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Diffirent results in a function approximation problem using MLPRegressor and Keras



2019 Community Moderator ElectionMulti-class text classification with LSTM in KerasBinary text classification problem with small label-dataset using kerasKeras : problem in fitting modelHow to obtain with a recurrent neural network the Xor function using keras?Fraud detection using auto-encoders and KerasKeras Loss Function for Multidimensional Regression ProblemUsing Keras to Predict a Function Following a Normal DistributionUsing a custom R generator function with fit_generator (Keras, R)Keras Attention Guided CNN problemKeras inconsistent training results










0












$begingroup$


I have different results in a function approximation problem. I am trying to approximate a sine wave using MLPRegressor and Keras (um dense layer)
Here is the code for the MLPRegressor:



import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import mean_squared_error

#Cria um dataset
X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
noise= np.random.normal(0,0.1,100).reshape(-1,1)



y_train = np.sin(2*np.pi*X_train)
y_train=y_train + noise
y_train=y_train.ravel() # transfoprma em 1D array

#X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
#y_train = np.sin(2 * np.pi * X_train).ravel()


# Experimentos
#hidden_layer sizes : 1,3, 100
#max_iter=10,100,1000
#
nn = MLPRegressor(
hidden_layer_sizes=(3,), activation='tanh', solver='lbfgs', alpha=0.000, batch_size='auto',
learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=80, shuffle=True,
random_state=0, tol=0.0001, verbose=True, warm_start=False, momentum=0.0, nesterovs_momentum=False,
early_stopping=False, validation_fraction=0.0, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

#Treina a Rede
n = nn.fit(X_train, y_train)

#previsoes na rede no conjunto de treinamento
predict_train =nn.predict(X_train)

#Plota o treinamento
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


#Conjunto de Teste
X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
y_test=y_test.ravel()


#Calcula as previsoes no conjunto de teste

predict_test= nn.predict(X_test)

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

plt.legend()
plt.show()

print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


Using MLPRegressor, I found satisfactory results with just 3 neurons. However, when I try to use Keras, I can not get reasonably results. The code is very similar with the exception of the optmizer and the activation function. Here is the code for Keras:



import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import keras
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import mean_squared_error





#
#Cria um dataset

#Cria um dataset
X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
noise= np.random.normal(0,0.1,100).reshape(-1,1)



y_train = np.sin(2*np.pi*X_train)
y_train=y_train + noise
y_train=y_train.ravel() # transfoprma em 1D array


#Construir a Rede
nn = Sequential() # sequencia de camada
#activation
# sigmoid, tanh, relu, linear
# units: numero de neuronios na camada
#primeira camada escondida tem input_dim
nn.add(Dense(units = 100, activation = 'relu',
kernel_initializer = 'random_uniform', input_dim = 1))
nn.add(Dense(units = 1, activation = 'linear'))

# Algorritmo de aprendizado
#sgd = keras.optimizers.SGD(lr=0.1, decay=0, momentum=0, nesterov=False)
adam=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

#determina a funcao de custo e a metrica utilizada
nn.compile(loss = 'mean_squared_error', optimizer = adam,
metrics = ['mean_squared_error'])
history= nn.fit(X_train, y_train, batch_size = 1, epochs = 1000)

#previsoes na rede no conjunto de treinamento
predict_train =nn.predict(X_train)



#Plota o treinamento
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


#Conjunto de Teste
X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
y_test=y_test.ravel()


#Calcula as previsoes no conjunto de teste

predict_test= nn.predict(X_test)

fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

plt.legend()
plt.show()

print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


I already tried sgd as optimizer and also tanh for activation function. I do not undestand what I am missing, that is why I cann make the code for function approximation using Keras work.










share|improve this question







New contributor




Jorge Amaral is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.







