoch 7/120- 2s - loss: 0.0848 - acc: 0.9736 - val_loss: 0.1464 - val_acc: 0.9553
Epoch 8/120- 2s - loss: 0.0731 - acc: 0.9765 - val_loss: 0.1513 - val_acc: 0.9526
Epoch 9/120- 2s - loss: 0.0664 - acc: 0.9785 - val_loss: 0.1365 - val_acc: 0.9580
Epoch 10/120- 2s - loss: 0.0590 - acc: 0.9810 - val_loss: 0.1404 - val_acc: 0.9553
Epoch 11/120- 2s - loss: 0.0493 - acc: 0.9838 - val_loss: 0.1258 - val_acc: 0.9646
Epoch 12/120- 2s - loss: 0.0446 - acc: 0.9859 - val_loss: 0.1265 - val_acc: 0.9642
Epoch 13/120- 2s - loss: 0.0389 - acc: 0.9874 - val_loss: 0.1258 - val_acc: 0.9678
Epoch 14/120- 2s - loss: 0.0385 - acc: 0.9872 - val_loss: 0.1220 - val_acc: 0.9669
Epoch 15/120- 2s - loss: 0.0348 - acc: 0.9889 - val_loss: 0.1200 - val_acc: 0.9662
Epoch 16/120- 2s - loss: 0.0354 - acc: 0.9883 - val_loss: 0.0980 - val_acc: 0.9728
Epoch 17/120- 2s - loss: 0.0316 - acc: 0.9897 - val_loss: 0.1222 - val_acc: 0.9680
Epoch 18/120- 2s - loss: 0.0280 - acc: 0.9909 - val_loss: 0.1069 - val_acc: 0.9703
Epoch 19/120- 2s - loss: 0.0285 - acc: 0.9911 - val_loss: 0.1080 - val_acc: 0.9703
Epoch 20/120- 2s - loss: 0.0240 - acc: 0.9915 - val_loss: 0.1049 - val_acc: 0.9714
Epoch 21/120- 2s - loss: 0.0248 - acc: 0.9921 - val_loss: 0.1137 - val_acc: 0.9703
Epoch 22/120- 2s - loss: 0.0248 - acc: 0.9922 - val_loss: 0.0974 - val_acc: 0.9728
Epoch 23/120- 2s - loss: 0.0213 - acc: 0.9930 - val_loss: 0.1277 - val_acc: 0.9633
Epoch 24/120- 2s - loss: 0.0232 - acc: 0.9924 - val_loss: 0.0980 - val_acc: 0.9751
Epoch 25/120- 2s - loss: 0.0208 - acc: 0.9932 - val_loss: 0.1124 - val_acc: 0.9739
Epoch 26/120- 2s - loss: 0.0183 - acc: 0.9939 - val_loss: 0.1076 - val_acc: 0.9726
Epoch 27/120- 2s - loss: 0.0205 - acc: 0.9928 - val_loss: 0.0998 - val_acc: 0.9735
Epoch 28/120- 2s - loss: 0.0167 - acc: 0.9944 - val_loss: 0.1231 - val_acc: 0.9701
Epoch 29/120- 2s - loss: 0.0188 - acc: 0.9940 - val_loss: 0.1025 - val_acc: 0.9737
Epoch 30/120- 2s - loss: 0.0164 - acc: 0.9947 - val_loss: 0.1189 - val_acc: 0.9701Epoch 110/120- 2s - loss: 0.0069 - acc: 0.9980 - val_loss: 0.0982 - val_acc: 0.9805
Epoch 111/120- 2s - loss: 0.0086 - acc: 0.9976 - val_loss: 0.0940 - val_acc: 0.9816
Epoch 112/120- 2s - loss: 0.0057 - acc: 0.9982 - val_loss: 0.0958 - val_acc: 0.9807
Epoch 113/120- 2s - loss: 0.0066 - acc: 0.9981 - val_loss: 0.1343 - val_acc: 0.9780
Epoch 114/120- 2s - loss: 0.0059 - acc: 0.9982 - val_loss: 0.1218 - val_acc: 0.9796
Epoch 115/120- 2s - loss: 0.0074 - acc: 0.9979 - val_loss: 0.1266 - val_acc: 0.9771
Epoch 116/120- 2s - loss: 0.0063 - acc: 0.9981 - val_loss: 0.1001 - val_acc: 0.9819
Epoch 117/120- 2s - loss: 0.0063 - acc: 0.9980 - val_loss: 0.0945 - val_acc: 0.9819
Epoch 118/120- 2s - loss: 0.0082 - acc: 0.9976 - val_loss: 0.1334 - val_acc: 0.9751
Epoch 119/120- 2s - loss: 0.0073 - acc: 0.9976 - val_loss: 0.1341 - val_acc: 0.9735
Epoch 120/120- 2s - loss: 0.0072 - acc: 0.9977 - val_loss: 0.1004 - val_acc: 0.9794
# 可视化指标
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(history3.history['acc'])
plt.plot(history3.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')plt.subplot(2,1,2)
plt.plot(history3.history['loss'])
