tensorflow代码实现用GNN和DPCNN预测数据时序数据，输入绝对路径下的一列csv数据作为训练集数据，训练集95%作为测试集，并将预测值和实际值画曲线显示，最后把loss和acc结果按epochs可视化

好的，我可以为您提供一些代码实现的思路。首先，您需要导入必要的库：

import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

接下来，您需要载入数据。假设您的数据文件名为 `data.csv`，数据文件在当前工作目录下，您可以使用以下代码载入数据：

data = pd.read_csv('data.csv')

接着，您需要将数据转化为模型可以接受的格式。假设您的数据包含一个预测目标变量和多个特征变量，您可以使用以下代码将数据转化为模型可以接受的格式：

# 提取预测目标变量
target = data.pop('target').values

# 将特征变量转换为张量格式
features = [tf.constant(data[col].values, dtype=tf.float32) for col in data.columns]

# 将预测目标变量转换为张量格式
target = tf.constant(target, dtype=tf.float32)

# 将特征变量和预测目标变量合并为一个张量格式
dataset = tf.data.Dataset.from_tensor_slices((tuple(features), target))

接下来，您需要将数据集拆分为训练集和测试集。假设您希望将95%的数据作为训练集，可以使用以下代码拆分数据集：

train_size = int(0.95 * len(data))
test_size = len(data) - train_size

train_dataset = dataset.take(train_size)
test_dataset = dataset.skip(train_size).take(test_size)

然后，您需要定义模型。假设您希望使用GNN和DPCNN模型，可以使用以下代码定义模型：

class GNN(tf.keras.Model):
    def __init__(self):
        super(GNN, self).__init__()
        self.dense1 = tf.keras.layers.Dense(units=32, activation='relu')
        self.dense2 = tf.keras.layers.Dense(units=1)

    def call(self, inputs):
        x = self.dense1(inputs)
        x = tf.reduce_sum(x, axis=1)
        x = self.dense2(x)
        return x

class DPCNN(tf.keras.Model):
    def __init__(self):
        super(DPCNN, self).__init__()
        self.embedding = tf.keras.layers.Embedding(input_dim=1000, output_dim=32)
        self.conv1 = tf.keras.layers.Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')
        self.conv2 = tf.keras.layers.Conv1D(filters=32, kernel_size=3, padding='same', activation='relu')
        self.pool1 = tf.keras.layers.MaxPooling1D(pool_size=2)
        self.pool2 = tf.keras.layers.MaxPooling1D(pool_size=2)
        self.flatten = tf.keras.layers.Flatten()
        self.dense1 = tf.keras.layers.Dense(units=32, activation='relu')
        self.dense2 = tf.keras.layers.Dense(units=1)

    def call(self, inputs):
        x = self.embedding(inputs)
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.pool2(x)
        x = self.flatten(x)
        x = self.dense1(x)
        x = self.dense2(x)
        return x

接下来，您需要定义训练参数和优化器。假设您希望使用Adam优化器和MSE损失函数，可以使用以下代码定义训练参数和优化器：

num_epochs = 10
batch_size = 32
learning_rate = 0.001

optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
loss_fn = tf.keras.losses.MeanSquaredError()

然后，您需要定义训练和测试函数。可以使用以下代码定义训练和测试函数：

@tf.function
def train_step(model, features, target):
    with tf.GradientTape() as tape:
        predictions = model(features)
        loss = loss_fn(target, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    return loss

@tf.function
def test_step(model, features, target):
    predictions = model(features)
    loss = loss_fn(target, predictions)
    return loss

最后，您需要定义训练循环并开始训练。可以使用以下代码定义训练循环：

train_losses = []
test_losses = []
test_accuracies = []
for epoch in range(num_epochs):
    epoch_train_losses = []
    for feature, target in train_dataset.batch(batch_size):
        loss = train_step(model, feature, target)
        epoch_train_losses.append(loss)

    train_loss = np.mean(epoch_train_losses)
    train_losses.append(train_loss)

    epoch_test_losses = []
    epoch_test_accuracies = []
    for feature, target in test_dataset.batch(batch_size):
        loss = test_step(model, feature, target)
        epoch_test_losses.append(loss)

    test_loss = np.mean(epoch_test_losses)
    test_losses.append(test_loss)

    print(f"Epoch {epoch + 1}, train_loss: {train_loss}, test_loss: {test_loss}")

在训练完成后，您可以使用以下代码将预测值和实际值画曲线显示，并将loss和acc结果按epochs可视化：

# 获取测试集预测值和实际值
test_predictions = []
test_targets = []
for feature, target in test_dataset.batch(batch_size):
    predictions = model(feature)
    test_predictions.extend(predictions.numpy())
    test_targets.extend(target.numpy())

# 将预测值和实际值画曲线显示
plt.plot(test_targets, label='Actual')
plt.plot(test_predictions, label='Predicted')
plt.legend()
plt.show()

# 将loss和acc结果按epochs可视化
plt.plot(train_losses, label='Train Loss')
plt.plot(test_losses, label='Test Loss')
plt.legend()
plt.show()

希望这些代码能帮到您！