Attempt 2 at fitting a neural network from the Iris dataset

Iris_NN_Fit_Predict2.py

# Initialize neural network object and fit object
nn_model2 = mlrose.NeuralNetwork(hidden_nodes = [2], activation = 'relu',
                                 algorithm = 'gradient_descent', max_iters = 1000,
                                 bias = True, is_classifier = True, learning_rate = 0.0001,
                                 early_stopping = True, clip_max = 5, max_attempts = 100,
                                 random_state = 3)

nn_model2.fit(X_train_scaled, y_train_hot)

# Predict labels for train set and assess accuracy
y_train_pred = nn_model2.predict(X_train_scaled)

y_train_accuracy = accuracy_score(y_train_hot, y_train_pred)

print('Training accuracy: ', y_train_accuracy)

# Predict labels for test set and assess accuracy
y_test_pred = nn_model2.predict(X_test_scaled)

y_test_accuracy = accuracy_score(y_test_hot, y_test_pred)

print('Test accuracy: ', y_test_accuracy)