plt.plot(history3.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.tight_layout()plt.show()
# 保存模型
import os
import tensorflow.gfile as gfilesave_dir = "/content/drive/model/v3/"if gfile.Exists(save_dir):gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)model_name = 'keras_traffic_3.h5'
model_path = os.path.join(save_dir, model_name)model3.save(model_path)
print('Saved trained model at %s ' % model_path)
Saved trained model at /content/drive/model/v3/keras_traffic_3.h5
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 统计模型在测试集上的分类结果
loss_and_metrics = traffic_model.evaluate(X_test_normalized, Y_test, verbose=2,batch_size=256)print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1]*100))predicted_classes = traffic_model.predict_classes(X_test_normalized)correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
print("Classified correctly count: {}".format(len(correct_indices)))
print("Classified incorrectly count: {}".format(len(incorrect_indices)))
Test Loss: 0.2989197100426409
Test Accuracy: 95.88281867056732%
Classified correctly count: 12110
Classified incorrectly count: 520
4.2层卷积池化层+1层展开层+3层全连接层
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers import Conv2D,MaxPooling2Dmodel4 = Sequential()# layers1
model4.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=X_train_normalized.shape[1:],activation="relu"))
model4.add(MaxPooling2D(pool_size=(2,2)))# layers2
model4.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model4.add(MaxPooling2D(pool_size=(2,2)))# flattern
model4.add(Flatten())# Dense
model4.add(Dense(512,activation="relu"))# Dense
model4.add(Dense(100,activation="relu"))# Dense
model4.add(Dense(n_classes,activation="softmax"))
# 查看模型的结构
model4.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_9 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
max_pooling2d_8 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_10 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
max_pooling2d_9 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_7 (Dense) (None, 512) 1180160
_________________________________________________________________
dense_8 (Dense) (None, 100) 51300
_________________________________________________________________
dense_9 (Dense) (None, 43) 4343
=================================================================
Total params: 1,254,619
Trainable params: 1,254,619
Non-trainable params: 0
_________________________________________________________________
# 编译模型
model4.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
# 训练模型
history4 = model4.fit(X_train_normalized,Y_train,batch_size=256,epochs=120,verbose=2,validation_data=(X_valid_normalized,Y_valid))
Train on 51690 samples, validate on 4410 samples
Epoch 1/120- 3s - loss: 0.9466 - acc: 0.7433 - val_loss: 0.4214 - val_acc: 0.8746
Epoch 2/120- 2s - loss: 0.1339 - acc: 0.9632 - val_loss: 0.3042 - val_acc: 0.9152
Epoch 3/120- 2s - loss: 0.0681 - acc: 0.9809 - val_loss: 0.2988 - val_acc: 0.9170
Epoch 4/120- 2s - loss: 0.0414 - acc: 0.9885 - val_loss: 0.3027 - val_acc: 0.9125
Epoch 5/120- 2s - loss: 0.0244 - acc: 0.9934 - val_loss: 0.2754 - val_acc: 0.9308
Epoch 6/120- 2s - loss: 0.0198 - acc: 0.9944 - val_loss: 0.2664 - val_acc: 0.9299
