自编码器python代码
时间: 2023-11-12 19:59:11 浏览: 30
好的,以下是一个简单的自编码器的 Python 代码示例:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
# 定义输入层
input_layer = Input(shape=(784,))
# 定义编码层
encoded = Dense(128, activation='relu')(input_layer)
encoded = Dense(64, activation='relu')(encoded)
encoded = Dense(32, activation='relu')(encoded)
# 定义解码层
decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)
# 定义自编码器模型
autoencoder = Model(input_layer, decoded)
# 编译模型
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
# 加载数据集
(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()
# 数据预处理
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), 784))
x_test = x_test.reshape((len(x_test), 784))
# 训练模型
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
```
这个自编码器使用了三个编码层和三个解码层,中间的编码层的维度为 32。它使用 MNIST 数据集进行训练,训练过程中使用了 Adam 优化器和二元交叉熵损失函数。