m = X.shape[0] random_indices = np.random.randint(m, size=100) #display_data(X[random_indices]) print('Display selected inputs') #input() print('Tests nn_cost_function with default data') test_theta1 = weights['Theta1'] test_theta2 = weights['Theta2'] nn_params = np.concatenate((test_theta1.T.ravel(), test_theta2.T.ravel())) _lambda = 0 j = compute_cost(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y_test, _lambda) print('\nCost without reg', j) print('(this value should be about 0.287629) \n') _lambda = 1 j = compute_cost(nn_params, input_layer_size, hidden_layer_size, num_labels, X, y_test, _lambda) print('\nReal cost', j) print('(this value should be about 0.383770) \n') print('\nEvaluating sigmoid gradient...\n') g = sigmoid_gradient(np.array([1, -0.5, 0, 0.5, 1])) print('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n ') print(g) print('\n\n')
def cost_function(p): return compute_cost(p, input_layer_size, hidden_layer_size, num_labels, x, y, _lambda, yk, x_bias)