如何使用函数式 API 处理 Python 中的残差连接?
keraspythonserver side programmingprogramming
Keras 位于 Tensorflow 包中。可以使用下面的代码行访问它。
import tensorflow from tensorflow import keras
与使用顺序 API 创建的模型相比，Keras 函数式 API 有助于创建更灵活的模型。函数式 API 可以处理具有非线性拓扑、可以共享层并处理多个输入和输出的模型。深度学习模型通常是包含多个层的有向无环图 (DAG)。函数式 API 有助于构建层图。
我们使用 Google Colaboratory 来运行以下代码。Google Colab 或 Colaboratory 有助于在浏览器上运行 Python 代码，并且不需要任何配置，并且可以免费访问 GPU(图形处理单元)。Colaboratory 是在 Jupyter Notebook 之上构建的。以下是代码片段；
示例
print("Toy ResNet model for CIFAR10")
print("Layers generated for model")
inputs = keras.Input(shape=(32, 32, 3), name="img")
x = layers.Conv2D(32, 3, activation="relu")(inputs)
x = layers.Conv2D(64, 3, activation="relu")(x)
block_1_output = layers.MaxPooling2D(3)(x)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_1_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_2_output = layers.add([x, block_1_output])
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_2_output)
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x)
block_3_output = layers.add([x, block_2_output])
x = layers.Conv2D(64, 3, activation="relu")(block_3_output)
x = layers.GlobalAveragePooling2D()(x)
x = layers.Dense(256, activation="relu")(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(10)(x)
model = keras.Model(inputs, outputs, name="toy_resnet")
print("More information about the model")
model.summary()
代码来源 − https://www.tensorflow.org/guide/keras/functional
输出
Toy ResNet model for CIFAR10 Layers generated for model More information about the model Model: "toy_resnet" ________________________________________________________________________________ __________________ Layer (type) Output Shape Param # Connected to ================================================================================ ================== img (InputLayer) [(None, 32, 32, 3)] 0 ________________________________________________________________________________ __________________ conv2d_32 (Conv2D) (None, 30, 30, 32) 896 img[0][0] ________________________________________________________________________________ __________________ conv2d_33 (Conv2D) (None, 28, 28, 64) 18496 conv2d_32[0][0] ________________________________________________________________________________ __________________ max_pooling2d_8 (MaxPooling2D) (None, 9, 9, 64) 0 conv2d_33[0][0] ________________________________________________________________________________ __________________ conv2d_34 (Conv2D) (None, 9, 9, 64) 36928 max_pooling2d_8[0][0] ________________________________________________________________________________ __________________ conv2d_35 (Conv2D) (None, 9, 9, 64) 36928 conv2d_34[0][0] ________________________________________________________________________________ __________________ add_12 (Add) (None, 9, 9, 64) 0 conv2d_35[0][0] max_pooling2d_8[0][0] ________________________________________________________________________________ __________________ conv2d_36 (Conv2D) (None, 9, 9, 64) 36928 add_12[0][0] ________________________________________________________________________________ __________________ conv2d_37 (Conv2D) (None, 9, 9, 64) 36928 conv2d_36[0][0] ________________________________________________________________________________ __________________ add_13 (Add) (None, 9, 9, 64) 0 conv2d_37[0][0] add_12[0][0] ________________________________________________________________________________ __________________ conv2d_38 (Conv2D) (None, 7, 7, 64) 36928 add_13[0][0] ________________________________________________________________________________ __________________ global_average_pooling2d_1 (Glo (None, 64) 0 conv2d_38[0][0] ________________________________________________________________________________ __________________ dense_40 (Dense) (None, 256) 16640 global_average_pooling2d_1[0][0] ________________________________________________________________________________ __________________ dropout_2 (Dropout) (None, 256) 0 dense_40[0][0] ________________________________________________________________________________ __________________ dense_41 (Dense) (None, 10) 2570 dropout_2[0][0] ================================================================================ ================== Total params: 223,242 Trainable params: 223,242 Non-trainable params: 0 ________________________________________________________________________________ __________________
解释
该模型具有多个输入和输出。
功能性 API 可轻松处理非线性连接拓扑。
此模型的各层不是按顺序连接的，因此 ‘Sequential’ API 无法处理它。
这就是残差连接发挥作用的地方。
使用 CIFAR10 构建了一个示例 ResNet 模型来演示这一点。
