pycharm用lstm预测，csv文件中，输入为x、y两列，输出为z，并图表展示loss和预测效果

好的，首先我们需要导入一些必要的库：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

然后读取csv文件：

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

接着将x、y列作为输入数据，z列作为输出数据：

x = data[['x', 'y']].values
y = data['z'].values

将数据集划分为训练集和测试集：

train_size = int(len(x) * 0.7)
test_size = len(x) - train_size
x_train, x_test = x[0:train_size,:], x[train_size:len(x),:]
y_train, y_test = y[0:train_size], y[train_size:len(y)]

对输入数据进行归一化处理：

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler(feature_range=(0, 1))
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)

我们现在可以定义LSTM模型了：

model = Sequential()
model.add(LSTM(50, input_shape=(2, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')

训练模型：

history = model.fit(x_train.reshape(-1, 2, 1), y_train, epochs=100, batch_size=32, validation_data=(x_test.reshape(-1, 2, 1), y_test))

绘制模型的训练和验证集的loss曲线：

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()

最后预测结果并将其与真实值绘制在同一张图上：

train_predict = model.predict(x_train.reshape(-1, 2, 1))
test_predict = model.predict(x_test.reshape(-1, 2, 1))

train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])

test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])

plt.plot(y_train[0], label='train')
plt.plot(train_predict[:,0], label='train predict')
plt.plot(y_test[0], label='test')
plt.plot(test_predict[:,0], label='test predict')
plt.legend()
plt.show()

这样就完成了用LSTM预测并绘制图表的任务。