$endgroup$
















    0












    $begingroup$


    I have different results in a function approximation problem. I am trying to approximate a sine wave using MLPRegressor and Keras (um dense layer)
    Here is the code for the MLPRegressor:



    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.neural_network import MLPRegressor
    from sklearn.metrics import mean_squared_error

    #Cria um dataset
    X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
    noise= np.random.normal(0,0.1,100).reshape(-1,1)



    y_train = np.sin(2*np.pi*X_train)
    y_train=y_train + noise
    y_train=y_train.ravel() # transfoprma em 1D array

    #X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
    #y_train = np.sin(2 * np.pi * X_train).ravel()


    # Experimentos
    #hidden_layer sizes : 1,3, 100
    #max_iter=10,100,1000
    #
    nn = MLPRegressor(
    hidden_layer_sizes=(3,), activation='tanh', solver='lbfgs', alpha=0.000, batch_size='auto',
    learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=80, shuffle=True,
    random_state=0, tol=0.0001, verbose=True, warm_start=False, momentum=0.0, nesterovs_momentum=False,
    early_stopping=False, validation_fraction=0.0, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

    #Treina a Rede
    n = nn.fit(X_train, y_train)

    #previsoes na rede no conjunto de treinamento
    predict_train =nn.predict(X_train)

    #Plota o treinamento
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
    ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


    #Conjunto de Teste
    X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
    y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
    y_test=y_test.ravel()


    #Calcula as previsoes no conjunto de teste

    predict_test= nn.predict(X_test)

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
    ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

    plt.legend()
    plt.show()

    print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
    print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


    Using MLPRegressor, I found satisfactory results with just 3 neurons. However, when I try to use Keras, I can not get reasonably results. The code is very similar with the exception of the optmizer and the activation function. Here is the code for Keras:



    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt

    import keras
    from keras.models import Sequential
    from keras.layers import Dense
    from sklearn.metrics import mean_squared_error





    #
    #Cria um dataset

    #Cria um dataset
    X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
    noise= np.random.normal(0,0.1,100).reshape(-1,1)



    y_train = np.sin(2*np.pi*X_train)
    y_train=y_train + noise
    y_train=y_train.ravel() # transfoprma em 1D array


    #Construir a Rede
    nn = Sequential() # sequencia de camada
    #activation
    # sigmoid, tanh, relu, linear
    # units: numero de neuronios na camada
    #primeira camada escondida tem input_dim
    nn.add(Dense(units = 100, activation = 'relu',
    kernel_initializer = 'random_uniform', input_dim = 1))
    nn.add(Dense(units = 1, activation = 'linear'))

    # Algorritmo de aprendizado
    #sgd = keras.optimizers.SGD(lr=0.1, decay=0, momentum=0, nesterov=False)
    adam=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

    #determina a funcao de custo e a metrica utilizada
    nn.compile(loss = 'mean_squared_error', optimizer = adam,
    metrics = ['mean_squared_error'])
    history= nn.fit(X_train, y_train, batch_size = 1, epochs = 1000)

    #previsoes na rede no conjunto de treinamento
    predict_train =nn.predict(X_train)



    #Plota o treinamento
    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
    ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


    #Conjunto de Teste
    X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
    y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
    y_test=y_test.ravel()


    #Calcula as previsoes no conjunto de teste

    predict_test= nn.predict(X_test)

    fig = plt.figure()
    ax1 = fig.add_subplot(111)
    ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
    ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

    plt.legend()
    plt.show()

    print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
    print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


    I already tried sgd as optimizer and also tanh for activation function. I do not undestand what I am missing, that is why I cann make the code for function approximation using Keras work.










    share|improve this question







    New contributor




    Jorge Amaral is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      0












      0








      0





      $begingroup$


      I have different results in a function approximation problem. I am trying to approximate a sine wave using MLPRegressor and Keras (um dense layer)
      Here is the code for the MLPRegressor:



      import numpy as np
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn.neural_network import MLPRegressor
      from sklearn.metrics import mean_squared_error

