import ANN
import numpy as np
import math
import matplotlib.pyplot as plt
if __name__ == "__main__":
dataX = np.linspace(0, 2 * 3.14, 1000).reshape(1000, 1)
dataY = np.sin(dataX)
nNL = [1, 10, 10, 1] # Number of neurons per every hidden layer.
actFunc = ANN.ReLU()
actFuncFinal = ANN.Act_dummy()
fcann = ANN.FCANN(nNL, actFunc, actFuncFinal)
fcann.make_random_w_b(0.1, -0.05, 0.0001)
lossFunc = ANN.SumOfSquares()
fcann.train(dataX, dataY, 200, 0.02,\
randomizeData = True, showFigure = True,\
lossFunc = lossFunc)
pathName = "/home/yaoyu/SourceCodes/NN/SimpleANN/SavedANN"
fcann.save_to_file(pathName)
# Test.
# imports
import importlib
import ANN
import numpy as np
import math
import matplotlib.pyplot as plt
if __name__ == "__main__":
fcann = ANN.FCANN()
pathName = "/home/yaoyu/SourceCodes/NN/SimpleANN/SavedANN/fcann"
try:
fcann.load_from_file(pathName)
except ANN.ANNEx as e:
e.show_message()
raise e
# Test.
print("========== Test. ==============")
dataX = np.linspace(0, 2 * 3.14, 2000)
dataY = np.sin(dataX)
yList = []
for i in range(dataX.shape[0]):
x = np.array(dataX[i]).reshape(fcann.layerDesc[0], 1)
