sae降维的python

在Python中，进行SAE（Stacked Autoencoder）降维可以使用多种库和框架来实现，其中最常用的是使用深度学习框架TensorFlow和Keras。以下是一个使用TensorFlow实现SAE降维的示例代码：

import tensorflow as tf

# 定义Stacked Autoencoder的模型
def autoencoder(input_shape):
    # 编码器
    encoder = tf.keras.Sequential([
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(32, activation='relu')
    ])
    # 解码器
    decoder = tf.keras.Sequential([
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dense(input_shape, activation='sigmoid')
    ])
    # 构建完整的SAE模型
    model = tf.keras.Sequential([encoder, decoder])
    return model

# 加载数据集
(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()
# 数据预处理
x_train = x_train.reshape(-1, 784) / 255.0
x_test = x_test.reshape(-1, 784) / 255.0

# 创建SAE模型
input_shape = x_train.shape<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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