写出对一维堆叠降噪自编码器进行改进的python代码
时间: 2024-05-14 18:16:36 浏览: 8
以下是一维堆叠降噪自编码器的改进代码:
```python
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Dropout
from tensorflow.keras.models import Model
# 构建模型
def build_model(input_shape, noise_factor):
# 输入层
input_layer = Input(shape=input_shape)
# 加噪层
noisy_input = Dropout(noise_factor)(input_layer)
# 编码层
encoded = Dense(64, activation='relu')(noisy_input)
encoded = Dense(32, activation='relu')(encoded)
# 解码层
decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(input_shape[0], activation='sigmoid')(decoded)
# 定义模型
autoencoder = Model(input_layer, decoded)
# 编译模型
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')
return autoencoder
# 训练模型
def train_model(model, x_train, x_test, batch_size, epochs):
model.fit(x_train, x_train,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
validation_data=(x_test, x_test))
# 测试模型
def test_model(model, x_test):
decoded_imgs = model.predict(x_test)
# 可视化测试结果
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# 原始图片
ax = plt.subplot(2, n, i+1)
plt.imshow(x_test[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# 解码后的图片
ax = plt.subplot(2, n, i+1+n)
plt.imshow(decoded_imgs[i].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
# 加载数据
(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), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
# 噪声因子
noise_factor = 0.5
# 添加噪声
x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)
# 截断噪声
x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
# 构建模型
model = build_model((784,), noise_factor)
# 训练模型
train_model(model, x_train_noisy, x_test_noisy, batch_size=128, epochs=20)
# 测试模型
test_model(model, x_test_noisy)
```
这里我们对原始的一维堆叠降噪自编码器进行了改进,添加了一个 Dropout 层来对输入数据进行随机失活,从而达到正则化的效果。同时我们也增加了一些可视化的代码来展示模型的测试结果。