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import numpy as np
np.random.seed(1337)
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import f_t_t_read
# 生成数据
X = np.linspace(-1, 1, 200) #在返回(-1, 1)范围内的等差序列
np.random.shuffle(X) # 打乱顺序
Y = 0.5 * X + 2 + np.random.normal(0, 0.05, (200, )) #生成Y并添加噪声
#X, Y = f_t_t_read.data_get()
# plot
plt.scatter(X, Y)
plt.show()
X_train, Y_train = X[:160], Y[:160] #前160组数据为训练数据集
X_test, Y_test = X[160:], Y[160:] #后40组数据为测试数据集
#构建神经网络模型
model = Sequential()
#model.add(Dense(units=1,input_dim=1))
model.add(Dense(units = 1, input_shape = (1, )))
#`Dense` implements the operation:
#`output = activation(dot(input, kernel) + bias)`
##
##以下三种方法等价
##model = Sequential()
##方法(1)model.add(LSTM(32, input_shape=(10, 64)))
##方法(2)model.add(LSTM(32, batch_input_shape=(None, 10, 64)))
##方法(3)model.add(LSTM(32, input_length=10, input_dim=64))
##有些2D层,如Dense,支持通过指定输入维度input_dim来隐含的指定输入数据的shape,
##一些3D层,的时域(时空)层,支持通过参数input_dim和input_length来指定输入数据的shape
#input_dim, input_length = input_shape(input_length, input_dim)
#input_dim = input_shape(input_dim,)
##model.add(Dense(32, input_shape=(16,)))
## | # now the model will take as input arrays of shape (*, 16)
## | # and output arrays of shape (*, 32)
# 选定loss函数和优化器
model.compile(loss='mse', optimizer='sgd')
# 训练过程
print('Training -----------')
for step in range(501):
cost = model.train_on_batch(X_train, Y_train)
print('model.output_shape:',model.output_shape)
if step % 50 == 0:
print("After %d trainings, the cost: %f" % (step, cost))
# 测试过程
print('\nTesting ------------')
cost = model.evaluate(X_test, Y_test, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
print('Weights=', W, '\nbiases=', b)
# 将训练结果绘出
Y_pred = model.predict(X_test)
plt.scatter(X_test, Y_test)
plt.plot(X_test, Y_pred)
plt.show()
#---------------------------------以上参考,以下可用------------------------------------------
import numpy
import pandas
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
def data_get():
forecast_day = '024'
single_grid_id = 2439 #lon = 117.5, lat = 40, beijing
df = pandas.read_csv(forecast_day+'_'+str(single_grid_id)+'.csv',index_col = 0,encoding='gbk')
df[df>9000] = numpy.nan
df = df.dropna(how='any')
dataset = df.values
X = dataset[:, 0:-1]
Y = dataset[:, -1]
return(X,Y)
# define base mode
def baseline_model():
# create model
model = Sequential()
model.add(Dense(20, input_dim=X.shape[1], kernel_initializer='normal', activation='relu'))
model.add(Dense(20,activation ='relu'))
model.add(Dense(1, kernel_initializer='normal'))
# Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
return(model)
def function_rmse(v):
rmse = []
for i in v:
rmse.append(i*i)
rmse = numpy.sqrt(sum(rmse)/len(v))
return(rmse)
# load dataset
##dataframe = pandas.read_csv("housing.csv", delim_whitespace=True, header=None)
##dataset = dataframe.values
# split into input (X) and output (Y) variables
##X = dataset[:, 0:13]
##Y = dataset[:, 13]
X, Y = data_get()
# split into train and test groups
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.99, random_state=42)
model = baseline_model()
# 训练过程
print('Training -----------')
for step in range(501):
cost = model.train_on_batch(X_train, Y_train)
#print('model.output_shape:',model.output_shape)
if step % 50 == 0:
print("After %d trainings, the cost: %f" % (step, cost))
# 测试过程
print('\nTesting ------------')
cost = model.evaluate(X_test, Y_test, batch_size=40)
print('test cost:', cost)
W, b = model.layers[0].get_weights()
#print('Weights=', W, '\nbiases=', b)
# 将训练结果绘出
Y_pred = model.predict(X)
Y_pred = Y_pred.reshape(len(Y_pred))
#Y_ensemble = X.mean(1)
Y_ensemble = X[:,0]
Y1=Y_ensemble-Y
Y2=Y_pred-Y
##
plt.plot(Y1,'b')
plt.plot(Y2,'r')
##plt.plot(Y,'k')
##plt.plot(Y_ensemble,'b')
##plt.plot(Y_pred,'r')
###plt.xlim([100,200])
plt.show(block=False)
print(function_rmse(Y1))
print(function_rmse(Y2))
print(aaa)
plt.scatter(Y_test,Y_pred)
Y_ensemble = X.mean(1)
plt.scatter(Y_test,Y_ensemble,'r')
#plt.plot(Y_pred)
plt.show()
print(aaa)
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