如何使用 Python 使用编码器和解码器生成自动编码器？
Tensorflow是Google提供的机器学习框架。它是一个与Python结合使用以实现算法、深度学习应用程序等的开源框架。它用于研究和生产目的。
可以使用以下代码行在Windows上安装“tensorflow”包-
pip install tensorflow
Tensor是TensorFlow中使用的一种数据结构。它有助于连接流程图中的边。该流程图被称为“数据流图”。张量只不过是一个多维数组或列表。
Keras是作为ONEIROS（开放式神经电子智能机器人操作系统）项目研究的一部分而开发的。Keras是一个深度学习API，它是用Python编写的。它是一种高级API，具有有助于解决机器学习问题的高效界面。它运行在Tensorflow框架之上。它旨在帮助快速进行实验。它提供了在开发和封装机器学习解决方案中必不可少的基本抽象和构建块。
Keras已经存在于Tensorflow包中。可以使用以下代码行访问它。
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结构的模型，可以共享层并处理多个输入和输出。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。
我们正在使用GoogleColaboratory运行以下代码。GoogleColab或Colaboratory帮助在浏览器上运行Python代码，并且需要零配置和免费访问GPU（图形处理单元）。Colaboratory建立在JupyterNotebook之上。以下是代码片段，了解如何使用编码器和解码器生成自动编码器-
示例
encoder_input = keras.Input(shape=(28, 28, 1), name="img")
print("Adding layers to the model")
x = layers.Conv2D(16, 3, activation="relu")(encoder_input)
x = layers.Conv2D(32, 3, activation="relu")(x)
x = layers.MaxPooling2D(3)(x)
x = layers.Conv2D(32, 3, activation="relu")(x)
x = layers.Conv2D(16, 3, activation="relu")(x)
print("Performing global max pooling")
encoder_output = layers.GlobalMaxPooling2D()(x)
print("Creating a model using the layers")
encoder = keras.Model(encoder_input, encoder_output, name="encoder")
print("More information about the model")
encoder.summary()
print("Reshaping the layers in the model")
x = layers.Reshape((4, 4, 1))(encoder_output)
x = layers.Conv2DTranspose(16, 3, activation="relu")(x)
x = layers.Conv2DTranspose(32, 3, activation="relu")(x)
x = layers.UpSampling2D(3)(x)
x = layers.Conv2DTranspose(16, 3, activation="relu")(x)
decoder_output = layers.Conv2DTranspose(1, 3, activation="relu")(x)
autoencoder = keras.Model(encoder_input, decoder_output, name="autoencoder")
print("More information about the autoencoder")
autoencoder.summary()代码信用-https://www.tensorflow.org/guide/keras/functional
输出结果
Adding layers to the model Performing global max pooling Creating a model using the layers More information about the model Model: "encoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= img (InputLayer) [(None, 28, 28, 1)] 0 _________________________________________________________________ conv2d (Conv2D) (None, 26, 26, 16) 160 _________________________________________________________________ conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 _________________________________________________________________ conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 _________________________________________________________________ global_max_pooling2d (Global (None, 16) 0 ================================================================= Total params: 18,672 Trainable params: 18,672 Non-trainable params: 0 _________________________________________________________________ Reshaping the layers in the model More information about the autoencoder Model: "autoencoder" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= img (InputLayer) [(None, 28, 28, 1)] 0 _________________________________________________________________ conv2d (Conv2D) (None, 26, 26, 16) 160 _________________________________________________________________ conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 _________________________________________________________________ max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 _________________________________________________________________ conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 _________________________________________________________________ global_max_pooling2d (Global (None, 16) 0 _________________________________________________________________ reshape (Reshape) (None, 4, 4, 1) 0 _________________________________________________________________ conv2d_transpose (Conv2DTran (None, 6, 6, 16) 160 _________________________________________________________________ conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32) 4640 _________________________________________________________________ up_sampling2d (UpSampling2D) (None, 24, 24, 32) 0 _________________________________________________________________ conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16) 4624 _________________________________________________________________ conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1) 145 ================================================================= Total params: 28,241 Trainable params: 28,241 Non-trainable params: 0 _________________________________________________________________
解释
层被添加到模型中。
在这些层上执行全局最大池化
使用层创建模型。
可以使用“摘要”方法显示有关模型的更多信息。
使用函数式API，在指定层图的输入和输出后创建模型。
这表明单个图可用于生成多个模型。
在这里，层堆栈用于实例化两个模型——一个将图像输入转换为16维向量的编码器和一个用于训练的自动编码器模型。