return self._par def get_hms(self): return self._hms def get_mpai(self): return self._mpai def get_mpap(self): return self._mpap def maximize(self): return self._maximize if __name__ == '__main__': obj_fun = ObjectiveFunction() num_processes = cpu_count() # use number of logical CPUs num_iterations = num_processes * 5 # each process does 5 iterations results = harmony_search(obj_fun, num_processes, num_iterations) print('Elapsed time: {}\nBest harmony: {}\nBest fitness: {}'.format( results.elapsed_time, results.best_harmony, results.best_fitness)) # training & validation best_training(results.best_harmony, EMBED) # prediction prediction(results.best_harmony, EMBED)
help="Usage mode: (training, prediction, test_model(folder))") parser.add_argument("-use_gpu", metavar="G", type=int, default=-1, help="Manually assign a gpu number") args = parser.parse_args() mode = args.mode use_gpu = args.use_gpu # load settings settings = {} path = "./meta_data.json" with open(path) as file: settings = json.loads(file.read()) #TODO print stats # maybe print link to Tensorboard? print("Check out TensorBoard: https://localhost:6006") #TODO set gpu torch.cuda.init() torch.cuda.set_device(0) if mode == "train": training.testfold_training(settings) elif mode == "predict": prediction.prediction(settings) #TODO train, test, predict
'ependymoma', 'greymatter', 'glioblastoma', 'lowgradeglioma', 'lymphoma', 'medulloblastoma', 'meningioma', 'metastasis', 'nondiagnostic', 'pilocyticastrocytoma', 'pituitaryadenoma', 'pseudoprogression', 'schwannoma', 'whitematter' ] if __name__ == "__main__": # load a trained SRH model deepSRHmodel = load_model(args.model) # import SRH mosaic specimen = import_srh_dicom(args.strip_directory) print("SRH mosaic size is: " + str(specimen.shape)) # generate preprocessed image patches patches = patch_generator(specimen, step_size=100, old_preprocess=True) del specimen # predict on patches specimen_prediction = prediction( feedforward(patch_dict=patches, model=deepSRHmodel)) # print diagnosis print("SRH specimen diagnosis is: " + CLASS_NAMES[np.argmax(specimen_prediction)] + " (probability = " + str(np.max(specimen_prediction)) + ")") # display probability histogram plot_srh_probability_histogram(specimen_prediction)
from flask import Flask, render_template, json, request from flask import jsonify import nltk from prediction.prediction import prediction obj = prediction() app = Flask(__name__) pos = 0 neg = 0 @app.route('/predict', methods=['POST']) def predict(): text = request.json['feedback'] global pos, neg result = obj.predict([text]) print(result) word_freqs, max_freq = obj.word_cloud() response = {} response["word_freqs"] = str(word_freqs) response["max_freq"] = max_freq response["result"] = str(result) response["pos"] = pos response["neg"] = neg return jsonify(response)
rand = Random() rand.seed(int(time.time())) es = ec.GA(rand) es.terminator = terminators.evaluation_termination es.observer = inspyred.ec.observers.stats_observer final_pop = es.evolve(generator=generate_design_variable, evaluator=evaluate_optimization, maximize=False, pop_size=100, bounder=inspyred.ec.Bounder([-100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0, -100.0], [100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0, 100.0]), max_evaluations=10000, num_elites=1, num_inputs=1) if __name__ == '__main__': start_time = time.time() optimize_core() elapsed_time = time.time() - start_time print('Elapsed time: {}\nBest gene: {}\nBest fitness: {}'.format(elapsed_time, BEST_GENE, BEST_FITNESS)) # training & validation best_training(BEST_GENE, EMBED) # prediction prediction(BEST_GENE, EMBED)
ub = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100] start_time = time.time() xopt, fopt = pso(func, lb, ub, ieqcons=[], f_ieqcons=None, args=(), kwargs={}, swarmsize=100, omega=0.5, phip=0.5, phig=0.5, maxiter=50000, minstep=1e-8, minfunc=1e-8, debug=False) elapsed_time = time.time() - start_time print('Elapsed time: {}\nBest swarm: {}\nBest fitness: {}'.format( elapsed_time, xopt, fopt)) # training & validation best_training(xopt, EMBED) # prediction prediction(xopt, EMBED)