在tensorflow实现用动物园算法和lstm对时序数据进行预测，输入训练集数据为一列的csv格式数据并指定绝对路径，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，我会尽力回答你的问题。首先需要安装tensorflow和matplotlib库，然后可以按照以下步骤实现：

1. 导入必要的库

import tensorflow as tf
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

2. 读取训练集数据并进行预处理

data = pd.read_csv('path/to/csv/file')
train_data = data.iloc[:int(len(data)*0.95), 0]
test_data = data.iloc[int(len(data)*0.95):, 0]

# 数据归一化处理
train_min = np.min(train_data)
train_max = np.max(train_data)
train_data = (train_data - train_min) / (train_max - train_min)
test_data = (test_data - train_min) / (train_max - train_min)

3. 构建训练数据集和测试数据集

# 定义时间步长
time_step = 10

# 构建训练数据集
def create_train_data(train_data, time_step):
    train_x, train_y = [], []
    for i in range(len(train_data)-time_step):
        x = train_data[i:i+time_step]
        y = train_data[i+time_step]
        train_x.append(x.tolist())
        train_y.append(y.tolist())
    return np.array(train_x), np.array(train_y)

train_x, train_y = create_train_data(train_data, time_step)

# 构建测试数据集
def create_test_data(test_data, time_step):
    test_x, test_y = [], []
    for i in range(len(test_data)-time_step):
        x = test_data[i:i+time_step]
        y = test_data[i+time_step]
        test_x.append(x.tolist())
        test_y.append(y.tolist())
    return np.array(test_x), np.array(test_y)

test_x, test_y = create_test_data(test_data, time_step)

4. 构建模型并进行训练

# 定义模型
model = tf.keras.Sequential([
    tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(time_step, 1)),
    tf.keras.layers.LSTM(50),
    tf.keras.layers.Dense(1)
])

# 定义优化器和损失函数
model.compile(optimizer='adam', loss='mean_squared_error')

# 进行模型训练
history = model.fit(train_x, train_y, epochs=100, batch_size=32, validation_data=(test_x, test_y))

5. 绘制loss和accuracy曲线

# 绘制loss曲线
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='test_loss')
plt.legend()
plt.show()

# 绘制accuracy曲线
plt.plot(history.history['accuracy'], label='train_acc')
plt.plot(history.history['val_accuracy'], label='test_acc')
plt.legend()
plt.show()

6. 进行预测并绘制预测值和实际值曲线

# 进行预测
predict_y = model.predict(test_x)

# 反归一化处理
predict_y = predict_y * (train_max - train_min) + train_min
test_y = test_y * (train_max - train_min) + train_min

# 绘制预测值和实际值曲线
plt.plot(predict_y, label='predict')
plt.plot(test_y, label='true')
plt.legend()
plt.show()

希望这些步骤可以帮助你实现用动物园算法和lstm对时序数据进行预测，并将预测值和实际值画曲线显示。