Darinyazanr

I want to use fm to solve a multi-class classification problem too,Can you give some advise？ @farimahfanaei @ibayer @macks22

## examples/run_classification_criteo.py model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary') model.compile("adam", "binary_crossentropy", metrics=['binary_crossentropy'], ) history = model.fit(train_model_input, train[target].values, batch_size=256, epochs=10, verbose=2, validation_split=0.2, ) pred_ans = model.predict(test_model_input, batch_size=256) print("test_model_input:", test[target].values) print("pred_ans:", pred_ans) model.save('./Criteo.pb') print('#'*120)...

How to slove this Q?

Have someone solve this problem?

@jrzaurin Do you have solved this issue? fastFM can save model well?

@jrzaurin thx very much.I will try to use lightFM. But I really don't kown why predict all 0s. fm ('fm', array([[ 0.04463324, 0.16691201, -0.026428 , -0.09678349, 0.00591884, 0.151462 , 0.03368704,...