      #Cria um dataset
      X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      noise= np.random.normal(0,0.1,100).reshape(-1,1)



      y_train = np.sin(2*np.pi*X_train)
      y_train=y_train + noise
      y_train=y_train.ravel() # transfoprma em 1D array

      #X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      #y_train = np.sin(2 * np.pi * X_train).ravel()


      # Experimentos
      #hidden_layer sizes : 1,3, 100
      #max_iter=10,100,1000
      #
      nn = MLPRegressor(
      hidden_layer_sizes=(3,), activation='tanh', solver='lbfgs', alpha=0.000, batch_size='auto',
      learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=80, shuffle=True,
      random_state=0, tol=0.0001, verbose=True, warm_start=False, momentum=0.0, nesterovs_momentum=False,
      early_stopping=False, validation_fraction=0.0, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

      #Treina a Rede
      n = nn.fit(X_train, y_train)

      #previsoes na rede no conjunto de treinamento
      predict_train =nn.predict(X_train)

      #Plota o treinamento
      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
      ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


      #Conjunto de Teste
      X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
      y_test=y_test.ravel()


      #Calcula as previsoes no conjunto de teste

      predict_test= nn.predict(X_test)

      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
      ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

      plt.legend()
      plt.show()

      print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
      print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


      Using MLPRegressor, I found satisfactory results with just 3 neurons. However, when I try to use Keras, I can not get reasonably results. The code is very similar with the exception of the optmizer and the activation function. Here is the code for Keras:



      import numpy as np
      import pandas as pd
      import matplotlib.pyplot as plt

      import keras
      from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.metrics import mean_squared_error





      #
      #Cria um dataset

      #Cria um dataset
      X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      noise= np.random.normal(0,0.1,100).reshape(-1,1)



      y_train = np.sin(2*np.pi*X_train)
      y_train=y_train + noise
      y_train=y_train.ravel() # transfoprma em 1D array


      #Construir a Rede
      nn = Sequential() # sequencia de camada
      #activation
      # sigmoid, tanh, relu, linear
      # units: numero de neuronios na camada
      #primeira camada escondida tem input_dim
      nn.add(Dense(units = 100, activation = 'relu',
      kernel_initializer = 'random_uniform', input_dim = 1))
      nn.add(Dense(units = 1, activation = 'linear'))

      # Algorritmo de aprendizado
      #sgd = keras.optimizers.SGD(lr=0.1, decay=0, momentum=0, nesterov=False)
      adam=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

      #determina a funcao de custo e a metrica utilizada
      nn.compile(loss = 'mean_squared_error', optimizer = adam,
      metrics = ['mean_squared_error'])
      history= nn.fit(X_train, y_train, batch_size = 1, epochs = 1000)

      #previsoes na rede no conjunto de treinamento
      predict_train =nn.predict(X_train)



      #Plota o treinamento
      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
      ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


      #Conjunto de Teste
      X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
      y_test=y_test.ravel()


      #Calcula as previsoes no conjunto de teste

      predict_test= nn.predict(X_test)

      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
      ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

      plt.legend()
      plt.show()

      print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
      print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


      I already tried sgd as optimizer and also tanh for activation function. I do not undestand what I am missing, that is why I cann make the code for function approximation using Keras work.










      share|improve this question







      New contributor




      Jorge Amaral is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I have different results in a function approximation problem. I am trying to approximate a sine wave using MLPRegressor and Keras (um dense layer)
      Here is the code for the MLPRegressor:



      import numpy as np
      import pandas as pd
      import matplotlib.pyplot as plt
      from sklearn.neural_network import MLPRegressor
      from sklearn.metrics import mean_squared_error

      #Cria um dataset
      X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      noise= np.random.normal(0,0.1,100).reshape(-1,1)



      y_train = np.sin(2*np.pi*X_train)
      y_train=y_train + noise
      y_train=y_train.ravel() # transfoprma em 1D array

