inFlow = NLP.addVars(10,name="inFlow") outFlow = NLP.addVars(10,name="outFlow") platInject = NLP.addVars(10,name="outFlow") print(type(platInject)) print(type(platInject[1])) for p in Platforms: q = Platforms.index(p) inFlow[p] = quicksum(PipeFlow[i, p] for i in Platforms if (i, p) in Capacities) outFlow[p] = quicksum(PipeFlow[p, j] for j in Platforms if (p, j) in Capacities) for i in range(len(wellFlow[q])): platInject[p]=quicksum(wellFlow[q][i])发生异常'Var' object is not iterable

The error seems to be occurring at the line:

platInject[p]=quicksum(wellFlow[q][i])

This is because platInject is defined as a Var object, which is not iterable. You are trying to assign a value to a Var object using the index operator. Instead, you should define platInject as a dictionary:

platInject = {}

Then, you can assign values to platInject using the dictionary syntax:

platInject[p] = quicksum(wellFlow[q][i])

This should resolve the error.

相关问题

import tensorflow as tf import numpy as np from keras import Model from keras.layers import * from sklearn.model_selection import train_test_split in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error') 数据划分及其归一化，模型的训练和预测输出

首先，你需要将数据划分为训练集和测试集，并进行归一化处理。可以使用 sklearn 中的 train_test_split 函数将数据集分为训练集和测试集，然后对数据进行归一化处理。

from sklearn.preprocessing import StandardScaler

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(in_flow, out_flow, test_size=0.2, random_state=42)

# 归一化处理
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

接下来，您可以使用 fit 方法训练模型，并使用 predict 方法进行预测：

# 训练模型
model.fit([c1, X_train, X_train], y_train, epochs=10, batch_size=32)

# 预测输出
y_pred = model.predict([c1, X_test, X_test])

最后，您可以计算预测输出与真实值之间的均方误差（MSE）：

from sklearn.metrics import mean_squared_error

mse = mean_squared_error(y_test, y_pred)
print("MSE: ", mse)

下述代码缺少什么import tensorflow as tf import numpy as np from keras import Model in_flow= np.load("X_in_30od.npy") out_flow= np.load("X_out_30od.npy") c1 = np.load("X_30od.npy") D1 = np.load("Y_30od.npy") print(train_in_flow.shape) print(val_in_flow.shape) print(test_in_flow.shape) from keras.layers import * input_od=Input(shape=(5,109,109)) x1=Reshape((5,109,109,1),input_shape=(5,109,109))(input_od) x1=ConvLSTM2D(filters=64,kernel_size=(3,3),activation='relu',padding='same',input_shape=(5,109,109,1))(x1) x1=Dropout(0.2)(x1) x1=Dense(1)(x1) x1=Reshape((109,109))(x1) input_inflow=Input(shape=(5,109)) x2=Permute((2,1))(input_inflow) x2=LSTM(109,return_sequences=True,activation='sigmoid')(x2) x2=Dense(109,activation='sigmoid')(x2) x2=tf.multiply(x1,x2) x2=Dense(109,activation='sigmoid')(x2) input_inflow2=Input(shape=(5,109)) x3=Permute([2,1])(input_inflow2) x3=LSTM(109,return_sequences=True,activation='sigmoid')(x3) x3=Dense(109,activation='sigmoid')(x3) x3 = Reshape((109, 109))(x3) x3=tf.multiply(x1,x3) x3=Dense(109,activation='sigmoid')(x3) mix=Add()([x2,x3]) mix=Bidirectional(LSTM(109,return_sequences=True,activation='sigmoid'))(mix) mix=Dense(109,activation='sigmoid')(mix) model= Model(inputs=[input_od,input_inflow,input_inflow2],outputs=[mix]) model.compile(optimizer='adam', loss='mean_squared_error')

The code is missing the following imports:

from keras.layers import Input, ConvLSTM2D, Dropout, Dense, Permute, LSTM, Multiply, Reshape, Add, Bidirectional

These are necessary for defining the model architecture.