def elm_predict(model,
X_train,
X_test,
X_valid,
multiTasks,
unweighted,
stl,
dictForLabelsTrain,
dictForLabelsTest,
dictForLabelsValid,
hidden_num=50,
main_task_id=-1,
elm_save_path='./model/elm.ckpt',
dataset='test'):
sess = tf.Session()
print('elm high level feature generating')
pred_train = model.predict([X_train])
feat_train = high_level_feature_mtl(pred_train,
stl=stl,
main_task_id=main_task_id)
print('high level feature dim for train: ', feat_train.shape[1])
#add total features
add_high_feature(feat_train, multiTasks, dictForLabelsTrain,
total_high_pred_train)
pred_test = model.predict([X_test])
feat_test = high_level_feature_mtl(pred_test,
stl=stl,
main_task_id=main_task_id)
#add total features
add_high_feature(feat_test, multiTasks, dictForLabelsTest,
total_high_pred_test)
print('high level feature dim for test: ', feat_test.shape[1])
if len(X_valid) != 0:
pred_valid = model.predict([X_valid])
feat_valid = high_level_feature_mtl(pred_valid,
stl=stl,
main_task_id=main_task_id)
scores = []
for task, classes, idx in multiTasks:
elm = ELM(sess,
feat_train.shape[0],
feat_train.shape[1],
hidden_num,
dictForLabelsTrain[task].shape[1],
task=str(task))
print('elm training')
elm.feed(feat_train, dictForLabelsTrain[task])
elm.save(elm_save_path + "." + str(task) + ".elm.ckpt")
print('elm testing')
labels = dictForLabelsTest[task]
if unweighted:
preds = elm.test(feat_test)
scores.append(unweighted_recall(preds, labels, task, dataset))
else:
acc = elm.test(feat_test, labels)
scores.append(acc)
if len(X_valid) != 0:
print('elm validating')
labels = dictForLabelsValid[task]
if unweighted:
preds = elm.test(feat_valid)
scores.append(unweighted_recall(preds, labels, task, dataset))
else:
acc = elm.test(feat_valid, labels)
scores.append(acc)
return scores
def main(args):
# ===============================
# Load dataset
# ===============================
n_classes = 10
(x_train, t_train), (x_test, t_test) = mnist.load_data()
# ===============================
# Preprocess
# ===============================
x_train = x_train.astype(np.float32) / 255.
x_train = x_train.reshape(-1, 28**2)
x_test = x_test.astype(np.float32) / 255.
x_test = x_test.reshape(-1, 28**2)
t_train = to_categorical(t_train, n_classes).astype(np.float32)
t_test = to_categorical(t_test, n_classes).astype(np.float32)
# ===============================
# Instantiate ELM
# ===============================
model = ELM(
n_input_nodes=28**2,
n_hidden_nodes=args.n_hidden_nodes,
n_output_nodes=n_classes,
loss=args.loss,
activation=args.activation,
name='elm',
)
# ===============================
# Training
# ===============================
model.fit(x_train, t_train)
train_loss, train_acc = model.evaluate(x_train,
t_train,
metrics=['loss', 'accuracy'])
print('train_loss: %f' % train_loss)
print('train_acc: %f' % train_acc)
# ===============================
# Validation
# ===============================
val_loss, val_acc = model.evaluate(x_test,
t_test,
metrics=['loss', 'accuracy'])
print('val_loss: %f' % val_loss)
print('val_acc: %f' % val_acc)
# ===============================
# Prediction
# ===============================
x = x_test[:10]
t = t_test[:10]
y = softmax(model.predict(x))
for i in range(len(y)):
print('---------- prediction %d ----------' % (i + 1))
class_pred = np.argmax(y[i])
prob_pred = y[i][class_pred]
class_true = np.argmax(t[i])
print('prediction:')
print('\tclass: %d, probability: %f' % (class_pred, prob_pred))
print('\tclass (true): %d' % class_true)
# ===============================
# Save model
# ===============================
print('saving model...')
model.save('model.h5')
del model
# ===============================
# Load model
# ===============================
print('loading model...')
model = load_model('model.h5')
