def trainNeuralNetwork(MyNeuralNetwork):
print(
'Input "target error(0.1)", "speed(0.01)" , "max training times (-1 for infinite)"'
)
rawinput = input('>>>')
rawinput = rawinput.split()
error = float(rawinput[0])
speed = float(rawinput[1])
times = int(rawinput[2])
Datas = IO.getDatas()
# Get Input/OutputData to matrix
if IO.getValuefromConfigfile('setting.json', 'Verbose') != None:
verbose = int(IO.getValuefromConfigfile('setting.json', 'Verbose'))
else:
verbose = VERBOSE_DEFAULT
if IO.getValuefromConfigfile('setting.json', 'Loop_per_N_times') != None:
loop = int(
IO.getValuefromConfigfile('setting.json', 'Loop_per_N_times'))
else:
loop = VERBOSE_PER_LOOP_DEFAULT
# Setting from config file
TrainingType.trainbyBatch(MyNeuralNetwork,
Datas,
error,
times,
speed,
VerbosePerLoop=loop,
Verbose=verbose)
MyNeuralNetwork.savetoFile()
print('Saved to "' + MyNeuralNetwork.Name + '.node".')
def printMatrix():
name = input(
'Input matrix\'s name to be printed. (Read from ".mtrx" file)\n>>>')
raw = IO.RAWReader()
raw.open(name + '.mtrx')
matrix = IO.getAMatrix(raw)
np.set_printoptions(threshold=np.nan)
print(matrix)
def listConfigfileValues():
print('Config list:')
print('[v] Verbose Level. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Verbose')))
print('[n] Verbose per "N" times. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Loop_per_N_times')))
print('[s] Default training speed. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Training_Speed')))
def setValuetoConfigfile():
print('Config list:')
print('[v] Verbose Level. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Verbose')))
print('[n] Verbose per "N" times. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Loop_per_N_times')))
print('[s] Default training speed. -> ' +
str(IO.getValuefromConfigfile('setting.json', 'Training_Speed')))
name = input('Name Code:\n')
value = input('Value:\n')
IO.setValuetoConfigfile('setting.json', ConfigDict[str(name)], value)
def savetoFile(self, Filename=''):
if Filename == '':
Filename = self.Name
MyRAWWriter = IO.RAWWriter()
MyRAWWriter.append(int(self.LayersCount))
for layer in range(0, self.LayersCount):
MyRAWWriter.append(int(self.LayerNeuronsCount[layer]))
# Save each layer's neurons count one by one
MyRAWWriter.newline()
for layer in range(0, self.LayersCount - 1):
IO.writeAMatrix(self.Weight[layer], MyRAWWriter)
# Save each layer's weight one by one
for layer in range(0, self.LayersCount - 1):
IO.writeAMatrix(self.Bias[layer], MyRAWWriter)
# Save each layer's bias one by one
MyRAWWriter.write(Filename + '.node')
def loadfromFile(self, Filename):
try:
MyRAWReader = IO.RAWReader()
MyRAWReader.open(Filename + '.node')
self.Name = Filename
self.LayersCount = int(MyRAWReader.pop())
self.LayerNeuronsCount = []
self.Weight = []
self.Bias = []
# Get the LayersCount first and Initlalize LayerNeuronsCount, Weight and Bias
for layer in range(0, self.LayersCount):
self.LayerNeuronsCount.append(int(MyRAWReader.pop()))
# Get each layer's neurons count one by one
for layer in range(0, self.LayersCount - 1):
self.Weight.append(IO.getAMatrix(MyRAWReader))
# Get each layer's weight one by one
for layer in range(0, self.LayersCount - 1):
self.Bias.append(IO.getAMatrix(MyRAWReader))
# Get each layer's bias one by one
except:
print('warning: Loading ' + Filename + '.node error!')
def idx2mtrx():
FilenameIn = input('Input input IDX file name\n')
FilenameOut = input('Input output mtrx filename.\n')
IO.idx2mtrx(FilenameIn, FilenameOut)