请编写一个基于keras的深度残差收缩网络，实现对格式为csv的三维时序信号去噪，并实现现去噪的可视化和将去噪后的数据以csv格式保存

以下是一个基于Keras的深度残差收缩网络，用于对格式为csv的三维时序信号去噪：

import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, Conv1D, MaxPooling1D, UpSampling1D, Concatenate, BatchNormalization, Activation, Add

def residual_block(x, filters, kernel_size, dilation_rate):
    r = Conv1D(filters=filters, kernel_size=1)(x)
    r = BatchNormalization()(r)
    r = Activation('relu')(r)
    r = Conv1D(filters=filters, kernel_size=kernel_size, padding='same', dilation_rate=dilation_rate)(r)
    r = BatchNormalization()(r)
    r = Activation('relu')(r)
    r = Conv1D(filters=filters, kernel_size=1)(r)
    r = BatchNormalization()(r)
    x = Conv1D(filters=filters, kernel_size=1)(x)
    x = Add()([x, r])
    return x

def build_model(input_shape):
    inputs = Input(shape=input_shape)
    x = Conv1D(filters=64, kernel_size=3, padding='same')(inputs)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)
    for i in range(4):
        for j in range(2):
            x = residual_block(x, filters=64, kernel_size=3, dilation_rate=2**j)
    x = Conv1D(filters=1, kernel_size=3, padding='same')(x)
    x = BatchNormalization()(x)
    outputs = Activation('linear')(x)
    model = Model(inputs=inputs, outputs=outputs)
    return model

def plot_data(data, name):
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 4))
    ax.plot(data)
    ax.set_title(name)
    ax.set_xlabel('Time step')
    ax.set_ylabel('Signal value')
    plt.show()

def load_data(filename):
    data = pd.read_csv(filename, header=None).values
    return data

def save_data(data, filename):
    pd.DataFrame(data).to_csv(filename, header=None, index=None)

def main():
    # Load data
    data = load_data('data.csv')
    plot_data(data, 'Original data')

    # Add noise
    noise = np.random.normal(0, 0.1, size=data.shape)
    noisy_data = data + noise
    plot_data(noisy_data, 'Noisy data')

    # Train model
    input_shape = noisy_data.shape[1:]
    model = build_model(input_shape)
    model.compile(optimizer='adam', loss='mse')
    model.fit(noisy_data, data, epochs=50, batch_size=32)

    # Denoise data
    denoised_data = model.predict(noisy_data)
    plot_data(denoised_data, 'Denoised data')

    # Save denoised data
    save_data(denoised_data, 'denoised_data.csv')

if __name__ == '__main__':
    main()

该模型包括卷积层、残差块、批量归一化层和激活层。训练之前，我们首先将原始数据加载到内存中，并添加高斯噪声。然后，我们使用带有均方误差损失的 Adam 优化器来训练模型。最后，我们使用训练的模型对噪声数据进行去噪，并将结果保存为 CSV 文件。