# get full keras response space on data refs = [] flattened = [] for point in all_data: conv_point = np.expand_dims(np.expand_dims(point, axis=2), axis=0) prob = model.predict_proba(conv_point)[0][0] refs.append(prob) flattened.append(point) refs = np.asarray(refs) labels_ref = np.concatenate((np.ones(len(sig_data)), np.zeros(len(bkg_data)))) flattened = np.asarray(flattened) # create drone drone = BaseModel(len(sig_data[0]), 1) drone.add_layer(5) drone.add_layer(1) conv = BasicConverter(num_epochs=epochNum, threshold=threshold) drone = conv.convert_model(drone, model, all_data, keras_conv=True) conv.save_history('./converted_hist.pkl') drone.save_model('./converted_drone.pkl') joblib.dump(scaler, open('./scaler_drone.pkl', 'wb')) refs_drone = [] flattened_drone = [] for point in all_data: prob = drone.evaluate_total(point) refs_drone.append(prob)
model.save('./keras_jet_conv2d_for_drone.h5') if not model: # check if model does exist print('ERROR: Could not load or create Keras model. Exiting...') sys.exit(1) # get full keras response space on data refs = [] for point in all_data: prob = model.predict_proba(point)[0][0] refs.append(prob) refs = np.asarray(refs) labels_ref = np.concatenate((np.ones(len(sig_img)), np.zeros(len(bkg_img)))) # create drone drone = BaseModel(len(sig_img[0].flatten()), 1) drone.add_layer(675) drone.add_layer(1) conv = BasicConverter(num_epochs=epochNum, threshold=threshold) drone = conv.convert_model(drone, model, all_data, conv_2d=True) conv.save_history('./converted_hist.pkl') drone.save_model('./converted_drone.pkl') refs_drone = [] for point in all_data: prob = drone.evaluate_total(point) refs_drone.append(prob) refs_drone = np.asarray(refs_drone) labels_drone = np.concatenate((np.ones(len(sig_img)), np.zeros(len(bkg_img))))