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hexiangnan / neural_factorization_machine

TenforFlow Implementation of Neural Factorization Machine
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Command for evaluating NeuralFM model #1

Open rezacsedu opened 6 years ago

rezacsedu commented 6 years ago

Hi there,

Could you please provide the command for evaluating NeuralFM model? -i.e. I was expecting something similar presented at https://github.com/hexiangnan/attentional_factorization_machine.

Best,

hexiangnan commented 6 years ago

When you run the codes, it evaluates the model after each training epoch. See: https://github.com/hexiangnan/neural_factorization_machine/blob/master/NeuralFM.py#L285

On Sat, Sep 16, 2017 at 10:01 AM, Md. Rezaul Karim <notifications@github.com

wrote:

Hi there,

Could you please provide the command for evaluating NeuralFM model? -i.e. I was expecting there will something similar presented at https://github.com/hexiangnan/attentional_factorization_machine.

Best,

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/hexiangnan/neural_factorization_machine/issues/1, or mute the thread https://github.com/notifications/unsubscribe-auth/ABGxjqbZNygTPEeBksSJZOhaz0aH0zpfks5siywKgaJpZM4PZq4X .

-- Best Regards, Xiangnan He

rezacsedu commented 6 years ago

@hexiangnan, thanks so much. I have seperated the test set from train/validation and did the evalaution seperately:

print("RMSE: ") print(model.evaluate(data.Test_data)) # evaluate the results for an input set

BTW, could you please upload the script you used to convert the raw MovieLens (or frappe) dataset into LibFM format?

hexiangnan commented 6 years ago

Sorry I can not find the script. Should be easy to create one. Check the libFM manual Section 2.1.1: http://www.libfm.org/libfm-1.42.manual.pdf

On Sat, Sep 16, 2017 at 9:53 PM, Md. Rezaul Karim notifications@github.com wrote:

@hexiangnan https://github.com/hexiangnan, thanks so much. I have seperated the test set from train/validation and did the evalaution seperately: Model evaluation

print("RMSE: ") print(model.evaluate(data.Test_data)) # evaluate the results for an input set

BTW, could you please upload the script you used to convert the raw MovieLens (or frappe) dataset into LibFM format?

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/hexiangnan/neural_factorization_machine/issues/1#issuecomment-329969669, or mute the thread https://github.com/notifications/unsubscribe-auth/ABGxjiW38lDYIsyuWzKSPF6x-IKQgaRWks5si9LggaJpZM4PZq4X .

-- Best Regards, Xiangnan He