import matplotlib.pyplot as plt
from math import sqrt
from matplotlib import pyplot
import pandas as pd
from numpy import concatenate
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import Adam
import tensorflow
'''
keras实现神经网络回归模型
'''
读取数据
path = 'data001.csv'
删掉不用字符串字段
train = pd.read_csv(path)
dataset = train.iloc[1:,:]
df转array
values = dataset.values
原始数据标准化，为了加速收敛
scaler = MinMaxScaler(feature_range=(0, 1))
scaled = scaler.fit_transform(values)
y = scaled[:, -1]
X = scaled[:, 0:-1]
随机拆分训练集与测试集
from sklearn.model_selection import train_test_split
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3)
全连接神经网络
model = Sequential()
input = X.shape[1]
隐藏层128
model.add(Dense(128, input_shape=(input,)))
model.add(Activation('relu'))
Dropout层用于防止过拟合
model.add(Dropout(0.2))
隐藏层128
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
没有激活函数用于输出层，因为这是一个回归问题，我们希望直接预测数值，而不需要采用激活函数进行变换。
model.add(Dense(1))
使用高效的 ADAM 优化算法以及优化的最小均方误差损失函数
model.compile(loss='mean_squared_error', optimizer=Adam())
early stoppping
from keras.callbacks import EarlyStopping
early_stopping = EarlyStopping(monitor='val_loss', patience=50, verbose=2)
训练
history = model.fit(train_X, train_y, epochs=300, batch_size=20, validation_data=(test_X, test_y), verbose=2,
shuffle=False, callbacks=[early_stopping])
loss曲线
pyplot.plot(history.history['loss'], label='train')
pyplot.plot(history.history['val_loss'], label='test')
pyplot.legend()
pyplot.show()
预测
yhat = model.predict(test_X)
预测y逆标准化
inv_yhat0 = concatenate((test_X, yhat), axis=1)
inv_yhat1 = scaler.inverse_transform(inv_yhat0)
inv_yhat = inv_yhat1[:, -1]
原始y逆标准化
test_y = test_y.reshape((len(test_y), 1))
inv_y0 = concatenate((test_X, test_y), axis=1)
inv_y1 = scaler.inverse_transform(inv_y0)
inv_y = inv_y1[:, -1]
计算 RMSE
rmse = sqrt(mean_squared_error(inv_y, inv_yhat))
print('Test RMSE: %.3f' % rmse)
plt.plot(inv_y)
plt.plot(inv_yhat)
plt.show()
报错是：Traceback (most recent call last):
File "F:/SSD/CNN.py", line 24, in
scaled = scaler.fit_transform(values)
File "D:\anaconda\lib\site-packages\sklearn\base.py", line 464, in fit_transform
return self.fit(X, **fit_params).transform(X)
File "D:\anaconda\lib\site-packages\sklearn\preprocessing\data.py", line 334, in fit
return self.partial_fit(X, y)
File "D:\anaconda\lib\site-packages\sklearn\preprocessing\data.py", line 362, in partial_fit
force_all_finite="allow-nan")
File "D:\anaconda\lib\site-packages\sklearn\utils\validation.py", line 527, in check_array
array = np.asarray(array, dtype=dtype, order=order)
File "D:\anaconda\lib\site-packages\numpy\core\numeric.py", line 538, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: 'label'
label是csv文件里的列名，但是就算去掉，还是会报这个错误
