深度残差网络代码
时间: 2023-08-06 10:11:46 浏览: 46
以下是深度残差网络(ResNet)的代码示例,实现了一个18层的ResNet。
```python
import tensorflow as tf
def identity_block(X, f, filters):
F1, F2, F3 = filters
X_shortcut = X
X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Activation('relu')(X)
X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Activation('relu')(X)
X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Add()([X, X_shortcut])
X = tf.keras.layers.Activation('relu')(X)
return X
def conv_block(X, f, filters, s=2):
F1, F2, F3 = filters
X_shortcut = X
X = tf.keras.layers.Conv2D(filters=F1, kernel_size=(1, 1), strides=(s, s), padding='valid')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Activation('relu')(X)
X = tf.keras.layers.Conv2D(filters=F2, kernel_size=(f, f), strides=(1, 1), padding='same')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Activation('relu')(X)
X = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(1, 1), padding='valid')(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X_shortcut = tf.keras.layers.Conv2D(filters=F3, kernel_size=(1, 1), strides=(s, s), padding='valid')(X_shortcut)
X_shortcut = tf.keras.layers.BatchNormalization(axis=3)(X_shortcut)
X = tf.keras.layers.Add()([X, X_shortcut])
X = tf.keras.layers.Activation('relu')(X)
return X
def ResNet18(input_shape=(64, 64, 3), classes=6):
X_input = tf.keras.layers.Input(input_shape)
X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)
X = tf.keras.layers.Conv2D(64, (7, 7), strides=(2, 2))(X)
X = tf.keras.layers.BatchNormalization(axis=3)(X)
X = tf.keras.layers.Activation('relu')(X)
X = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(X)
X = conv_block(X, f=3, filters=[64, 64, 256], s=1)
X = identity_block(X, 3, [64, 64, 256])
X = identity_block(X, 3, [64, 64, 256])
X = conv_block(X, f=3, filters=[128, 128, 512], s=2)
X = identity_block(X, 3, [128, 128, 512])
X = identity_block(X, 3, [128, 128, 512])
X = conv_block(X, f=3, filters=[256, 256, 1024], s=2)
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = conv_block(X, f=3, filters=[512, 512, 2048], s=2)
X = identity_block(X, 3, [512, 512, 2048])
X = identity_block(X, 3, [512, 512, 2048])
X = tf.keras.layers.AveragePooling2D(pool_size=(2, 2))(X)
X = tf.keras.layers.Flatten()(X)
X = tf.keras.layers.Dense(classes, activation='softmax')(X)
model = tf.keras.models.Model(inputs=X_input, outputs=X, name='ResNet18')
return model
```
上面代码中,`identity_block` 和 `conv_block` 分别表示 ResNet 中的残差块和卷积块。`ResNet18` 函数则构建了一个18层的ResNet模型。
注意,在实际使用中需要根据具体的数据集和任务进行调整和优化。