Epoch 7/120- 2s - loss: 0.0139 - acc: 0.9963 - val_loss: 0.3340 - val_acc: 0.9197
Epoch 8/120- 2s - loss: 0.0150 - acc: 0.9958 - val_loss: 0.2918 - val_acc: 0.9336
Epoch 9/120- 2s - loss: 0.0166 - acc: 0.9948 - val_loss: 0.2496 - val_acc: 0.9399
Epoch 10/120- 2s - loss: 0.0070 - acc: 0.9979 - val_loss: 0.2661 - val_acc: 0.9365
Epoch 11/120- 2s - loss: 0.0060 - acc: 0.9984 - val_loss: 0.3019 - val_acc: 0.9290
Epoch 12/120- 2s - loss: 0.0063 - acc: 0.9984 - val_loss: 0.2890 - val_acc: 0.9347
Epoch 13/120- 2s - loss: 0.0076 - acc: 0.9979 - val_loss: 0.2583 - val_acc: 0.9363
Epoch 14/120- 2s - loss: 0.0124 - acc: 0.9962 - val_loss: 0.3169 - val_acc: 0.9240
Epoch 15/120- 2s - loss: 0.0097 - acc: 0.9970 - val_loss: 0.2796 - val_acc: 0.9429
Epoch 16/120- 2s - loss: 0.0088 - acc: 0.9975 - val_loss: 0.3084 - val_acc: 0.9320
Epoch 17/120- 2s - loss: 0.0093 - acc: 0.9972 - val_loss: 0.3469 - val_acc: 0.9281
Epoch 18/120- 2s - loss: 0.0029 - acc: 0.9991 - val_loss: 0.2834 - val_acc: 0.9388
Epoch 19/120- 2s - loss: 9.1336e-04 - acc: 0.9998 - val_loss: 0.2210 - val_acc: 0.9535
Epoch 20/120- 2s - loss: 3.3410e-04 - acc: 0.9999 - val_loss: 0.2416 - val_acc: 0.9546
Epoch 21/120- 2s - loss: 4.8947e-05 - acc: 1.0000 - val_loss: 0.2395 - val_acc: 0.9556
Epoch 22/120- 2s - loss: 2.7690e-05 - acc: 1.0000 - val_loss: 0.2449 - val_acc: 0.9556
Epoch 23/120- 2s - loss: 2.0403e-05 - acc: 1.0000 - val_loss: 0.2481 - val_acc: 0.9556
Epoch 24/120- 2s - loss: 1.6893e-05 - acc: 1.0000 - val_loss: 0.2507 - val_acc: 0.9553
Epoch 25/120- 2s - loss: 1.4042e-05 - acc: 1.0000 - val_loss: 0.2534 - val_acc: 0.9553
Epoch 26/120- 2s - loss: 1.2080e-05 - acc: 1.0000 - val_loss: 0.2556 - val_acc: 0.9556
Epoch 27/120- 2s - loss: 1.0456e-05 - acc: 1.0000 - val_loss: 0.2577 - val_acc: 0.9551
Epoch 28/120- 2s - loss: 9.1271e-06 - acc: 1.0000 - val_loss: 0.2589 - val_acc: 0.9560
Epoch 29/120- 2s - loss: 8.0037e-06 - acc: 1.0000 - val_loss: 0.2610 - val_acc: 0.9549
Epoch 30/120- 2s - loss: 7.1773e-06 - acc: 1.0000 - val_loss: 0.2630 - val_acc: 0.9560Epoch 110/120- 2s - loss: 1.2023e-07 - acc: 1.0000 - val_loss: 0.3727 - val_acc: 0.9533
Epoch 111/120- 2s - loss: 1.2010e-07 - acc: 1.0000 - val_loss: 0.3727 - val_acc: 0.9537
Epoch 112/120- 2s - loss: 1.1994e-07 - acc: 1.0000 - val_loss: 0.3716 - val_acc: 0.9535
Epoch 113/120- 2s - loss: 1.1984e-07 - acc: 1.0000 - val_loss: 0.3747 - val_acc: 0.9535
Epoch 114/120- 2s - loss: 1.1972e-07 - acc: 1.0000 - val_loss: 0.3758 - val_acc: 0.9535
Epoch 115/120- 2s - loss: 1.1968e-07 - acc: 1.0000 - val_loss: 0.3763 - val_acc: 0.9533
Epoch 116/120- 2s - loss: 1.1957e-07 - acc: 1.0000 - val_loss: 0.3789 - val_acc: 0.9528
Epoch 117/120- 2s - loss: 1.1953e-07 - acc: 1.0000 - val_loss: 0.3783 - val_acc: 0.9531
Epoch 118/120- 2s - loss: 1.1947e-07 - acc: 1.0000 - val_loss: 0.3796 - val_acc: 0.9528
Epoch 119/120- 2s - loss: 1.1943e-07 - acc: 1.0000 - val_loss: 0.3805 - val_acc: 0.9535
Epoch 120/120- 2s - loss: 1.1942e-07 - acc: 1.0000 - val_loss: 0.3811 - val_acc: 0.9533
# 可视化指标
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(history4.history['acc'])
plt.plot(history4.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')plt.subplot(2,1,2)
