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

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})