import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()
时间: 2024-02-16 12:59:46 浏览: 29
这段代码使用TensorFlow和Keras实现了一个神经网络模型,用于预测波士顿房价。以下是代码的解释:
首先,导入必要的库和模块:
```
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
from sklearn.preprocessing import scale
```
然后,读取波士顿房价数据集并对其进行预处理:
```
df = pd.read_csv("C:\\boston.csv",header=0)
ds = df.values
```
接着,从sklearn.datasets模块中加载波士顿房价数据集,并将其分为训练集和测试集:
```
from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
对训练集和测试集进行标准化处理:
```
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```
定义一个包含4个Dense层的神经网络模型:
```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
model = Sequential([
Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
Dense(64, activation='relu'),
Dense(64, activation='relu'),
Dense(1)
])
```
编译模型并训练:
```
model.compile(loss='mean_squared_error', optimizer='adam')
history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32)
```
使用模型对测试集进行预测,并计算均方误差:
```
from sklearn.metrics import mean_squared_error
y_pred = model.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:’, mse)
```
最后,绘制模型的训练和验证准确率曲线:
```
import matplotlib.pyplot as plt
plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='validation')
plt.legend()
plt.show()
```