ValueError: You are trying to load a weight file containing 6 layers into a model with 0

1

I have a simple keras model. After the model is saved. I am unable to load the model. This is the error I get after instantiating the model and trying to load weights:

Using TensorFlow backend.

Traceback (most recent call last):

  File "test.py", line 4, in <module>

    model = load_model("test.h5")

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 419, in load_model

  model = _deserialize_model(f, custom_objects, compile)

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 258, in _deserialize_model

.format(len(layer_names), len(filtered_layers))

 ValueError: You are trying to load a weight file containing 6 layers into a model with 0 layers

For instantiating the model and using model.load_weights and doing a model summary. I get None when I print the model using print(model)

Traceback (most recent call last):

File "test.py", line 7, in <module>

    print(model.summary())

AttributeError: 'NoneType' object has no attribute 'summary'

Here is my Network:

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D, InputLayer, Flatten,    Dense, BatchNormalization





def create_model():

    kernel_size = 5

    pool_size = 2

    batchsize = 64

    model = Sequential()

    model.add(InputLayer((36, 120, 1)))

    model.add(Conv2D(filters=20, kernel_size=kernel_size,    activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Conv2D(filters=50, kernel_size=kernel_size, activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Flatten())

    model.add(Dense(120, activation='relu'))

    model.add(Dense(2, activation='relu'))

    return model

Training procedure script:

import numpy as np

from keras import optimizers

from keras import losses

from sklearn.model_selection import train_test_split

from model import create_model





def data_loader(images, pos):

    while(True):

        for i in range(0, images.shape[0], 64):

            if (i+64) < images.shape[0]:

                img_batch = images[i:i+64]

                pos_batch = pos[i:i+64]

                yield img_batch, pos_batch

            else:

                img_batch = images[i:]

                pos_batch = pos[i:]

                yield img_batch, pos_batch





def main():

    model = create_model()

    sgd = optimizers.Adadelta(lr=0.01, rho=0.95, epsilon=None, decay=0.0)

    model.compile(loss=losses.mean_squared_error, optimizer=sgd)

    print("traning")

    data = np.load("data.npz")

    images = data['images']

    pos = data['pos']

    x_train, x_test, y_train, y_test = train_test_split(images, pos, test_size=0.33, random_state=42)

    model.fit_generator(data_loader(x_train, y_train), steps_per_epoch=x_train.shape[0]//64, validation_data=data_loader(x_test, y_test), 

                    validation_steps = x_test.shape[0]//64, epochs=1)

    model.save('test.h5')

    model.save_weights('test_weights.h5')



    print("training done")





if __name__ == '__main__':

    main()

python keras neural-network deep-learning

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

asked Jan 1 at 6:02

joganjogan

82

add a comment | 

1

I have a simple keras model. After the model is saved. I am unable to load the model. This is the error I get after instantiating the model and trying to load weights:

Using TensorFlow backend.

Traceback (most recent call last):

  File "test.py", line 4, in <module>

    model = load_model("test.h5")

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 419, in load_model

  model = _deserialize_model(f, custom_objects, compile)

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 258, in _deserialize_model

.format(len(layer_names), len(filtered_layers))

 ValueError: You are trying to load a weight file containing 6 layers into a model with 0 layers

For instantiating the model and using model.load_weights and doing a model summary. I get None when I print the model using print(model)

Traceback (most recent call last):

File "test.py", line 7, in <module>

    print(model.summary())

AttributeError: 'NoneType' object has no attribute 'summary'

Here is my Network:

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D, InputLayer, Flatten,    Dense, BatchNormalization





def create_model():

    kernel_size = 5

    pool_size = 2

    batchsize = 64

    model = Sequential()

    model.add(InputLayer((36, 120, 1)))

    model.add(Conv2D(filters=20, kernel_size=kernel_size,    activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Conv2D(filters=50, kernel_size=kernel_size, activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Flatten())

    model.add(Dense(120, activation='relu'))

    model.add(Dense(2, activation='relu'))

    return model

Training procedure script:

import numpy as np

from keras import optimizers

from keras import losses

from sklearn.model_selection import train_test_split

from model import create_model





def data_loader(images, pos):

    while(True):

        for i in range(0, images.shape[0], 64):

            if (i+64) < images.shape[0]:

                img_batch = images[i:i+64]

                pos_batch = pos[i:i+64]

                yield img_batch, pos_batch

            else:

                img_batch = images[i:]

                pos_batch = pos[i:]

                yield img_batch, pos_batch





def main():

    model = create_model()

    sgd = optimizers.Adadelta(lr=0.01, rho=0.95, epsilon=None, decay=0.0)

    model.compile(loss=losses.mean_squared_error, optimizer=sgd)

    print("traning")

    data = np.load("data.npz")

    images = data['images']

    pos = data['pos']

    x_train, x_test, y_train, y_test = train_test_split(images, pos, test_size=0.33, random_state=42)

    model.fit_generator(data_loader(x_train, y_train), steps_per_epoch=x_train.shape[0]//64, validation_data=data_loader(x_test, y_test), 

                    validation_steps = x_test.shape[0]//64, epochs=1)

    model.save('test.h5')

    model.save_weights('test_weights.h5')



    print("training done")





if __name__ == '__main__':

    main()

python keras neural-network deep-learning

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

asked Jan 1 at 6:02

joganjogan

82

add a comment | 

1

1

1

I have a simple keras model. After the model is saved. I am unable to load the model. This is the error I get after instantiating the model and trying to load weights:

Using TensorFlow backend.

Traceback (most recent call last):

  File "test.py", line 4, in <module>

    model = load_model("test.h5")

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 419, in load_model

  model = _deserialize_model(f, custom_objects, compile)

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 258, in _deserialize_model

.format(len(layer_names), len(filtered_layers))

 ValueError: You are trying to load a weight file containing 6 layers into a model with 0 layers

For instantiating the model and using model.load_weights and doing a model summary. I get None when I print the model using print(model)

Traceback (most recent call last):

File "test.py", line 7, in <module>

    print(model.summary())

AttributeError: 'NoneType' object has no attribute 'summary'

Here is my Network:

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D, InputLayer, Flatten,    Dense, BatchNormalization





def create_model():

    kernel_size = 5

    pool_size = 2

    batchsize = 64

    model = Sequential()

    model.add(InputLayer((36, 120, 1)))

    model.add(Conv2D(filters=20, kernel_size=kernel_size,    activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Conv2D(filters=50, kernel_size=kernel_size, activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Flatten())

    model.add(Dense(120, activation='relu'))

    model.add(Dense(2, activation='relu'))

    return model

Training procedure script:

import numpy as np

from keras import optimizers

from keras import losses

from sklearn.model_selection import train_test_split

from model import create_model





def data_loader(images, pos):

    while(True):

        for i in range(0, images.shape[0], 64):

            if (i+64) < images.shape[0]:

                img_batch = images[i:i+64]

                pos_batch = pos[i:i+64]

                yield img_batch, pos_batch

            else:

                img_batch = images[i:]

                pos_batch = pos[i:]

                yield img_batch, pos_batch





def main():

    model = create_model()

    sgd = optimizers.Adadelta(lr=0.01, rho=0.95, epsilon=None, decay=0.0)

    model.compile(loss=losses.mean_squared_error, optimizer=sgd)

    print("traning")

    data = np.load("data.npz")

    images = data['images']

    pos = data['pos']

    x_train, x_test, y_train, y_test = train_test_split(images, pos, test_size=0.33, random_state=42)

    model.fit_generator(data_loader(x_train, y_train), steps_per_epoch=x_train.shape[0]//64, validation_data=data_loader(x_test, y_test), 

                    validation_steps = x_test.shape[0]//64, epochs=1)

    model.save('test.h5')

    model.save_weights('test_weights.h5')



    print("training done")





if __name__ == '__main__':

    main()

python keras neural-network deep-learning

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

asked Jan 1 at 6:02

joganjogan

82

I have a simple keras model. After the model is saved. I am unable to load the model. This is the error I get after instantiating the model and trying to load weights:

Using TensorFlow backend.