plt.plot(history4.history['loss'])
plt.plot(history4.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.tight_layout()plt.show()
# 保存模型
import os
import tensorflow.gfile as gfilesave_dir = "/content/drive/model/v4/"if gfile.Exists(save_dir):gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)model_name = 'keras_traffic_4.h5'
model_path = os.path.join(save_dir, model_name)model4.save(model_path)
print('Saved trained model at %s ' % model_path)Saved trained model at /content/drive/model/v4/keras_traffic_4.h5
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 统计模型在测试集上的分类结果
loss_and_metrics = traffic_model.evaluate(X_test_normalized, Y_test, verbose=2,batch_size=256)print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1]*100))predicted_classes = traffic_model.predict_classes(X_test_normalized)correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
print("Classified correctly count: {}".format(len(correct_indices)))
print("Classified incorrectly count: {}".format(len(incorrect_indices)))
Test Loss: 0.61759698928016
Test Accuracy: 93.18289788016608%
Classified correctly count: 11769
Classified incorrectly count: 861
5.对上述模型进行改进增加3层Dropout层
# 改进模型
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten
from keras.layers import Conv2D,MaxPooling2Dmodel5 = Sequential()# layers1
model5.add(Conv2D(filters=32,kernel_size=(3,3),input_shape=X_train_normalized.shape[1:],activation="relu"))
model5.add(MaxPooling2D(pool_size=(2,2)))# layers2
model5.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model5.add(MaxPooling2D(pool_size=(2,2)))# Dropout
model5.add(Dropout(0.5))# flattern
model5.add(Flatten())# Dense
model5.add(Dense(512,activation="relu"))# Dropout
model5.add(Dropout(0.5))# Dense
model5.add(Dense(100,activation="relu"))# Dropout
model5.add(Dropout(0.5))# Dense
model5.add(Dense(n_classes,activation="softmax"))
model5.summary()
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_11 (Conv2D) (None, 30, 30, 32) 320
_________________________________________________________________
max_pooling2d_10 (MaxPooling (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_12 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
max_pooling2d_11 (MaxPooling (None, 6, 6, 64) 0
_________________________________________________________________
dropout_5 (Dropout) (None, 6, 6, 64) 0
_________________________________________________________________
flatten_5 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_10 (Dense) (None, 512) 1180160
_________________________________________________________________
dropout_6 (Dropout) (None, 512) 0
_________________________________________________________________
dense_11 (Dense) (None, 100) 51300
_________________________________________________________________
dropout_7 (Dropout) (None, 100) 0
_________________________________________________________________
dense_12 (Dense) (None, 43) 4343
=================================================================
Total params: 1,254,619
Trainable params: 1,254,619
Non-trainable params: 0
_________________________________________________________________
# 编译模型
model5.compile(loss="categorical_crossentropy",metrics=["accuracy"],optimizer="adam")
