一维卷积降噪自编码器python代码
时间: 2023-10-10 19:12:40 浏览: 155
以下是一维卷积降噪自编码器的Python代码:
```python
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Input, Conv1D, MaxPooling1D, UpSampling1D
from tensorflow.keras.models import Model
# 定义输入层
input_layer = Input(shape=(100, 1))
# 定义编码器
encoder = Conv1D(filters=16, kernel_size=3, activation='relu', padding='same')(input_layer)
encoder = MaxPooling1D(pool_size=2)(encoder)
encoder = Conv1D(filters=8, kernel_size=3, activation='relu', padding='same')(encoder)
encoder = MaxPooling1D(pool_size=2)(encoder)
encoder = Conv1D(filters=8, kernel_size=3, activation='relu', padding='same')(encoder)
encoded = MaxPooling1D(pool_size=2)(encoder)
# 定义解码器
decoder = Conv1D(filters=8, kernel_size=3, activation='relu', padding='same')(encoded)
decoder = UpSampling1D(size=2)(decoder)
decoder = Conv1D(filters=8, kernel_size=3, activation='relu', padding='same')(decoder)
decoder = UpSampling1D(size=2)(decoder)
decoder = Conv1D(filters=16, kernel_size=3, activation='relu')(decoder)
decoder = UpSampling1D(size=2)(decoder)
decoded = Conv1D(filters=1, kernel_size=3, activation='sigmoid', padding='same')(decoder)
# 定义自编码器模型
autoencoder = Model(input_layer, decoded)
# 编译模型
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
# 生成随机数据
data = np.random.rand(1000, 100, 1)
# 训练模型
autoencoder.fit(data, data, epochs=10, batch_size=32, validation_split=0.2)
```
这段代码定义了一个一维卷积降噪自编码器,其中编码器和解码器都包括了卷积层和池化层,用于对输入数据进行编码和解码。在训练模型时,使用了随机生成的数据,并将输入数据和输出数据都设置为相同的数据,以实现自编码器的训练。
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