写一个bp神经网络做预测的程序_bp神经网络回归预测代码python

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
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接下来，我们准备数据。假设我们有一组时间序列数据，其中前两列是输入特征，最后一列是输出结果。我们将数据分为训练集和测试集，并进行归一化处理：

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
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接下来，我们建立一个三层的BP神经网络模型，其中第一层有64个神经元，第二层有32个神经元，输出层只有一个神经元：

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)
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接下来进行模型训练：

history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)
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最后，我们可以使用该模型进行预测：

predict = model.predict(X_test)
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完整代码如下：

# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

# 建立模型
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)

# 训练模型
history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)

# 预测值
predict = model.predict(X_test)
```我们首先需要导入必要的库：

```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
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接下来，我们准备数据。假设我们有一组时间序列数据，其中前两列是输入特征，最后一列是输出结果。我们将数据分为训练集和测试集，并进行归一化处理：

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
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接下来，我们建立一个三层的BP神经网络模型，其中第一层有64个神经元，第二层有32个神经元，输出层只有一个神经元：

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)
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接下来进行模型训练：

history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)
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最后，我们可以使用该模型进行预测：

predict = model.predict(X_test)
1

完整代码如下：

# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

# 建立模型
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)

# 训练模型
history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)

# 预测值
predict = model.predict(X_test)
```我们首先需要导入必要的库：

```python
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
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57

接下来，我们准备数据。假设我们有一组时间序列数据，其中前两列是输入特征，最后一列是输出结果。我们将数据分为训练集和测试集，并进行归一化处理：

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))
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3
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接下来，我们建立一个三层的BP神经网络模型，其中第一层有64个神经元，第二层有32个神经元，输出层只有一个神经元：

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)
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接下来进行模型训练：

history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)
1

最后，我们可以使用该模型进行预测：

predict = model.predict(X_test)
1

完整代码如下：

# 导入必要的库
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam

# 读取数据
data = pd.read_csv('data.csv')

# 划分数据集
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

# 特征归一化
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

# 划分输入和输出
X_train, y_train = train_data[:, :-1], train_data[:, -1]
X_test, y_test = test_data[:, :-1], test_data[:, -1]

# 将输入转换为三维数组
X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
X_test = X_test.reshape((X_test.shape[0], X_test.shape[1], 1))

# 建立模型
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1))

# 定义优化器
adam = Adam(lr=0.001)

# 编译模型
model.compile(loss='mse', optimizer=adam)

# 训练模型
history = model.fit(X_train, y_train, epochs=100, batch_size=16, validation_data=(X_test, y_test), verbose=2, shuffle=False)

# 预测值
predict = model.predict(X_test)
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该博文为原创文章，未经博主同意不得转。本文章博客地址：https://cplusplus.blog.csdn.net/article/details/133968628