if TRAIN: for fold in range(1, folds): train, test = dl.get_k_split(fold) tr, val = train_test_split(train, test_size=.15, random_state=43) gen, test_gen = create_gens(tr, val) model = UNet3D_Extra(input_shape=(1, 128, 128, 128), n_labels=1) model.compile(optimizer=keras.optimizers.adam(.001), loss=loss_func) callbacks = get_callbacks(model_file=main_dr + 'saved_models/Leak-' + str(fold) + '.h5', initial_learning_rate=5e-3, learning_rate_drop=.5, learning_rate_epochs=None, learning_rate_patience=10, verbosity=1, early_stopping_patience=30) model.fit_generator(generator=gen, validation_data=test_gen, use_multiprocessing=True, workers=8, epochs=epochs, callbacks=callbacks) #model.save_weights(main_dr + 'saved_models/Leak-' + str(fold) + '.h5') if EVAL:
model = CNN_3D_AE(input_dims, 200) model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.adam(initial_lr)) if load: model.load_weights(model_path) print('loaded weights') model.summary() gen, test_gen = create_gens(train, test) callbacks = get_callbacks(model_file=model_path, initial_learning_rate=initial_lr, learning_rate_drop=.5, learning_rate_epochs=None, learning_rate_patience=100, verbosity=1, early_stopping_patience=100) model.fit_generator(generator=gen, validation_data=test_gen, use_multiprocessing=True, workers=8, epochs=epochs, callbacks=callbacks) preds = model.predict_generator(test_gen) for i in range(5): ex = preds[i]
n_classes=1, shuffle=True, augment=True, label_size=5) test_gen = RN_Generator(data_points=val, dim=config['RN_input_size'], batch_size=bs, n_classes=1, shuffle=False, augment=False, label_size=5) callbacks = get_callbacks(model_file=main_dr + 'saved_models/RN.h5', initial_learning_rate=initial_lr, learning_rate_drop=.5, learning_rate_epochs=None, learning_rate_patience=10, verbosity=1, early_stopping_patience=50) #model = load_RN_model('/home/sage/GenDiagFramework/saved_models/RN.h5') model = RetinaNet_Train() model.fit_generator(generator=train_gen, validation_data=test_gen, use_multiprocessing=True, workers=8, epochs=25, callbacks=callbacks)