x_train, x_test, y_train, y_test = load_data(test_size=0.25)
n_epoch=50
model = MLPClassifier(alpha=0.01, batch_size=128, epsilon=1e-08, hidden_layer_sizes=(300,), learning_rate='adaptive',max_iter=500,early_stopping=True)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
accuracy = accuracy_score(y_true=y_test, y_pred=y_pred)
print(classification_report(y_pred, y_test))

As you can see code is able to find accuracy, classification report, and confusion matrix.
But don’t know in this situation how to plot.
Tried the code below using some online resource. But getting a small error here.
scores_train = []
scores_test = []


epoch = 0
while epoch < n_epoch:
    print('epoch: ', epoch)
    # SHUFFLING
    random_perm = np.random.permutation(x_train.shape[0])
    mini_batch_index = 0
    while True:
        # MINI-BATCH
        indices = random_perm[mini_batch_index:mini_batch_index + 128]
        model.partial_fit(x_train[indices], y_train[indices], classes=7)
        mini_batch_index += 128

        if mini_batch_index >= x_train.shape[0]:
            break

    # SCORE TRAIN
    scores_train.append(model.score(x_train, y_train))

    # SCORE TEST
    scores_test.append(model.score(x_test, y_test))

    epoch += 1


plt.plot(scores_train, color='green', alpha=0.8, label='Train')
plt.plot(scores_test, color='magenta', alpha=0.8, label='Test')
plt.title("Accuracy over epochs", fontsize=14)
plt.xlabel('Epochs')
plt.legend(loc='upper left')
plt.show()

The error is :TypeError: only integer scalar arrays can be converted to a scalar index at line
  model.partial_fit(x_train[indices], y_train[indices], classes=7)

I know it’s not directly related to Pyorch but I hope if someone can guide me.