# 训练模型
history5 = model5.fit(X_train_normalized,Y_train,batch_size=256,epochs=120,verbose=2,validation_data=(X_valid_normalized,Y_valid)) Train on 51690 samples, validate on 4410 samples
Epoch 1/120- 3s - loss: 2.0740 - acc: 0.4183 - val_loss: 0.5267 - val_acc: 0.8590
Epoch 2/120- 2s - loss: 0.5917 - acc: 0.8149 - val_loss: 0.2372 - val_acc: 0.9395
Epoch 3/120- 2s - loss: 0.3512 - acc: 0.8919 - val_loss: 0.1723 - val_acc: 0.9508
Epoch 4/120- 2s - loss: 0.2560 - acc: 0.9214 - val_loss: 0.1449 - val_acc: 0.9583
Epoch 5/120- 2s - loss: 0.2048 - acc: 0.9382 - val_loss: 0.1341 - val_acc: 0.9590
Epoch 6/120- 2s - loss: 0.1682 - acc: 0.9497 - val_loss: 0.1183 - val_acc: 0.9655
Epoch 7/120- 2s - loss: 0.1406 - acc: 0.9573 - val_loss: 0.1070 - val_acc: 0.9698
Epoch 8/120- 2s - loss: 0.1288 - acc: 0.9604 - val_loss: 0.1037 - val_acc: 0.9667
Epoch 9/120- 2s - loss: 0.1175 - acc: 0.9641 - val_loss: 0.1056 - val_acc: 0.9685
Epoch 10/120- 2s - loss: 0.1016 - acc: 0.9690 - val_loss: 0.0935 - val_acc: 0.9696
Epoch 11/120- 2s - loss: 0.0940 - acc: 0.9709 - val_loss: 0.0856 - val_acc: 0.9741
Epoch 12/120- 2s - loss: 0.0896 - acc: 0.9729 - val_loss: 0.0901 - val_acc: 0.9741
Epoch 13/120- 2s - loss: 0.0857 - acc: 0.9744 - val_loss: 0.0879 - val_acc: 0.9719
Epoch 14/120- 2s - loss: 0.0803 - acc: 0.9757 - val_loss: 0.0894 - val_acc: 0.9723
Epoch 15/120- 2s - loss: 0.0740 - acc: 0.9782 - val_loss: 0.1040 - val_acc: 0.9705
Epoch 16/120- 2s - loss: 0.0708 - acc: 0.9796 - val_loss: 0.1073 - val_acc: 0.9683
Epoch 17/120- 2s - loss: 0.0652 - acc: 0.9800 - val_loss: 0.0791 - val_acc: 0.9753
Epoch 18/120- 2s - loss: 0.0660 - acc: 0.9801 - val_loss: 0.0794 - val_acc: 0.9751
Epoch 19/120- 2s - loss: 0.0571 - acc: 0.9828 - val_loss: 0.0925 - val_acc: 0.9705
Epoch 20/120- 2s - loss: 0.0573 - acc: 0.9828 - val_loss: 0.0692 - val_acc: 0.9791
Epoch 21/120- 2s - loss: 0.0532 - acc: 0.9834 - val_loss: 0.0726 - val_acc: 0.9785
Epoch 22/120- 2s - loss: 0.0548 - acc: 0.9837 - val_loss: 0.0824 - val_acc: 0.9741
Epoch 23/120- 2s - loss: 0.0529 - acc: 0.9841 - val_loss: 0.0747 - val_acc: 0.9789
Epoch 24/120- 2s - loss: 0.0544 - acc: 0.9839 - val_loss: 0.0710 - val_acc: 0.9776
Epoch 25/120- 2s - loss: 0.0470 - acc: 0.9859 - val_loss: 0.0687 - val_acc: 0.9789
Epoch 26/120- 2s - loss: 0.0459 - acc: 0.9860 - val_loss: 0.0710 - val_acc: 0.9769
Epoch 27/120- 2s - loss: 0.0482 - acc: 0.9859 - val_loss: 0.0718 - val_acc: 0.9764
Epoch 28/120- 2s - loss: 0.0456 - acc: 0.9862 - val_loss: 0.0744 - val_acc: 0.9812
Epoch 29/120- 2s - loss: 0.0469 - acc: 0.9861 - val_loss: 0.0883 - val_acc: 0.9762
Epoch 30/120- 2s - loss: 0.0445 - acc: 0.9863 - val_loss: 0.0757 - val_acc: 0.9757Epoch 110/120- 2s - loss: 0.0217 - acc: 0.9941 - val_loss: 0.0600 - val_acc: 0.9832
Epoch 111/120- 2s - loss: 0.0243 - acc: 0.9936 - val_loss: 0.0716 - val_acc: 0.9800
Epoch 112/120- 2s - loss: 0.0204 - acc: 0.9941 - val_loss: 0.0740 - val_acc: 0.9794
Epoch 113/120- 2s - loss: 0.0178 - acc: 0.9948 - val_loss: 0.0669 - val_acc: 0.9828
Epoch 114/120- 2s - loss: 0.0184 - acc: 0.9949 - val_loss: 0.0569 - val_acc: 0.9850
Epoch 115/120- 2s - loss: 0.0224 - acc: 0.9943 - val_loss: 0.0703 - val_acc: 0.9791
