N_CLASSES = np.unique(y_train)
scores_train = []
scores_test = []
# EPOCH
epoch = 0
while epoch < N_EPOCHS:
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 + N_BATCH]
clf.partial_fit(X_train[indices], y_train[indices], classes=N_CLASSES)
mini_batch_index += N_BATCH
if mini_batch_index >= N_TRAIN_SAMPLES:
break
# SCORE TRAIN
scores_train.append(clf.score(X_train, y_train))
# SCORE TEST
scores_test.append(clf.score(X_test, y_test))
epoch += 1
plt.figure()
plt.plot(scores_train, color='b', alpha=0.8, label='Train')
plt.plot(scores_test, color='r', alpha=0.8, label='Test')
