使用Keras和Kedro构建自动编码器

# 安装依赖
# pip install keras kedro

import numpy as np
from keras.layers import Input, Dense
from keras.models import Model
from kedro.pipeline import Pipeline, node
from kedro.io import MemoryDataSet

# 生成一些示例数据
data = np.random.random((1000, 784))
data = data.reshape((1000, 28, 28))

# 定义自动编码器模型
input_img = Input(shape=(784,))
encoded = Dense(128, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)

# 编译模型
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# 训练模型
autoencoder.fit(data, data,
                epochs=50,
                batch_size=256,
                shuffle=True,
                validation_data=(data, data))

# Kedro Pipeline示例
def create_autoencoder():
    input_img = Input(shape=(784,))
    encoded = Dense(128, activation='relu')(input_img)
    decoded = Dense(784, activation='sigmoid')(encoded)
    autoencoder = Model(input_img, decoded)
    autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
    return autoencoder

def train_autoencoder(autoencoder, data):
    autoencoder.fit(data, data,
                    epochs=50,
                    batch_size=256,
                    shuffle=True,
                    validation_data=(data, data))

# 创建Kedro Pipeline
pipeline = Pipeline([
    node(create_autoencoder, 'input_data', 'autoencoder'),
    node(train_autoencoder, ['autoencoder', 'input_data'], 'trained_autoencoder')
])

# 使用MemoryDataSet存储数据
input_data = MemoryDataSet(data)
output_data = MemoryDataSet()

# 运行Pipeline
pipeline.run({'input_data': input_data})