I am using Google Colab with the Tensorflow v2.17 and Keras v 3.4.1 libraries.
I need to save and load my models, but I haven't been able to make the '.keras' file format load correctly.
Here is the line for saving the model:
model.save(os.path.join(model_path, 'model_' + model_name + '.keras'))
Here is the line for loading the model:
model = keras.models.load_model(os.path.join(model_path, 'model_' + model_name + '.keras'), custom_objects=custom_objects)
This is my error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-9-882590e77519>](https://localhost:8080/#) in <cell line: 10>()
8
9 # Load the model
---> 10 model = keras.models.load_model(os.path.join(model_path, 'model_' + model_name + '.keras'), custom_objects=custom_objects)
11
12
3 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in _raise_loading_failure(error_msgs, warn_only)
454 warnings.warn(msg)
455 else:
--> 456 raise ValueError(msg)
457
458
ValueError: A total of 2 objects could not be loaded. Example error message for object <Dense name=z_mean, built=True>:
Layer 'z_mean' expected 2 variables, but received 0 variables during loading. Expected: ['kernel', 'bias']
List of objects that could not be loaded:
[<Dense name=z_mean, built=True>, <Dense name=z_log_var, built=True>]
This is the model that I trained:
latent_dim = 32
# Encoder
encoder_input = Input(shape=(height, width, channels), name='encoder_input')
x = Conv2D(64, (3, 3), activation='relu', padding='same')(encoder_input)
# Flatten layer
shape_before_flattening = K.int_shape(x)[1:]
x = Flatten()(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# Reparameterization trick
@keras.saving.register_keras_serializable()
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random_normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=1.0)
return z_mean + K.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
# Decoder
decoder_input = Input(K.int_shape(z)[1:])
x = Dense(np.prod(shape_before_flattening))(decoder_input)
x = Reshape(shape_before_flattening)(x)
decoder_output = Conv2D(channels, (3, 3), activation='sigmoid', padding='same')(x)
@register_keras_serializable('CustomLayer')
class CustomLayer(keras.layers.Layer):
def __init__(self, beta=1.0, **kwargs):
self.is_placeholder = True
super(CustomLayer, self).__init__(**kwargs)
self.beta = beta
self.recon_loss_metric = tf.keras.metrics.Mean(name='recon_loss')
self.kl_loss_metric = tf.keras.metrics.Mean(name='kl_loss')
def vae_loss(self, x, z_decoded, z_mean, z_log_var):
recon_loss = keras.losses.binary_crossentropy(K.flatten(x), K.flatten(z_decoded))
kl_loss = -0.5 * K.mean(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
return recon_loss, self.beta * kl_loss
def call(self, inputs):
x = inputs[0]
z_decoded = inputs[1]
z_mean = inputs[2]
z_log_var = inputs[3]
recon_loss, kl_loss = self.vae_loss(x, z_decoded, z_mean, z_log_var)
self.add_loss(K.mean(recon_loss + kl_loss))
self.recon_loss_metric.update_state(recon_loss)
self.kl_loss_metric.update_state(kl_loss)
return x
def compute_output_shape(self, input_shape):
return input_shape[0]
def get_metrics(self):
return {'recon_loss': self.recon_loss_metric.result().numpy(),
'kl_loss': self.kl_loss_metric.result().numpy()}
# Models
encoder = Model(encoder_input, [z_mean, z_log_var, z], name='encoder')
decoder = Model(decoder_input, decoder_output, name='decoder')
vae_output = decoder(encoder(encoder_input)[2])
y = CustomLayer()([encoder_input, vae_output, z_mean, z_log_var])
model = Model(encoder_input, y, name='vae')
This model was just used for testing the bug. I have used tf.keras as an alternative for loading the model, but I received the same error. Interestingly, when I run the code for the first time, this is included in the error output. When The same code is run again, the line is no longer included:
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:576: UserWarning: Skipping variable loading for optimizer 'adam', because it has 30 variables whereas the saved optimizer has 22 variables.
saveable.load_own_variables(weights_store.get(inner_path))
I have tested the code on the latest Keras v3.5, and have gotten similiar results:
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:713: UserWarning: Skipping variable loading for optimizer 'adam', because it has 30 variables whereas the saved optimizer has 22 variables.
saveable.load_own_variables(weights_store.get(inner_path))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-9-00610835a4a5>](https://localhost:8080/#) in <cell line: 10>()
8
9 # Load the model
---> 10 model = keras.models.load_model(os.path.join(model_path, 'model_' + model_name + '.keras'), custom_objects=custom_objects)
11
12
3 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in _raise_loading_failure(error_msgs, warn_only)
591 warnings.warn(msg)
592 else:
--> 593 raise ValueError(msg)
594
595
ValueError: A total of 2 objects could not be loaded. Example error message for object <Dense name=z_mean, built=True>:
Layer 'z_mean' expected 2 variables, but received 0 variables during loading. Expected: ['kernel', 'bias']
List of objects that could not be loaded:
[<Dense name=z_mean, built=True>, <Dense name=z_log_var, built=True>]
I have tested the bug again by saving and loading the model into separate weights and json files:
# saving
with open(os.path.join(model_path, 'model_' + model_name + '.json'), 'w') as json_file:
json_file.write(model.to_json())
model.save_weights(os.path.join(model_path, 'model_' + model_name + '.weights.h5'))
# loading
with open(os.path.join(model_path, 'model_' + model_name + '.json'), 'r') as json_file:
model_json = json_file.read()
model = model_from_json(model_json, custom_objects=custom_objects)
model.load_weights(os.path.join(model_path, 'model_' + model_name + '.weights.h5'))
The error is at least slightly different:
/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:713: UserWarning: Skipping variable loading for optimizer 'adam', because it has 34 variables whereas the saved optimizer has 22 variables.