Traceback (most recent call last):

  File "test.py", line 4, in <module>

    model = load_model("test.h5")

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 419, in load_model

  model = _deserialize_model(f, custom_objects, compile)

  File "/usr/lib/python3.7/site-packages/keras/engine/saving.py", line 258, in _deserialize_model

.format(len(layer_names), len(filtered_layers))

 ValueError: You are trying to load a weight file containing 6 layers into a model with 0 layers

For instantiating the model and using model.load_weights and doing a model summary. I get None when I print the model using print(model)

Traceback (most recent call last):

File "test.py", line 7, in <module>

    print(model.summary())

AttributeError: 'NoneType' object has no attribute 'summary'

Here is my Network:

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D, InputLayer, Flatten,    Dense, BatchNormalization





def create_model():

    kernel_size = 5

    pool_size = 2

    batchsize = 64

    model = Sequential()

    model.add(InputLayer((36, 120, 1)))

    model.add(Conv2D(filters=20, kernel_size=kernel_size,    activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Conv2D(filters=50, kernel_size=kernel_size, activation='relu', padding='same'))

    model.add(BatchNormalization())

    model.add(MaxPooling2D(pool_size))

    model.add(Flatten())

    model.add(Dense(120, activation='relu'))

    model.add(Dense(2, activation='relu'))

    return model

Training procedure script:

import numpy as np

from keras import optimizers

from keras import losses

from sklearn.model_selection import train_test_split

from model import create_model





def data_loader(images, pos):

    while(True):

        for i in range(0, images.shape[0], 64):

            if (i+64) < images.shape[0]:

                img_batch = images[i:i+64]

                pos_batch = pos[i:i+64]

                yield img_batch, pos_batch

            else:

                img_batch = images[i:]

                pos_batch = pos[i:]

                yield img_batch, pos_batch





def main():

    model = create_model()

    sgd = optimizers.Adadelta(lr=0.01, rho=0.95, epsilon=None, decay=0.0)

    model.compile(loss=losses.mean_squared_error, optimizer=sgd)

    print("traning")

    data = np.load("data.npz")

    images = data['images']

    pos = data['pos']

    x_train, x_test, y_train, y_test = train_test_split(images, pos, test_size=0.33, random_state=42)

    model.fit_generator(data_loader(x_train, y_train), steps_per_epoch=x_train.shape[0]//64, validation_data=data_loader(x_test, y_test), 

                    validation_steps = x_test.shape[0]//64, epochs=1)

    model.save('test.h5')

    model.save_weights('test_weights.h5')



    print("training done")





if __name__ == '__main__':

    main()

python keras neural-network deep-learning

python keras neural-network deep-learning

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

asked Jan 1 at 6:02

joganjogan

82

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

asked Jan 1 at 6:02

joganjogan

82

share|improve this question

share|improve this question

edited Jan 1 at 10:18

Amir

7,89264173

edited Jan 1 at 10:18

Amir

7,89264173

edited Jan 1 at 10:18

Amir

7,89264173

7,89264173

asked Jan 1 at 6:02

joganjogan

82

asked Jan 1 at 6:02

joganjogan

82

asked Jan 1 at 6:02

joganjogan

82

82

add a comment | 

add a comment | 

1 Answer
1

active

oldest

votes

1

1. 
   

   Drop InputLayer and use input_shape in first layer. Your code will be similar to:

   
   
   

   model = Sequentional()
   
   model.add(Conv2D(filters=20,..., input_shape=(36, 120, 1)))

   
   
   

   It seems models with InputLayer are not serialized to HDF5 correctly.

