# 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))))