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b/MI-DESS_IWTSE/ModelDCB.py |
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# ============================================================================== |
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# Copyright (C) 2023 Haresh Rengaraj Rajamohan, Tianyu Wang, Kevin Leung, |
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# Gregory Chang, Kyunghyun Cho, Richard Kijowski & Cem M. Deniz |
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# |
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# This file is part of OAI-MRI-TKR |
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# |
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# This program is free software: you can redistribute it and/or modify |
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# it under the terms of the GNU Affero General Public License as published |
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# by the Free Software Foundation, either version 3 of the License, or |
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# (at your option) any later version. |
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# This program is distributed in the hope that it will be useful, |
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# but WITHOUT ANY WARRANTY; without even the implied warranty of |
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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# GNU Affero General Public License for more details. |
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# You should have received a copy of the GNU Affero General Public License |
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# along with this program. If not, see <https://www.gnu.org/licenses/>. |
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# ============================================================================== |
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#!/usr/bin/env python3 |
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from keras.models import Sequential,Model |
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from keras.optimizers import SGD, Adam |
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from keras.layers import Input#, Dropout, Dense, Conv3D, MaxPooling3D, GlobalMaxPooling3D ,GlobalAveragePooling3D, Activation, BatchNormalization,Flatten |
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#from keras.models import Sequential |
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#from keras.layers import Dropout, Dense, Conv3D, MaxPooling3D,GlobalMaxPooling3D, GlobalAveragePooling3D, Activation, BatchNormalization,Flatten |
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from resnet3d import Resnet3DBuilder |
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#from keras.regularizers import l2 |
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def generate_model(learning_rate = 1 * 10 **(-4)): |
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print('**************************') |
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model = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32), 1) |
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model.compile(loss='binary_crossentropy', |
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metrics = ['accuracy'], |
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optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9)) |
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return model |
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#input1 = Input(shape=(384, 384, 36, 1)) |
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#input2 = Input(shape=(352, 352, 144, 1)) |
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#out = Resnet3DBuilder.build_resnet_18((48, 96, 18, 32),input1,input2,1) |
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#merged_model = Model([input1, input2], out) |
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merged_model.compile(loss='binary_crossentropy', |
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metrics = ['accuracy'], |
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optimizer = Adam(lr=learning_rate,beta_1=0.99, beta_2=0.999))#SGD(lr=1e-2, momentum = 0.9)) |
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#loss='categorical_crossentropy' |
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return merged_model |
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