'tf' is not defined on load_model() - using lambda

Question

I have a Keras model that I am trying to export and use in a different python code.

Here is my code:

from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, GRU, Flatten, Dropout, Lambda
from keras.layers.embeddings import Embedding
import tensorflow as tf


EMBEDDING_DIM = 100

model = Sequential()
model.add(Embedding(vocab_size, 300, weights=[embedding_matrix], input_length=max_length, trainable=False))
model.add(Lambda(lambda x: tf.reduce_mean(x, axis=1)))
model.add(Dense(8, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train_pad, y_train, batch_size=128, epochs=25, validation_data=(X_val_pad, y_val), verbose=2)
model.save('my_model.h5') 

---

In another file, when I import my_model.h5 :

from keras.models import load_model
from keras.layers import Lambda
import tensorflow as tf


def learning(test_samples):
    model = load_model('my_model.h5')
    #ERROR HERE
    #rest of the code

The error is the following:

  in <lambda>
    model.add(Lambda(lambda x: tf.reduce_mean(x, axis=1)))
NameError: name 'tf' is not defined

After research, I got that the fact that I used lambda in my model is the reason for this problem, but I added these references and it didn't help:

from keras.models import load_model
from keras.layers import Lambda
import tensorflow as tf

What could be the problem?

Thank you

Answer 1

When loading the model, you need to explicitly handle custom objects or custom layers (CTRL+f the docs for Handling custom layers):

import tensorflow as tf
import keras
model = keras.models.load_model('my_model.h5', custom_objects={'tf': tf})