Unable to load a model using load_model()

Question

All the below imports are executed without any error

import cv2
import numpy as np
import dlib
import glob
from scipy.spatial import distance
from imutils import face_utils
from keras.models import load_model
from fr_utils import *
from inception_blocks_v2 import *

When I try to import a pre trained model 'face-rec_Google.h5' which has been trained using facenet. The file('face-rec_Google.h5') is in my project folder.

x = load_model('face-rec_Google.h5')

I am getting the following error

ValueError: Initializer for variable conv1_4/kernel/ is from inside a 
control-flow construct, such as a loop or conditional. When creating a   
variable inside a loop or conditional,
use a lambda as the initializer.

here's the entire face recognition code:

import cv2
import numpy as np
import dlib
import glob
from scipy.spatial import distance
from imutils import face_utils
from keras.models import load_model
from fr_utils import *
from inception_blocks_v2 import *

detector = dlib.get_frontal_face_detector()

x = load_model('face-rec_Google.h5')
print("Total Params:", x.count_params())
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
thresh = 0.25


def eye_aspect_ratio(eye):
    A = distance.euclidean(eye[1], eye[5])
    B = distance.euclidean(eye[2], eye[4])
    C = distance.euclidean(eye[0], eye[3])
    ear = (A + B) / (2.0 * C)
    return ear

def recognize_face(face_descriptor, database):
    encoding = img_to_encoding(face_descriptor, FRmodel)
    min_dist = 100
    identity = None

    # Loop over the database dictionary's names and encodings.
    for (name, db_enc) in database.items():

        # Compute L2 distance between the target "encoding" and the current "emb" from the database.
        dist = np.linalg.norm(db_enc - encoding)

        print('distance for %s is %s' % (name, dist))

        # If this distance is less than the min_dist, then set min_dist to dist, and identity to name
        if dist < min_dist:
            min_dist = dist
            identity = name

    if int(identity) <=4:
        return str('Akshay'), min_dist
    if int(identity) <=8:
        return str('Apoorva'), min_dist



def extract_face_info(img, img_rgb, database,ear):
    faces = detector(img_rgb)
    x, y, w, h = 0, 0, 0, 0
    if len(faces) > 0:
        for face in faces:
            (x, y, w, h) = face_utils.rect_to_bb(face)
            cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)
            image = img[y:y + h, x:x + w]
            name, min_dist = recognize_face(image, database)
            if ear > thresh:
                if min_dist < 0.1:
                    cv2.putText(img, "Face : " + name, (x, y - 50), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
                    cv2.putText(img, "Dist : " + str(min_dist), (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
                else:
                    cv2.putText(img, 'No matching faces', (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
            else:
                cv2.putText(img, 'Eyes Closed', (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)

def initialize():
    #load_weights_from_FaceNet(FRmodel)
    #we are loading model from keras hence we won't use the above method
    database = {}

    # load all the images of individuals to recognize into the database
    for file in glob.glob("images//"):
        identity = os.path.splitext(os.path.basename(file))[0]
        database[identity] = fr_utils.img_path_to_encoding(file, FRmodel)
    return database


def recognize():
    database = initialize()
    cap = cv2.VideoCapture(0)
    (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
    (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
    while True:
        ret, img = cap.read()
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        subjects = detector(gray, 0)
        for subject in subjects:
            shape = predictor(gray, subject)
            shape = face_utils.shape_to_np(shape)  # converting to NumPy Array
            leftEye = shape[lStart:lEnd]
            rightEye = shape[rStart:rEnd]
            leftEAR = eye_aspect_ratio(leftEye)
            rightEAR = eye_aspect_ratio(rightEye)
            ear = (leftEAR + rightEAR) / 2.0
            leftEyeHull = cv2.convexHull(leftEye)
            rightEyeHull = cv2.convexHull(rightEye)
            cv2.drawContours(img, [leftEyeHull], -1, (0, 255, 0), 1)
            cv2.drawContours(img, [rightEyeHull], -1, (0, 255, 0), 1)
            extract_face_info(img, img_rgb, database,ear)
        cv2.imshow('Recognizing faces', img)
        if cv2.waitKey(1) == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()


recognize()

Answer 1

What Keras version are you using?

One suggestion I would make is to try to update Keras to its latest version(2.2.4 at the time of writing this comment).

Make sure you also update keras.applications and keras.preprocessing to their latest version.

If this does not work, you could try the following option:

First uninstall Keras and its applications+preprocessing(I forgot to add this, sorry)

Then, update the version of your TensorFlow. After this step, follow the suggestion below.

Try using the load_model method via tensorflow. Ex: from tensorflow.keras.models import load_model