tensorflow2用DPCNN和GCN预测数据，输入绝对路径下的一列的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，这个问题包含了很多内容，需要分步骤回答。我将尽力回答您的问题，但是请注意，这里的代码只是示例，并且可能需要根据您的具体情况进行修改。

1. 加载数据：

import pandas as pd
import numpy as np

# 读取csv文件
data = pd.read_csv('path/to/your/csv/file.csv', header=None)

# 将数据分为输入和输出
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 将输出转换为one-hot编码（如果是分类问题）
from tensorflow.keras.utils import to_categorical
y = to_categorical(y)

2. 划分数据集：

from sklearn.model_selection import train_test_split

# 划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=42)

3. 构建模型：

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, Conv1D, MaxPooling1D, GlobalMaxPooling1D, Concatenate

# 输入层
inputs = Input(shape=(X_train.shape[1],))

# DPCNN
conv1 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(inputs)
conv2 = Conv1D(filters=64, kernel_size=3, padding='same', activation='relu')(conv1)
pool1 = MaxPooling1D(pool_size=2)(conv2)

conv3 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')(pool1)
conv4 = Conv1D(filters=128, kernel_size=3, padding='same', activation='relu')(conv3)
pool2 = MaxPooling1D(pool_size=2)(conv4)

conv5 = Conv1D(filters=256, kernel_size=3, padding='same', activation='relu')(pool2)
conv6 = Conv1D(filters=256, kernel_size=3, padding='same', activation='relu')(conv5)
conv7 = Conv1D(filters=256, kernel_size=3, padding='same', activation='relu')(conv6)
pool3 = MaxPooling1D(pool_size=2)(conv7)

# 将输入和DPCNN的输出连接
concat = Concatenate()([inputs, GlobalMaxPooling1D()(pool3)])

# GCN
dense1 = Dense(units=512, activation='relu')(concat)
dropout1 = Dropout(rate=0.5)(dense1)
dense2 = Dense(units=256, activation='relu')(dropout1)
dropout2 = Dropout(rate=0.5)(dense2)
dense3 = Dense(units=128, activation='relu')(dropout2)
dropout3 = Dropout(rate=0.5)(dense3)
outputs = Dense(units=y.shape[1], activation='softmax')(dropout3)

# 定义模型
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

4. 训练模型：

history = model.fit(X_train, y_train, validation_split=0.05, epochs=100, batch_size=32)

5. 可视化结果：

import matplotlib.pyplot as plt

# 绘制训练集和验证集的loss曲线
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.legend()
plt.show()

# 绘制训练集和验证集的accuracy曲线
plt.plot(history.history['accuracy'], label='train_acc')
plt.plot(history.history['val_accuracy'], label='val_acc')
plt.legend()
plt.show()

# 在测试集上评估模型
test_loss, test_acc = model.evaluate(X_test, y_test)
print('Test loss:', test_loss)
print('Test accuracy:', test_acc)

希望这些代码可以帮到您！