Epoch 116/120- 2s - loss: 0.0203 - acc: 0.9944 - val_loss: 0.0762 - val_acc: 0.9789
Epoch 117/120- 2s - loss: 0.0182 - acc: 0.9945 - val_loss: 0.0705 - val_acc: 0.9798
Epoch 118/120- 2s - loss: 0.0204 - acc: 0.9944 - val_loss: 0.0603 - val_acc: 0.9819
Epoch 119/120- 2s - loss: 0.0188 - acc: 0.9950 - val_loss: 0.0544 - val_acc: 0.9848
Epoch 120/120- 2s - loss: 0.0198 - acc: 0.9947 - val_loss: 0.0593 - val_acc: 0.9857
# 可视化指标
fig = plt.figure()
plt.subplot(2,1,1)
plt.plot(history5.history['acc'])
plt.plot(history5.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='lower right')plt.subplot(2,1,2)
plt.plot(history5.history['loss'])
plt.plot(history5.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper right')
plt.tight_layout()plt.show()
# 保存模型
import os
import tensorflow.gfile as gfilesave_dir = "/content/drive/model/v5/"if gfile.Exists(save_dir):gfile.DeleteRecursively(save_dir)
gfile.MakeDirs(save_dir)model_name = 'keras_traffic_5.h5'
model_path = os.path.join(save_dir, model_name)model5.save(model_path)
print('Saved trained model at %s ' % model_path)Saved trained model at /content/drive/model/v5/keras_traffic_5.h5
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 统计模型在测试集上的分类结果
loss_and_metrics = traffic_model.evaluate(X_test_normalized, Y_test, verbose=2,batch_size=256)print("Test Loss: {}".format(loss_and_metrics[0]))
print("Test Accuracy: {}%".format(loss_and_metrics[1]*100))predicted_classes = traffic_model.predict_classes(X_test_normalized)correct_indices = np.nonzero(predicted_classes == y_test)[0]
incorrect_indices = np.nonzero(predicted_classes != y_test)[0]
print("Classified correctly count: {}".format(len(correct_indices)))
print("Classified incorrectly count: {}".format(len(incorrect_indices)))
Test Loss: 0.15594110083032578
Test Accuracy: 96.76167853182493%
Classified correctly count: 12221
Classified incorrectly count: 409
补充单张图片预测
# 加载模型
from keras.models import load_modeltraffic_model = load_model(model_path)
# 加载预测图片import os
import cv2
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as npimage_path = "/content/drive/image/"
filenames = os.listdir(image_path)X_predict = []for filename in filenames:if filename.endswith(".jpg"):img = cv2.imread(image_path+filename)img = cv2.resize(img,(32,32),cv2.INTER_LINEAR)X_predict.append(img)plt.imshow(img)# 封装数据预处理的方法
import cv2def preprocess_features(X, equalize_hist=True):normalized_X = []for i in range(len(X)):# Convert from RGB to YUVyuv_img = cv2.cvtColor(X[i], cv2.COLOR_RGB2YUV)yuv_img_v = X[i][:, :, 0]# equalizeHistyuv_img_v = cv2.equalizeHist(yuv_img_v)# expand_disyuv_img_v = np.expand_dims(yuv_img_v, 2)normalized_X.append(yuv_img_v)# normalizenormalized_X = np.array(normalized_X, dtype=np.float32)normalized_X = (normalized_X-128)/128# normalized_X /= (np.std(normalized_X, axis=0) + np.finfo('float32').eps)return normalized_X
# 对数据进行处理
X_predict_normalized = preprocess_features(X_predict)
# 数据维度及数据类型
print(X_predict_normalized.shape,type(X_predict_normalized))
# 模型预测
y_predict = traffic_model.predict_classes(X_predict_normalized)
print(id_to_name(y_predict[0]))
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