resnet50v2
时间: 2023-10-18 14:05:54 浏览: 45
ResNet50V2是ResNet50算法的改进版本。与ResNet50相比,ResNet50V2在框架图中添加了一个名为block的模块,将原来的Conv Block模块的conv2d替换为MaxPool。ResNet50V2的构建方式与ResNet101V2和ResNet152V2完全相同,只是堆叠的Residual Block数量不同。
相关问题
tensorflow搭建ResNet50V2
以下为使用TensorFlow搭建ResNet50V2的步骤:
1. 导入必要的库
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, Dense, Flatten, Add, ZeroPadding2D, AveragePooling2D
from tensorflow.keras.models import Model
from tensorflow.keras import backend as K
```
2. 定义卷积块函数
```python
def conv_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
"""
定义ResNet中的卷积块
"""
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
# 定义卷积块名称
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# 第一层卷积
x = Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
# 第二层卷积
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
# 第三层卷积
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
# shortcut
shortcut = Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
shortcut = BatchNormalization(axis=bn_axis, name=bn_name_base + '1')(shortcut)
x = Add()([x, shortcut])
x = Activation('relu')(x)
return x
```
3. 定义恒等块函数
```python
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""
定义ResNet中的恒等块
"""
filters1, filters2, filters3 = filters
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
# 定义恒等块名称
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
# 第一层卷积
x = Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = Activation('relu')(x)
# 第二层卷积
x = Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = Activation('relu')(x)
# 第三层卷积
x = Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
x = Add()([x, input_tensor])
x = Activation('relu')(x)
return x
```
4. 定义ResNet50V2模型
```python
def ResNet50V2(input_shape=(224, 224, 3), classes=1000):
"""
定义ResNet50V2模型
"""
if K.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
# 输入层
img_input = Input(shape=input_shape)
# 第一层卷积
x = ZeroPadding2D(padding=(3, 3))(img_input)
x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = BatchNormalization(axis=bn_axis, name='bn_conv1')(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2))(x)
# 卷积块
x = conv_block(x, 3, [64, 64, 256], stage=2, block='a', strides=(1, 1))
x = identity_block(x, 3, [64, 64, 256], stage=2, block='b')
x = identity_block(x, 3, [64, 64, 256], stage=2, block='c')
# 卷积块
x = conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='c')
x = identity_block(x, 3, [128, 128, 512], stage=3, block='d')
# 卷积块
x = conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='b')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='c')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='d')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='e')
x = identity_block(x, 3, [256, 256, 1024], stage=4, block='f')
# 卷积块
x = conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
x = identity_block(x, 3, [512, 512, 2048], stage=5, block='c')
# 平均池化层
x = AveragePooling2D((7, 7), name='avg_pool')(x)
# 全连接层
x = Flatten()(x)
x = Dense(classes, activation='softmax', name='fc1000')(x)
# 构建模型
model = Model(img_input, x, name='resnet50v2')
return model
```
5. 编译模型并训练
```python
model = ResNet50V2(input_shape=(224, 224, 3), classes=1000)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)
```
以上就是使用TensorFlow搭建ResNet50V2的步骤。
tensorflow2搭建ResNet50V2
以下是使用TensorFlow 2搭建ResNet50V2的步骤:
1. 导入必要的库和模块
```
import tensorflow as tf
from tensorflow.keras.applications.resnet_v2 import ResNet50V2
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.models import Model
```
2. 加载ResNet50V2模型
```
base_model = ResNet50V2(include_top=False, weights='imagenet', input_shape=(224, 224, 3))
```
3. 冻结前面的层
```
for layer in base_model.layers:
layer.trainable = False
```
4. 添加自定义的全连接层
```
x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
```
其中,`num_classes`为分类数目。
5. 创建新的模型
```
model = Model(inputs=base_model.input, outputs=predictions)
```
6. 编译模型
```
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
```
7. 训练模型
```
model.fit(train_images, train_labels, epochs=num_epochs, validation_data=(val_images, val_labels))
```
其中,`train_images`和`train_labels`为训练集图片和标签,`val_images`和`val_labels`为验证集图片和标签,`num_epochs`为训练轮数。
8. 评估模型
```
model.evaluate(test_images, test_labels)
```
其中,`test_images`和`test_labels`为测试集图片和标签。
完成以上步骤,就可以使用TensorFlow 2搭建ResNet50V2模型了。