ResNet的python代码
时间: 2023-07-23 21:40:40 浏览: 47
以下是ResNet的Python代码实现:
```python
import tensorflow as tf
def identity_block(input_tensor, kernel_size, filters, stage, block):
"""Identity block"""
filters1, filters2, filters3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = tf.keras.layers.Conv2D(filters1, (1, 1), name=conv_name_base + '2a')(input_tensor)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2a')(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2b')(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2c')(x)
x = tf.keras.layers.add([x, input_tensor])
x = tf.keras.layers.Activation('relu')(x)
return x
def convolutional_block(input_tensor, kernel_size, filters, stage, block, strides=(2, 2)):
"""Convolutional block"""
filters1, filters2, filters3 = filters
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = tf.keras.layers.Conv2D(filters1, (1, 1), strides=strides, name=conv_name_base + '2a')(input_tensor)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2a')(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same', name=conv_name_base + '2b')(x)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2b')(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.Conv2D(filters3, (1, 1), name=conv_name_base + '2c')(x)
x = tf.keras.layers.BatchNormalization(name=bn_name_base + '2c')(x)
shortcut = tf.keras.layers.Conv2D(filters3, (1, 1), strides=strides, name=conv_name_base + '1')(input_tensor)
shortcut = tf.keras.layers.BatchNormalization(name=bn_name_base + '1')(shortcut)
x = tf.keras.layers.add([x, shortcut])
x = tf.keras.layers.Activation('relu')(x)
return x
def ResNet50(input_shape=(224, 224, 3), classes=1000):
"""ResNet-50"""
img_input = tf.keras.layers.Input(shape=input_shape)
x = tf.keras.layers.ZeroPadding2D(padding=(3, 3))(img_input)
x = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
x = tf.keras.layers.BatchNormalization(name='bn_conv1')(x)
x = tf.keras.layers.Activation('relu')(x)
x = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
x = convolutional_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')
x = convolutional_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')
x = convolutional_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')
x = convolutional_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')
x = tf.keras.layers.AveragePooling2D((7, 7), name='avg_pool')(x)
x = tf.keras.layers.Flatten()(x)
x = tf.keras.layers.Dense(classes, activation='softmax', name='fc1000')(x)
model = tf.keras.models.Model(img_input, x, name='resnet50')
return model
```