      #X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      #y_train = np.sin(2 * np.pi * X_train).ravel()


      # Experimentos
      #hidden_layer sizes : 1,3, 100
      #max_iter=10,100,1000
      #
      nn = MLPRegressor(
      hidden_layer_sizes=(3,), activation='tanh', solver='lbfgs', alpha=0.000, batch_size='auto',
      learning_rate='constant', learning_rate_init=0.01, power_t=0.5, max_iter=80, shuffle=True,
      random_state=0, tol=0.0001, verbose=True, warm_start=False, momentum=0.0, nesterovs_momentum=False,
      early_stopping=False, validation_fraction=0.0, beta_1=0.9, beta_2=0.999, epsilon=1e-08)

      #Treina a Rede
      n = nn.fit(X_train, y_train)

      #previsoes na rede no conjunto de treinamento
      predict_train =nn.predict(X_train)

      #Plota o treinamento
      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
      ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


      #Conjunto de Teste
      X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
      y_test=y_test.ravel()


      #Calcula as previsoes no conjunto de teste

      predict_test= nn.predict(X_test)

      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
      ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

      plt.legend()
      plt.show()

      print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
      print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


      Using MLPRegressor, I found satisfactory results with just 3 neurons. However, when I try to use Keras, I can not get reasonably results. The code is very similar with the exception of the optmizer and the activation function. Here is the code for Keras:



      import numpy as np
      import pandas as pd
      import matplotlib.pyplot as plt

      import keras
      from keras.models import Sequential
      from keras.layers import Dense
      from sklearn.metrics import mean_squared_error





      #
      #Cria um dataset

      #Cria um dataset
      X_train = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      noise= np.random.normal(0,0.1,100).reshape(-1,1)



      y_train = np.sin(2*np.pi*X_train)
      y_train=y_train + noise
      y_train=y_train.ravel() # transfoprma em 1D array


      #Construir a Rede
      nn = Sequential() # sequencia de camada
      #activation
      # sigmoid, tanh, relu, linear
      # units: numero de neuronios na camada
      #primeira camada escondida tem input_dim
      nn.add(Dense(units = 100, activation = 'relu',
      kernel_initializer = 'random_uniform', input_dim = 1))
      nn.add(Dense(units = 1, activation = 'linear'))

      # Algorritmo de aprendizado
      #sgd = keras.optimizers.SGD(lr=0.1, decay=0, momentum=0, nesterov=False)
      adam=keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)

      #determina a funcao de custo e a metrica utilizada
      nn.compile(loss = 'mean_squared_error', optimizer = adam,
      metrics = ['mean_squared_error'])
      history= nn.fit(X_train, y_train, batch_size = 1, epochs = 1000)

      #previsoes na rede no conjunto de treinamento
      predict_train =nn.predict(X_train)



      #Plota o treinamento
      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_train, y_train, s=5, c='b', marker="o", label='real')
      ax1.plot(X_train,predict_train, c='r', label='NN Prediction')


      #Conjunto de Teste
      X_test = np.arange(0.0, 1, 0.01).reshape(-1, 1)
      y_test = np.sin(2*np.pi*X_test) + np.random.normal(0,0.2,100).reshape(-1,1)
      y_test=y_test.ravel()


      #Calcula as previsoes no conjunto de teste

      predict_test= nn.predict(X_test)

      fig = plt.figure()
      ax1 = fig.add_subplot(111)
      ax1.scatter(X_test, y_test, s=5, c='b', marker="o", label='real')
      ax1.plot(X_test,predict_test, c='r', label='NN Prediction')

      plt.legend()
      plt.show()

      print('MSE training : :.3f'.format(mean_squared_error(y_train, predict_train)))
      print('MSE testing : :.3f'.format(mean_squared_error(y_test, predict_test)))


      I already tried sgd as optimizer and also tanh for activation function. I do not undestand what I am missing, that is why I cann make the code for function approximation using Keras work.







      keras mlp






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