一共有12类不同的图像,分别被划分为训练集,验证集,分别在data文件夹下的train文件夹和val文件夹中。这12类图像中,其中有11类图像描述着各种各样的问题,分别在data文件夹下的train1文件夹和val1文件夹中。有1类图像是无缺陷的图像,分别在data文件夹下的train2文件夹和val2文件夹中。现在准备在一个分支上将11类有问题的图像用VGG16网络进行训练,再在另一个分支上将1类无问题的图像用VGG16网络进行训练,然后将这两个分支得到的结果进行相减,相减之后得到的结果,再次通过VGG16网络进行训练,对12类图像的数据集进行训练、验证。请给出相应代码,并使用ContrastiveLoss作为损失函数。
时间: 2023-12-03 11:45:34 浏览: 19
首先需要导入必要的库和模块,包括tensorflow、keras等。代码如下:
```python
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.losses import ContrastiveLoss
```
接着,我们需要设置一些超参数,如图像的大小、批次大小、迭代次数等。代码如下:
```python
# 设置超参数
img_size = (224, 224)
batch_size = 32
epochs = 10
steps_per_epoch = 100
validation_steps = 50
```
接下来,我们需要创建两个数据生成器,一个用于读取11类有问题的图像数据集,另一个用于读取1类无问题的图像数据集。代码如下:
```python
# 创建数据生成器
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train1_generator = train_datagen.flow_from_directory(
'data/train1',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
val1_generator = val_datagen.flow_from_directory(
'data/val1',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
train2_generator = train_datagen.flow_from_directory(
'data/train2',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
val2_generator = val_datagen.flow_from_directory(
'data/val2',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
```
接下来,我们需要创建两个VGG16网络模型,分别用于训练11类有问题的图像数据集和1类无问题的图像数据集。代码如下:
```python
# 创建VGG16网络模型
def create_model():
input_tensor = Input(shape=(224, 224, 3))
base_model = tf.keras.applications.VGG16(input_tensor=input_tensor, weights='imagenet', include_top=False)
x = base_model.output
x = Flatten()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
model1 = create_model()
model2 = create_model()
```
接下来,我们需要将两个VGG16网络模型相减,得到的结果再次通过VGG16网络进行训练。这里我们需要创建一个新的模型,并将两个VGG16网络模型作为其子模型。代码如下:
```python
# 将两个模型相减
input_tensor1 = Input(shape=(224, 224, 3))
input_tensor2 = Input(shape=(224, 224, 3))
output_tensor1 = model1(input_tensor1)
output_tensor2 = model2(input_tensor2)
merged_output = tf.keras.layers.Subtract()([output_tensor1, output_tensor2])
x = Dense(256, activation='relu')(merged_output)
x = Dropout(0.5)(x)
predictions = Dense(12, activation='softmax')(x)
model = Model(inputs=[input_tensor1, input_tensor2], outputs=predictions)
```
最后,我们需要编译模型,并使用ContrastiveLoss作为损失函数。代码如下:
```python
# 编译模型
optimizer = Adam(lr=0.0001)
loss = ContrastiveLoss(margin=1)
metrics = ['accuracy']
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
```
完整代码如下:
```python
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten, Input
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import Adam
from keras.losses import ContrastiveLoss
# 设置超参数
img_size = (224, 224)
batch_size = 32
epochs = 10
steps_per_epoch = 100
validation_steps = 50
# 创建数据生成器
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train1_generator = train_datagen.flow_from_directory(
'data/train1',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
val1_generator = val_datagen.flow_from_directory(
'data/val1',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
train2_generator = train_datagen.flow_from_directory(
'data/train2',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
val2_generator = val_datagen.flow_from_directory(
'data/val2',
target_size=img_size,
batch_size=batch_size,
class_mode='binary')
# 创建VGG16网络模型
def create_model():
input_tensor = Input(shape=(224, 224, 3))
base_model = tf.keras.applications.VGG16(input_tensor=input_tensor, weights='imagenet', include_top=False)
x = base_model.output
x = Flatten()(x)
x = Dense(256, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(1, activation='sigmoid')(x)
model = Model(inputs=base_model.input, outputs=predictions)
return model
model1 = create_model()
model2 = create_model()
# 将两个模型相减
input_tensor1 = Input(shape=(224, 224, 3))
input_tensor2 = Input(shape=(224, 224, 3))
output_tensor1 = model1(input_tensor1)
output_tensor2 = model2(input_tensor2)
merged_output = tf.keras.layers.Subtract()([output_tensor1, output_tensor2])
x = Dense(256, activation='relu')(merged_output)
x = Dropout(0.5)(x)
predictions = Dense(12, activation='softmax')(x)
model = Model(inputs=[input_tensor1, input_tensor2], outputs=predictions)
# 编译模型
optimizer = Adam(lr=0.0001)
loss = ContrastiveLoss(margin=1)
metrics = ['accuracy']
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
# 训练模型
model.fit_generator(
zip(train1_generator, train2_generator),
steps_per_epoch=steps_per_epoch,
epochs=epochs,
validation_data=zip(val1_generator, val2_generator),
validation_steps=validation_steps)
```