Unable to load a model using load

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ height:90px;width:728px;box-sizing:border-box;
}

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()

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

Can you include the full traceback of the error?

– Matias Valdenegro
Jan 3 at 13:24

add a comment |

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()

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

Can you include the full traceback of the error?

– Matias Valdenegro
Jan 3 at 13:24

add a comment |

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()

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

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()

python opencv keras face-recognition dlib

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

edited Jan 3 at 13:25

Matias Valdenegro

32.5k45782

asked Jan 3 at 7:31

abhinay oja

asked Jan 3 at 7:31

abhinay oja

asked Jan 3 at 7:31

abhinay oja

Can you include the full traceback of the error?

– Matias Valdenegro
Jan 3 at 13:24

add a comment |

Can you include the full traceback of the error?

– Matias Valdenegro
Jan 3 at 13:24

Can you include the full traceback of the error?

– Matias Valdenegro
Jan 3 at 13:24

add a comment |

1 Answer
1

active

oldest

votes

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

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

add a comment |

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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

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

add a comment |

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

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

add a comment |

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

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

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

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

edited Jan 3 at 12:48

answered Jan 3 at 10:00

Timbus Calin

637

answered Jan 3 at 10:00

Timbus Calin

637

answered Jan 3 at 10:00

Timbus Calin

637

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

add a comment |

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

Here are my dependencies Keras -2.2.4 ,Keras-Applications -1.0.6 Keras-Preprocessing -1.0.5 tensorflow-1.8 from tensorflow.keras.models import load_model results in ModuleNotFoundError

– abhinay oja
Jan 3 at 11:03

Try uninstalling Keras. Since Keras is now a part of tensorflow backend, you should not need Keras installed separately. Upgrade TensorFlow and uninstall Keras.

– Timbus Calin
Jan 3 at 12:46

Also, make sure you import everything in the same manner; example : instead of keras.x use tensorflow.keras.x

– Timbus Calin
Jan 3 at 15:11

Thanks man it worked forgot to rename tensorflow.keras my other files so the error persisted for a while

– abhinay oja
Jan 3 at 17:45

No problem, I am very happy that it solved your issue! :D

– Timbus Calin
Jan 3 at 19:47

add a comment |

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