编写利用Resnet50去噪的python代码

好的，这个问题可以回答，以下是基于ResNet50的Python代码示例：

import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.applications.resnet50 import ResNet50

# 加载 ResNet50 模型并去掉顶部分类层和最后一层激活函数
resnet50 = ResNet50(weights='imagenet', include_top=False,
                    input_shape=(224, 224, 3), pooling=None)
resnet50.layers.pop()
resnet50.layers.pop()

# 创建去噪卷积神经网络
model = tf.keras.Sequential([
    layers.Input(shape=(224, 224, 3)),
    layers.Lambda(lambda image: tf.image.resize(image, (224, 224))),
    layers.GaussianNoise(0.1),
    resnet50,
    layers.Conv2DTranspose(filters=256, kernel_size=3, strides=2, padding='same', activation='relu'),
    layers.Conv2DTranspose(filters=128, kernel_size=3, strides=2, padding='same', activation='relu'),
    layers.Conv2DTranspose(filters=64, kernel_size=3, strides=2, padding='same', activation='relu'),
    layers.Conv2D(3, kernel_size=(3, 3), strides=(1, 1), padding='same'),
])

# 编译模型
model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError())

# 训练模型
history = model.fit(train_images, train_labels,
                    validation_data=(validation_images, validation_labels),
                    epochs=10, verbose=2)

当然，以上代码仅仅是个示例，具体的实现还要根据你的具体需求而定。