   
   



2. Upgrade your Tensorflow and Keras to the latest version




3. Fix the interpreter problem as explained here

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  That worked, removing the input layer. Thanks man
  
  – jogan
  Jan 1 at 9:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.
  
  – Amir
  Jan 1 at 18:27
  
  
  

  
  
  
  

add a comment | 

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

active

oldest

votes

active

oldest

votes

1

1. 
   

   Drop InputLayer and use input_shape in first layer. Your code will be similar to:

   
   
   

   model = Sequentional()
   
   model.add(Conv2D(filters=20,..., input_shape=(36, 120, 1)))

   
   
   

   It seems models with InputLayer are not serialized to HDF5 correctly.

   
   



2. Upgrade your Tensorflow and Keras to the latest version




3. Fix the interpreter problem as explained here

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  That worked, removing the input layer. Thanks man
  
  – jogan
  Jan 1 at 9:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.
  
  – Amir
  Jan 1 at 18:27
  
  
  

  
  
  
  

add a comment | 

1

1. 
   

   Drop InputLayer and use input_shape in first layer. Your code will be similar to:

   
   
   

   model = Sequentional()
   
   model.add(Conv2D(filters=20,..., input_shape=(36, 120, 1)))

   
   
   

   It seems models with InputLayer are not serialized to HDF5 correctly.

   
   



2. Upgrade your Tensorflow and Keras to the latest version




3. Fix the interpreter problem as explained here

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  That worked, removing the input layer. Thanks man
  
  – jogan
  Jan 1 at 9:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.
  
  – Amir
  Jan 1 at 18:27
  
  
  

  
  
  
  

add a comment | 

1

1

1

1. 
   

   Drop InputLayer and use input_shape in first layer. Your code will be similar to:

   
   
   

   model = Sequentional()
   
   model.add(Conv2D(filters=20,..., input_shape=(36, 120, 1)))

   
   
   

   It seems models with InputLayer are not serialized to HDF5 correctly.

   
   



2. Upgrade your Tensorflow and Keras to the latest version




3. Fix the interpreter problem as explained here

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

1. 
   

   Drop InputLayer and use input_shape in first layer. Your code will be similar to:

   
   
   

   model = Sequentional()
   
   model.add(Conv2D(filters=20,..., input_shape=(36, 120, 1)))

   
   
   

   It seems models with InputLayer are not serialized to HDF5 correctly.

   
   



2. Upgrade your Tensorflow and Keras to the latest version




3. Fix the interpreter problem as explained here

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

share|improve this answer

share|improve this answer

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

edited Jan 1 at 13:50

Keyur Potdar

5,64451632

5,64451632

answered Jan 1 at 8:03

AmirAmir

7,89264173

answered Jan 1 at 8:03

AmirAmir

7,89264173

answered Jan 1 at 8:03

AmirAmir

7,89264173

7,89264173

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  That worked, removing the input layer. Thanks man
  
  – jogan
  Jan 1 at 9:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.
  
  – Amir
  Jan 1 at 18:27
  
  
  

  
  
  
  

add a comment | 

• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  That worked, removing the input layer. Thanks man
  
  – jogan
  Jan 1 at 9:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  @jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.
  
  – Amir
  Jan 1 at 18:27
  
  
  

  
  
  
  

1

1

That worked, removing the input layer. Thanks man

– jogan
Jan 1 at 9:51

That worked, removing the input layer. Thanks man

– jogan
Jan 1 at 9:51

@jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.

– Amir
Jan 1 at 18:27

@jogan If the answer is ok, please mark it as accepted, so other users can benefit from this question and answer.

– Amir
Jan 1 at 18:27

add a comment | 

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• Making statements based on opinion; back them up with references or personal experience.

To learn more, see our tips on writing great answers.

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