saveable.load_own_variables(weights_store.get(inner_path))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-14-52bd158e3e0f>](https://localhost:8080/#) in <cell line: 11>()
9 model_json = json_file.read()
10 model = model_from_json(model_json, custom_objects=custom_objects)
---> 11 model.load_weights(os.path.join(model_path, 'model_' + model_name + '.weights.h5'))
12
13 # Load the encoder architecture and weights
1 frames
[/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py](https://localhost:8080/#) in _raise_loading_failure(error_msgs, warn_only)
591 warnings.warn(msg)
592 else:
--> 593 raise ValueError(msg)
594
595
ValueError: A total of 3 objects could not be loaded. Example error message for object <Conv2D name=conv2d, built=True>:
Layer 'conv2d' expected 2 variables, but received 0 variables during loading. Expected: ['kernel', 'bias']
List of objects that could not be loaded:
[<Conv2D name=conv2d, built=True>, <Dense name=z_mean, built=True>, <Dense name=z_log_var, built=True>]
Ultimately it would be a lot better to find out that I've been doing something wrong and I can fix this problem myself. I've been hung up on this for awhile, and I have a thesis to write.
Comment From: mehtamansi29
Hi @ErrolDaRocha -
Thanks for reporting the issue. Here you can save and load the model in .h5 format rather then keras. It will load the custom layers and Dense layer properly without error.
# saving
import os
model_path= '/content/drive/MyDrive/issue'
model_name= 'custom_layer_model'
model.save(os.path.join(model_path, 'model_' + model_name + '.h5'))
# loading
new_model_path= '/content/drive/MyDrive/issue/model_custom_layer_model.h5'
loaded_model = keras.models.load_model(new_model_path)
print(loaded_model.summary())
Attached the gist for your reference.
Comment From: mehtamansi29
Hi @ErrolDaRocha -
Are you still able to reproduce this issue ?
Comment From: ghsanti
@ErrolDaRocha
Comment From: github-actions[bot]
This issue is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.
Comment From: harshaljanjani
Hello! Since the bug is no longer reproducible, please consider closing this issue, thanks!
Code to reproduce the outdated issue (which runs perfectly fine with Keras 3.8 with across backends):
import os
import numpy as np
from keras import ops
import keras
from keras import layers, backend as K
from keras.layers import Input, Conv2D, Dense, Flatten, Lambda, Reshape
from keras.models import Model
from keras.saving import register_keras_serializable
from keras.models import model_from_json
import tensorflow as tf
from keras import ops
import torch
latent_dim = 32
os.environ['KERAS_BACKEND'] = 'numpy' # works across 'numpy', 'tensorflow' and 'jax' as well
encoder_input = Input(shape=(28, 28, 1), name='encoder_input')
x = Conv2D(64, (3, 3), activation='relu', padding='same')(encoder_input)
shape_before_flattening = ops.shape(x)[1:]
x = Flatten()(x)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# reparameterization trick
@keras.saving.register_keras_serializable()
def sampling(args):
z_mean, z_log_var = args
epsilon = K.random.normal(shape=(K.shape(z_mean)[0], latent_dim), mean=0., stddev=1.0)
return z_mean + ops.exp(z_log_var / 2) * epsilon
z = Lambda(sampling, output_shape=(latent_dim,), name='z')([z_mean, z_log_var])
decoder_input = Input(ops.shape(z)[1:])
x = Dense(np.prod(shape_before_flattening))(decoder_input)
x = Reshape(shape_before_flattening)(x)
decoder_output = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)
@register_keras_serializable('CustomLayer')
class CustomLayer(layers.Layer):
def __init__(self, beta=1.0, **kwargs):
self.is_placeholder = True
super(CustomLayer, self).__init__(**kwargs)
self.beta = beta
def call(self, inputs):
x = inputs[0]
return x
encoder = Model(encoder_input, [z_mean, z_log_var, z], name='encoder')
decoder = Model(decoder_input, decoder_output, name='decoder')
vae_output = decoder(encoder(encoder_input)[2])
y = CustomLayer()([encoder_input, vae_output])
model = Model(encoder_input, y, name='vae')
model.compile(optimizer='adam', loss='binary_crossentropy')
model_path = 'models'
model_name = 'custom_layer_model'
os.makedirs(model_path, exist_ok=True)
model.save(os.path.join(model_path, 'model_' + model_name + '.keras'))
loaded_model = keras.models.load_model(os.path.join(model_path, 'model_' + model_name + '.keras'))
print(loaded_model.summary())
# model in .json and weights
model_json = model.to_json()
with open(os.path.join(model_path, 'model_' + model_name + '.json'), 'w') as json_file:
json_file.write(model_json)
model.save_weights(os.path.join(model_path, 'model_' + model_name + '.weights.h5'))
# from .json and .weights
with open(os.path.join(model_path, 'model_' + model_name + '.json'), 'r') as json_file:
model_json = json_file.read()
loaded_model = model_from_json(model_json)
loaded_model.load_weights(os.path.join(model_path, 'model_' + model_name + '.weights.h5'))
print(loaded_model.summary())
Environment:
- Keras version: 3.8.0
- TensorFlow version: 2.18.0
- NumPy version: 2.2.1
- PyTorch version: 2.5.1+cu121
Attaching the gist here.
Comment From: github-actions[bot]
This issue is stale because it has been open for 14 days with no activity. It will be closed if no further activity occurs. Thank you.
Comment From: github-actions[bot]
This issue was closed because it has been inactive for 28 days. Please reopen if you'd like to work on this further.