Robust Sine Function Fitting
import random
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from numpy import asarray
from matplotlib import pyplot
import math
import numpy
define the dataset
x =numpy.arange(-3.14,3.14, 0.005)
y = asarray([math.sin(i)+ random.uniform(-0.3, 0.3) for i in x])
print(x.min(), x.max(), y.min(), y.max())
reshape arrays into into rows and cols
x = x.reshape((len(x), 1))
y = y.reshape((len(y), 1))
separately scale the input and output variables
scale_x = MinMaxScaler()
x = scale_x.fit_transform(x)
scale_y = MinMaxScaler()
y = scale_y.fit_transform(y)
print(x.min(), x.max(), y.min(), y.max())
design the neural network model
model = Sequential()
model.add(Dense(10, input_dim=1, activation='relu', kernel_initializer='he_uniform'))
model.add(Dense(10, activation='sigmoid', kernel_initializer='he_uniform'))
model.add(Dense(1))
define the loss function and optimization algorithm
model.compile(loss='mse', optimizer='adam')
ft the model on the training dataset
model.fit(x, y, epochs=500, batch_size=10, verbose=0)
make predictions for the input data
yhat = model.predict(x)
inverse transforms
x_plot = scale_x.inverse_transform(x)
y_plot = scale_y.inverse_transform(y)
yhat_plot = scale_y.inverse_transform(yhat)
report model error
print('MSE: %.3f' % mean_squared_error(y_plot, yhat_plot))
plot x vs y
pyplot.scatter(x_plot,y_plot, label='Actual')
plot x vs yhat
pyplot.scatter(x_plot,yhat_plot, label='Predicted')
pyplot.title('Input (x) versus Output (y) Sin Function')
pyplot.xlabel('Input Variable (x)')
pyplot.ylabel('Output Variable (y)')
pyplot.legend()
pyplot.show()
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