Adding validation data, loading method and train.py source file with instructions

    train_data = []

    train_labels = []

    val_data_size = 1

    val_indices = []

    val_data = []

    val_labels = []

    test_data_size = 1

    test_indices = []

    test_data = []

    test_points_y = np.linspace(1,-1,51)

    def __init__(self, data_size, training_data_size):

    def __init__(self, data_size, training_data_size, validation_data_size, test_data_size):

        self.data_size = data_size

        self.train_data_size = training_data_size

        self.test_data_size = data_size - training_data_size

        self.val_data_size = validation_data_size

        if test_data_size != data_size - training_data_size - validation_data_size:

            print("Your dataset is not split into training/val/test data properly. The sum must be the total number of datapoints!")

            sys.exit(1)

        self.test_data_size = data_size - training_data_size - validation_data_size

    def initializeDataSet(self, type):

        self.createAllData(type)

        self.train_data = self.all_data[self.train_indices,:]

        self.train_labels = self.labels[self.train_indices]

        self.test_indices = np.delete(range(self.data_size), self.train_indices, axis=0)

        self.val_indices = random.sample(list(np.delete(range(self.data_size), self.train_indices, axis=0)), self.val_data_size)

        self.val_data = self.all_data[self.val_indices,:]

        self.val_labels = self.labels[self.val_indices]

        self.test_indices = np.delete(range(self.data_size), self.train_indices+self.val_indices, axis=0)

        self.test_data = self.all_data[self.test_indices,:]

        self.test_labels = self.labels[self.test_indices]

                self.all_data[i][0] = 0.5*self.all_data[i][0]

                self.all_data[i][1] = 0.5*self.all_data[i][1]

        self.labels = 2*(self.norm((self.all_data[:,0]/1), (self.all_data[:,1])/1) < 0.7)-1

        #self.labels = (self.norm((self.all_data[:,0]/1), (self.all_data[:,1])/1) < 0.7)

    def createSinus(self):

        self.all_data = 2*np.random.rand(self.data_size,self.input_coord_dimension)-np.ones((self.data_size,self.input_coord_dimension))