data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]) data = data.reshape(1, -1, 1) print(data.shape) # 定义模型 input = keras.Input(shape=(9, 1)) output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear')(input) model = keras.Model(inputs=input, outputs=output, name="fs_feature") model.summary() # 获取模型中所有层的权重列表 weights_list = model.get_weights() print("权重列表：", weights_list) # 修改第一层的权重 weights = np.ones(3) / 3 weights_list[0] = np.transpose(np.array([weights])) # 设置所有层的权重 model.set_weights(weights_list) result = model.predict(data) print("原始数据：", data) print("滑动平均结果：", result.squeeze())出现错误

根据你提供的代码，我发现有一个错误：在修改第一层权重的代码中，应该使用np.array([weights])而不是np.transpose(np.array([weights]))。修改后的代码如下：

import numpy as np
from tensorflow import keras
from tensorflow.keras.layers import Conv1D

data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
data = data.reshape(1, -1, 1)
print(data.shape)

input = keras.Input(shape=(9, 1))
output = Conv1D(filters=1, kernel_size=3, padding='causal', activation='linear')(input)
model = keras.Model(inputs=input, outputs=output, name="fs_feature")
model.summary()

weights_list = model.get_weights()
print("权重列表：", weights_list)

weights = np.ones(3) / 3
weights_list[0] = np.array([weights])
model.set_weights(weights_list)

result = model.predict(data)
print("原始数据：", data)
print("滑动平均结果：", result.squeeze())

请注意，这里的滑动平均是通过卷积实现的。如果你想实现其他类型的滑动平均，请根据具体需求修改代码。