标签：layers decay VGG label add model data
vgg代码如下：
1 import tensorflow as tf 2 from tensorflow import keras 3 from tensorflow.keras import layers, regularizers 4 import numpy as np 5 import os 6 import cv2 7 import matplotlib.pyplot as plt 8 9 10 os.environ["CUDA_VISIBLE_DEVICES"] = "1"
1 resize = 224 2 path ="train/" 3 4 def load_data(): 5 imgs = os.listdir(path) 6 num = len(imgs) 7 train_data = np.empty((5000, resize, resize, 3), dtype="int32") 8 train_label = np.empty((5000, ), dtype="int32") 9 test_data = np.empty((5000, resize, resize, 3), dtype="int32") 10 test_label = np.empty((5000, ), dtype="int32") 11 for i in range(5000): 12 if i % 2: 13 train_data[i] = cv2.resize(cv2.imread(path+'/'+ 'dog.' + str(i) + '.jpg'), (resize, resize)) 14 train_label[i] = 1 15 else: 16 train_data[i] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resize)) 17 train_label[i] = 0 18 for i in range(5000, 10000): 19 if i % 2: 20 test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'dog.' + str(i) + '.jpg'), (resize, resize)) 21 test_label[i-5000] = 1 22 else: 23 test_data[i-5000] = cv2.resize(cv2.imread(path+'/' + 'cat.' + str(i) + '.jpg'), (resize, resize)) 24 test_label[i-5000] = 0 25 return train_data, train_label, test_data, test_label
1 def vgg16():
2 weight_decay = 0.0005
3 nb_epoch = 100
4 batch_size = 32
5
6 # layer1
7 model = keras.Sequential()
8 model.add(layers.Conv2D(64, (3, 3), padding='same',
9 input_shape=(224, 224, 3), kernel_regularizer=regularizers.l2(weight_decay)))
10 model.add(layers.Activation('relu'))
11 model.add(layers.BatchNormalization())
12 model.add(layers.Dropout(0.3))
13 # layer2
14 model.add(layers.Conv2D(64, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
15 model.add(layers.Activation('relu'))
16 model.add(layers.BatchNormalization())
17 model.add(layers.MaxPooling2D(pool_size=(2, 2)))
18 # layer3
19 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
20 model.add(layers.Activation('relu'))
21 model.add(layers.BatchNormalization())
22 model.add(layers.Dropout(0.4))
23 # layer4
24 model.add(layers.Conv2D(128, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
25 model.add(layers.Activation('relu'))
26 model.add(layers.BatchNormalization())
27 model.add(layers.MaxPooling2D(pool_size=(2, 2)))
28 # layer5
29 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
30 model.add(layers.Activation('relu'))
31 model.add(layers.BatchNormalization())
32 model.add(layers.Dropout(0.4))
33 # layer6
34 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
35 model.add(layers.Activation('relu'))
36 model.add(layers.BatchNormalization())
37 model.add(layers.Dropout(0.4))
38 # layer7
39 model.add(layers.Conv2D(256, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
40 model.add(layers.Activation('relu'))
41 model.add(layers.BatchNormalization())
42 model.add(layers.MaxPooling2D(pool_size=(2, 2)))
43 # layer8
44 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
45 model.add(layers.Activation('relu'))
46 model.add(layers.BatchNormalization())
47 model.add(layers.Dropout(0.4))
48 # layer9
49 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
50 model.add(layers.Activation('relu'))
51 model.add(layers.BatchNormalization())
52 model.add(layers.Dropout(0.4))
53 # layer10
54 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
55 model.add(layers.Activation('relu'))
56 model.add(layers.BatchNormalization())
57 model.add(layers.MaxPooling2D(pool_size=(2, 2)))
58 # layer11
59 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
60 model.add(layers.Activation('relu'))
61 model.add(layers.BatchNormalization())
62 model.add(layers.Dropout(0.4))
63 # layer12
64 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
65 model.add(layers.Activation('relu'))
66 model.add(layers.BatchNormalization())
67 model.add(layers.Dropout(0.4))
68 # layer13
69 model.add(layers.Conv2D(512, (3, 3), padding='same', kernel_regularizer=regularizers.l2(weight_decay)))
70 model.add(layers.Activation('relu'))
71 model.add(layers.BatchNormalization())
72 model.add(layers.MaxPooling2D(pool_size=(2, 2)))
73 model.add(layers.Dropout(0.5))
74 # layer14
75 model.add(layers.Flatten())
76 model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay)))
77 model.add(layers.Activation('relu'))
78 model.add(layers.BatchNormalization())
79 # layer15
80 model.add(layers.Dense(512, kernel_regularizer=regularizers.l2(weight_decay)))
81 model.add(layers.Activation('relu'))
82 model.add(layers.BatchNormalization())
83 # layer16
84 model.add(layers.Dropout(0.5))
85 model.add(layers.Dense(2))
86 model.add(layers.Activation('softmax'))
87
88 return model
1 #if __name__ == '__main__':
2 train_data, train_label, test_data, test_label = load_data()
3 train_data = train_data.astype('float32')
4 test_data = test_data.astype('float32')
5 train_label = keras.utils.to_categorical(train_label, 2)
6 test_label = keras.utils.to_categorical(test_label, 2)
1 #定义训练方法,超参数设置
2 model = vgg16()
3 sgd = tf.keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) #设置优化器为SGD
4 model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
5 history = model.fit(train_data, train_label,
6 batch_size=20,
7 epochs=10,
8 validation_split=0.2, #把训练集中的五分之一作为验证集
9 shuffle=True)
10 scores = model.evaluate(test_data,test_label,verbose=1)
11 print(scores)
12 model.save('model/vgg16dogcat.h5')
1 acc = history.history['accuracy'] # 获取训练集准确性数据 2 val_acc = history.history['val_accuracy'] # 获取验证集准确性数据 3 loss = history.history['loss'] # 获取训练集错误值数据 4 val_loss = history.history['val_loss'] # 获取验证集错误值数据 5 epochs = range(1, len(acc) + 1) 6 plt.plot(epochs, acc, 'bo', label='Trainning acc') # 以epochs为横坐标，以训练集准确性为纵坐标 7 plt.plot(epochs, val_acc, 'b', label='Vaildation acc') # 以epochs为横坐标，以验证集准确性为纵坐标 8 plt.legend() # 绘制图例，即标明图中的线段代表何种含义 9 10 plt.show()
运行结果如下：
标签：layers,decay,VGG,label,add,model,data 来源： https://www.cnblogs.com/-tsir-/p/16275376.html
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