def activation(self, new_activation):
if new_activation == 'leaky_relu':
LR = LeakyReLU(alpha=0.001)
LR.__name__ = 'relu'
self._activation = LR
else:
self._activation = new_activation
def __init__(self, X, Y, *dictionary):
self.X = X
self.Y = Y
self._activation = 'relu'
self._batch_size = 64
self._n_epochs = 1000
self._getNeurons = [1, 1]
self._dropout = 0
self._patience = 10
self._batchNormalization = False
self._alpha = 0.0001
self.save_txt = True
if dictionary:
settings = dictionary[0]
try:
self._center = settings["center"]
except:
raise Exception(
"centering decision not given to the dictionary!")
exit()
try:
self._centering = settings["centering_method"]
except:
raise Exception(
"centering criterion not given to the dictionary!")
exit()
try:
self._scale = settings["scale"]
except:
raise Exception(
"scaling decision not given to the dictionary!")
exit()
try:
self._scaling = settings["scaling_method"]
except:
raise Exception(
"scaling criterion not given to the dictionary!")
exit()
try:
self._activation = settings["activation_function"]
except:
raise Exception(
"activation function not given to the dictionary!")
exit()
try:
self._batch_size = settings["batch_size"]
except:
raise Exception("batch size not given to the dictionary!")
exit()
try:
self._n_epochs = settings["number_of_epochs"]
except:
raise Exception(
"number of epochs not given to the dictionary!")
exit()
try:
self._getNeurons = settings["neurons_per_layer"]
except:
raise Exception(
"number of neurons not given to the dictionary!")
exit()
try:
self._dropout = settings["dropout"]
except:
raise Exception("dropout not given to the dictionary!")
exit()
try:
self._patience = settings["patience"]
except:
raise Exception(
"patience for early stopping not given to the dictionary!")
exit()
try:
self._batchNormalization = settings["batchNormalization"]
except:
raise Exception(
"batch normalization not given to the dictionary!")
exit()
try:
self._alpha = settings["alpha_LR"]
except:
raise Exception(
"alpha for leaky relu not given to the dictionary!")
exit()
if settings["activation_function"] == 'leaky_relu':
LR = LeakyReLU(alpha=self._alpha)
LR.__name__ = 'relu'
self._activation = LR
def __init__(self, X, Y, *dictionary):
self.X = X
self.Y = Y
super().__init__(self.X, self.Y, *dictionary)
if dictionary:
settings = dictionary[0]
try:
self._center = settings["center"]
except:
raise Exception(
"centering decision not given to the dictionary!")
exit()
try:
self._centering = settings["centering_method"]
except:
raise Exception(
"centering criterion not given to the dictionary!")
exit()
try:
self._scale = settings["scale"]
except:
raise Exception(
"scaling decision not given to the dictionary!")
exit()
try:
self._scaling = settings["scaling_method"]
except:
raise Exception(
"scaling criterion not given to the dictionary!")
exit()
try:
self._activation = settings["activation_function"]
except:
raise Exception(
"activation function not given to the dictionary!")
exit()
try:
self._batch_size = settings["batch_size"]
except:
raise Exception("batch size not given to the dictionary!")
exit()
try:
self._n_epochs = settings["number_of_epochs"]
except:
raise Exception(
"number of epochs not given to the dictionary!")
exit()
try:
self._getNeurons = settings["neurons_per_layer"]
except:
raise Exception(
"number of neurons not given to the dictionary!")
exit()
try:
self._dropout = settings["dropout"]
except:
raise Exception("dropout not given to the dictionary!")
exit()
try:
self._patience = settings["patience"]
except:
raise Exception(
"patience for early stopping not given to the dictionary!")
exit()
try:
self._alpha = settings["alpha_LR"]
except:
raise Exception(
"alpha for leaky relu not given to the dictionary!")
exit()
if settings["activation_function"] == 'leaky_relu':
LR = LeakyReLU(alpha=self._alpha)
LR.__name__ = 'relu'
self._activation = LR
try:
self.Xtest = dictionary[1]
except:
print("Test not given!!")
def __init__(self, X, Y, *dictionary):
self.X = X
self.Y = Y
self._activation_output = 'linear'
self._loss_function = 'mean_squared_error'
self._monitor_early_stop = 'mean_squared_error'
self._learningRate = 0.0001
self.testProcess = False
super().__init__(self.X, self.Y, *dictionary)
if dictionary:
settings = dictionary[0]
try:
self._center = settings["center"]
except:
raise Exception(
"centering decision not given to the dictionary!")
exit()
try:
self._centering = settings["centering_method"]
except:
raise Exception(
"centering criterion not given to the dictionary!")
exit()
try:
self._scale = settings["scale"]
except:
raise Exception(
"scaling decision not given to the dictionary!")
exit()
try:
self._scaling = settings["scaling_method"]
except:
raise Exception(
"scaling criterion not given to the dictionary!")
exit()
try:
self._activation = settings["activation_function"]
except:
raise Exception(
"activation function not given to the dictionary!")
exit()
try:
self._batch_size = settings["batch_size"]
except:
raise Exception("batch size not given to the dictionary!")
exit()
try:
self._n_epochs = settings["number_of_epochs"]
except:
raise Exception(
"number of epochs not given to the dictionary!")
exit()
try:
self._getNeurons = settings["neurons_per_layer"]
except:
raise Exception(
"number of neurons per layer not given to the dictionary!")
exit()
try:
self._dropout = settings["dropout"]
except:
raise Exception("dropout not given to the dictionary!")
exit()
try:
self._patience = settings["patience"]
except:
raise Exception(
"patience for early stopping not given to the dictionary!")
exit()
try:
self._alpha = settings["alpha_LR"]
except:
raise Exception(
"centering decision not given to the dictionary!")
exit()
try:
self._activation_output = settings["activation_output"]
except:
raise Exception(
"activation output layer not given to the dictionary!")
exit()
try:
self._loss_function = settings["loss_function"]
except:
raise Exception("loss function not given to the dictionary!")
exit()
try:
self._monitor_early_stop = settings["monitor"]
except:
raise Exception(
"monitor for early stopping not given to the dictionary!")
exit()
try:
self._learningRate = settings["learning_rate"]
except:
raise Exception(
"initial learning rate not given to the dictionary!")
exit()
try:
self._batchNormalization = settings["batchNormalization"]
except:
raise Exception(
"batch normalization not given to the dictionary!")
exit()
if settings["activation_function"] == 'leaky_relu':
LR = LeakyReLU(alpha=self._alpha)
LR.__name__ = 'relu'
self._activation = LR
if len(dictionary) > 1:
self.Z = dictionary[1]
self.testProcess = True
