相关疑难解决方法(0)

在花费数天未能使用神经网络进行 Q 学习之后，我决定回归基础并做一个简单的函数近似，看看一切是否正常工作，以及一些参数如何影响学习过程。这是我想出的代码

from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt
import random
import numpy
from sklearn.preprocessing import MinMaxScaler

regressor = Sequential()
regressor.add(Dense(units=20, activation='sigmoid', kernel_initializer='uniform', input_dim=1))
regressor.add(Dense(units=20, activation='sigmoid', kernel_initializer='uniform'))
regressor.add(Dense(units=20, activation='sigmoid', kernel_initializer='uniform'))
regressor.add(Dense(units=1))
regressor.compile(loss='mean_squared_error', optimizer='sgd')
#regressor = ExtraTreesRegressor()

N = 5000
X = numpy.empty((N,))
Y = numpy.empty((N,))

for i in range(N):
    X[i] = random.uniform(-10, 10)
X = numpy.sort(X).reshape(-1, 1)

for i in range(N):
    Y[i] = numpy.sin(X[i])
Y = Y.reshape(-1, 1)

X_scaler = MinMaxScaler()
Y_scaler = MinMaxScaler() …