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: 2023-07-15 12:15:19 浏览: 51
以下是使用ContrastiveLoss作为损失函数的代码,首先需要导入必要的库和模块:
```python
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Input, Flatten, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import ContrastiveLoss
```
然后,我们定义一些超参数:
```python
input_shape = (224, 224, 3)
batch_size = 32
epochs = 10
learning_rate = 0.001
```
接下来,我们使用ImageDataGenerator生成器从文件夹中读取训练和验证数据:
```python
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
train1_generator = train_datagen.flow_from_directory(
directory='data/train1',
target_size=input_shape[:2],
batch_size=batch_size,
class_mode=None,
shuffle=True
)
val1_generator = val_datagen.flow_from_directory(
directory='data/val1',
target_size=input_shape[:2],
batch_size=batch_size,
class_mode=None,
shuffle=False
)
train2_generator = train_datagen.flow_from_directory(
directory='data/train2',
target_size=input_shape[:2],
batch_size=batch_size,
class_mode=None,
shuffle=True
)
val2_generator = val_datagen.flow_from_directory(
directory='data/val2',
target_size=input_shape[:2],
batch_size=batch_size,
class_mode=None,
shuffle=False
)
```
接着,我们定义一个VGG16模型,分别对11类有问题的图像和1类无问题的图像进行训练:
```python
def create_model():
base_model = tf.keras.applications.VGG16(include_top=False, weights='imagenet', input_shape=input_shape)
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
x = Flatten()(x)
x = Dense(512, activation='relu')(x)
x = Dense(256, activation='relu')(x)
x = Dense(128, activation='relu')(x)
output = Dense(64, activation='relu')(x)
model = Model(inputs=base_model.input, outputs=output)
return model
model1 = create_model()
model2 = create_model()
optimizer = Adam(lr=learning_rate)
model1.compile(optimizer=optimizer, loss=ContrastiveLoss(margin=1.0))
model2.compile(optimizer=optimizer, loss=ContrastiveLoss(margin=1.0))
history1 = model1.fit(train1_generator, epochs=epochs, validation_data=val1_generator)
history2 = model2.fit(train2_generator, epochs=epochs, validation_data=val2_generator)
```
接着,我们获取训练后的模型的输出,并将两个模型输出相减,然后再次通过VGG16网络进行训练:
```python
output1 = model1.get_layer('dense_3').output
output2 = model2.get_layer('dense_3').output
merged_output = tf.keras.layers.Subtract()([output1, output2])
x = Dense(512, activation='relu')(merged_output)
x = Dense(256, activation='relu')(x)
x = Dense(128, activation='relu')(x)
output = Dense(12, activation='softmax')(x)
model = Model(inputs=[model1.input, model2.input], outputs=output)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
train1_generator.reset()
train2_generator.reset()
val1_generator.reset()
val2_generator.reset()
history = model.fit(
[train1_generator, train2_generator],
epochs=epochs,
validation_data=([val1_generator, val2_generator]),
)
model.save('final_model.h5')
```
最后,我们将训练好的模型保存为final_model.h5文件。