神经网络正弦近似 [英] Neural network sine approximation

问题描述

在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从 keras.layers 导入密集导入 matplotlib.pyplot 作为 plt随机导入导入 numpy从 sklearn.preprocessing 导入 MinMaxScaler回归器 = 顺序()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(密集(单位=1))regressor.compile(loss='mean_squared_error', 优化器='sgd')#regressor = ExtraTreesRegressor()N = 5000X = numpy.empty((N,))Y = numpy.empty((N,))对于范围(N)中的我:X[i] = random.uniform(-10, 10)X = numpy.sort(X).reshape(-1, 1)对于范围(N)中的我:Y[i] = numpy.sin(X[i])Y = Y.reshape(-1, 1)X_scaler = MinMaxScaler()Y_scaler = MinMaxScaler()X = X_scaler.fit_transform(X)Y = Y_scaler.fit_transform(Y)regressor.fit(X, Y, epochs=2,verbose=1,batch_size=32)#regressor.fit(X, Y.reshape(5000,))x = numpy.mgrid[-10:10:100*1j]x = x.reshape(-1, 1)y = numpy.mgrid[-10:10:100*1j]y = y.reshape(-1, 1)x = X_scaler.fit_transform(x)对于范围内的 i(len(x)):y[i] = regressor.predict(numpy.array([x[i]]))plt.figure()plt.plot(X_scaler.inverse_transform(x), Y_scaler.inverse_transform(y))plt.plot(X_scaler.inverse_transform(X), Y_scaler.inverse_transform(Y))

问题是我所有的预测值都在 0 左右.如您所见，我使用了 sklearn(注释行)中的 ExtraTreesRegressor 来检查协议是否确实正确.那么我的神经网络有什么问题呢?为什么它不起作用?

(我试图解决的实际问题是使用神经网络计算山地车问题的 Q 函数.它与这个函数逼近器有什么不同?)

解决方案

有了这些变化:

• 激活relu
• 删除kernel_initializer(即保留

  修补匠，一次又一次......

  After spending days failing to use neural network for Q learning, I decided to go back to the basics and do a simple function approximation to see if everything was working correctly and see how some parameters affected the learning process. Here is the code that I came up with

  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()
  X = X_scaler.fit_transform(X)
  Y = Y_scaler.fit_transform(Y)
  
  regressor.fit(X, Y, epochs=2, verbose=1, batch_size=32)
  #regressor.fit(X, Y.reshape(5000,))
  
  x = numpy.mgrid[-10:10:100*1j]
  x = x.reshape(-1, 1)
  y = numpy.mgrid[-10:10:100*1j]
  y = y.reshape(-1, 1)
  x = X_scaler.fit_transform(x)
  
  for i in range(len(x)):
      y[i] = regressor.predict(numpy.array([x[i]]))
  
  plt.figure()
  plt.plot(X_scaler.inverse_transform(x), Y_scaler.inverse_transform(y))
  plt.plot(X_scaler.inverse_transform(X), Y_scaler.inverse_transform(Y))
  

  The problem is that all my predictions are around 0 in value. As you can see I used an ExtraTreesRegressor from sklearn (commented lines) to check that the protocol is actually correct. So what is wrong with my neural network ? Why is it not working ?

  (The actual problem that I'm trying to solve is to compute the Q function for the mountain car problem using neural network. How is it different from this function approximator ?)

  解决方案

  With these changes:

  • Activations to relu
  • Remove kernel_initializer (i.e. leave the default 'glorot_uniform')
  • Adam optimizer
  • 100 epochs

  i.e.

  regressor = Sequential()
  regressor.add(Dense(units=20, activation='relu', input_dim=1)) 
  regressor.add(Dense(units=20, activation='relu')) 
  regressor.add(Dense(units=20, activation='relu')) 
  regressor.add(Dense(units=1))
  regressor.compile(loss='mean_squared_error', optimizer='adam')
  
  regressor.fit(X, Y, epochs=100, verbose=1, batch_size=32)
  

  and the rest of your code unchanged, here is the result:

  Tinker, again and again...

  这篇关于神经网络正弦近似的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！