Vfrdtyky

I'm currently working on a simple prediction system, where the user is asked a series of yes/no questions and based on their responses, a pre-trained model (MLPClassifier) predicted a class and asks the user whether or not the prediction was right. I'm not sure if this is possible, but I was hoping to then alter the weights of the pre-trained model (in sort of an online-learning fashion) so that the network (in that session) doesn't predict the same class later. Currently, I'm just adding the bad responses to a dictionary and if the network predicts the class already in the black-listed set of classes it is ignored however I feel there must be a better approach than this! My code for the classifier is:

mlp = MLPClassifier(hidden_layer_sizes=(128,), max_iter=500, alpha=1e-4,

                    solver='sgd', verbose=10, tol=1e-4, random_state=1,

                    learning_rate_init=.1, )

x_train, x_test, y_train, y_test = train_test_split(df.values[:, 0:8], df.label_idx, test_size=0.33,

                                                    random_state=42)

And the code for the predictions is:

def receive_input():

responses = 

bad_guesses = 

print("Answer questions (Yes/No) or enter END to make prediction")

count = 0

while count < len(questions):

    print(questions[count])

    response = input().lower().strip()

    if response == 'end':

        break

    elif response == 'yes':

        responses.append(1)

    elif response == 'no':

        responses.append(0)

    else:

        print('Invalid Input')

        continue

    count += 1



    padded_responses = np.pad(np.array(responses), (0, 8 - len(responses)), 'constant', constant_values=(0, -1))

    prob_pred = mlp.predict_proba(padded_responses.reshape(1, -1)).flatten()

    index = np.argmax(prob_pred)

    best_score = prob_pred[index]

    guess = labels[index]

    if best_score > 0.8 and guess not in bad_guesses:

        print('Early guess is: ' + labels[index] + ' is this right ? (Yes/No)')

        correct = input()

        if correct == 'Yes':

            break

        elif correct == 'No':

            bad_guesses.append(labels[index])



pred = mlp.predict(np.array(responses).reshape(1, -1))

print('Prediction is: ' + labels[pred[0]])

mlp.coefs_ gives you a list, in which the ith element represents the weight matrix corresponding to layer i.

Moreover, mlp.intercepts_ gives you a list, in which the ith element represents the bias vector corresponding to layer i + 1.

So you can try and see if these attributes are alterable.