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