# ==============================================================================
# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung, 
# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz 
#
# This file is part of OAI-MRI-TKR
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Affero General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
# ==============================================================================
#!/usr/bin/env python3

from keras.models import Sequential
from keras.optimizers import SGD, Adam
#from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D,GlobalMaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten
from resnet3d import Resnet3DBuilder
import tensorflow as tf
def generate_model(learning_rate = 2 * 10 **(-4)):
    #auc = tf.keras.metrics.AUC()
    #model = Resnet3DBuilder.build_resnet_50((384, 384, 160, 1), 1)
    model = Resnet3DBuilder.build_resnet_18((352, 352, 144, 1), 1)
    model.compile(loss='binary_crossentropy',
                      metrics = ['accuracy'],
                      optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9))
    return model