I'm using a custom mask in my Keras model. When I try to load the model with model=tf.saved_model.load('model.keras') from a .keras file, I get the following error:
TypeError: <keras.src.layers.core.masking.Masking object at 0x7e7735fa1460> could not be deserialized properly. Please ensure that components that are Python object instances (layers, models, etc.) returned by `get_config()` are explicitly deserialized in the model's `from_config()` method.
Here is my model:
N_FEATURES = 6
MASK_VALUE = np.asarray([0.0 for i in range(N_FEATURES)])
def get_clean_model():
# Input layer
input_layer = Input(shape=(None, N_FEATURES))
masked_input = Masking(mask_value=MASK_VALUE)(input_layer)
# LSTM layer with regularization
lstm_layer = LSTM(units=N_FEATURES, activation='tanh', return_sequences=True,
recurrent_regularizer='l2', kernel_regularizer='l2')(masked_input)
# Dropout layer
dropout_layer = Dropout(0.05)(lstm_layer)
# Dense layers with regularization
dense_layer1 = Dense(N_FEATURES, activation='sigmoid', kernel_regularizer='l2')(dropout_layer)
# Skip connection: Concatenate masked input with dense_layer1
concatenated_layer = Concatenate()([masked_input, dense_layer1])
dense_layer2 = Dense(N_FEATURES*2, activation='sigmoid', kernel_regularizer='l2')(dense_layer1)
# Dropout layer
dropout_layer2 = Dropout(0.05)(dense_layer2)
# Output layer
output_layer = Dense(1, activation='sigmoid')(dropout_layer2)
# Create the model
model = Model(inputs=input_layer, outputs=output_layer)
This question shows how to do it for custom layers, but I don't have any custom layers. How can I serialize and retrieve this model? Thank you!
The problem might be caused by new version of wrapt package,
for more context see here
Could you try with setting this environment variable?
WRAPT_DISABLE_EXTENSIONS=true