tffm
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geffy
TensorFlow implementation of an arbitrary order Factorization Machine
I used sparse style and the following error occurs: `AttributeError Traceback (most recent call last) in ----> 1 model.fit(X_tr, y_tr, show_progress=True) ~\anaconda3\lib\site-packages\tffm\models.py in fit(self, X, y, sample_weight, n_epochs, show_progress) 124...
This is not an issue but more of a question. Is it so that tffm supports SGD but not ALS and MCMC algorithms? This was my understanding with a quick...
Issue with tensorflow 2.0: module 'tensorflow_core._api.v2.train' has no attribute 'AdamOptimizer'
pip install tensorflow==2.0 import numpy as np import tensorflow as tf from tffm import TFFMClassifier gives error: --------------------------------------------------------------------------- AttributeError Traceback (most recent call last) in ----> 1 from tffm import...
I want to manually verify the prediction of a particular testing sample.
 In base.py, there is a function named **batcher**. You define a variable **upper_bound**, but why not use it to update **ret_y** and **ret_w** ?
Hi there, I'm thinking of contributing codes for BPR. What is the best way to extend the code to handle this optimization in your opinion?
How can I save a trained tffm for future predictions ? The model object cannot be pickled due to thread.RLock type object and isn't json serializable either.
I ran the model on my data and could see huge variations in predictions with respect to previous training on the same data. How can I tackle that so that...
for example this http://srome.github.io/Leveraging-Factorization-Machines-for-Sparse-Data-and-Supervised-Visualization/ gives bid data use can you run code for this data? X_train.shape #(7580, 1048576) where number of features is 1048576
