import numpy as np from sklearn.datasets import load_iris from NeuralNet import NeuralNetwork from NeuralNet import calc_accuracy from sklearn.model_selection import train_test_split iris = load_iris() X = iris.data Y = iris.target # Transform Y into the required format. Y_iris = np.zeros((Y.shape[0], 3)) for i in range(Y_iris.shape[0]): Y_iris[i, Y[i]] = 1 # Split the data into training and testing data. X_train, X_test, y_train, y_test = train_test_split(X, Y_iris, random_state=76) nn = NeuralNetwork(X_train, y_train, X_train.shape[1], 0.01, 0.1, 1000, 32, y_train.shape[1]) nn.train_neural_network() print("Training accuracy: " + str(calc_accuracy(nn, X_train, y_train))) print("Testing accuracy: " + str(calc_accuracy(nn, X_test, y_test))) nn.save_theta()