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ctr's Issues

NFM模型代码疑问

你好,请问NFM代码中model_fn_sparse函数是做什么的呢?model_fn_dense这个函数不是已经实现论文中的模型了吗?多出的这个model_fn_sparse是做什么的呢?

PNN模型中代码疑问

你好,看了PNN下PNN.py中关于OPNN下的代码。有两个疑问:
1.outer_product = tf.concat(outer_collection, axis=1)这句加不加有什么用吗?还是其他什么原因?
2.“tf.einsum('ai,aj->aij',vi,vj)”这一句怎么理解呢?和论文中哪一部分对应呢?

DIN模型的代码疑问

1.DIN.py中attention函数下
dense = tf.layers.dense(dense, units= unit, activation = tf.nn.relu, name = 'attention_{}'.format(i))
这里的激活函数tf.nn.relu是不是换成论文中提到的Dice更好?
2.CTR这类模型在对特征做embedding时候,是不是每一个特征变量都要单独做一个从特征维度(即每一个变量one_hot后的维度)到D维(即embedding输出后的固定维度)的变换?

Performance issue in utils.py (by P3)

Hello! I've found a performance issue in utils.py: .batch(MODEL_PARAMS['batch_size'] )(line 72) should be called before .map( parse_example_helper_csv, num_parallel_calls=8 )(line 46), which could make your program more efficient.

Here is the tensorflow document to support it.

Besides, you need to check the function parse_example_helper_csv called in .map( parse_example_helper_csv, num_parallel_calls=8 ) whether to be affected or not to make the changed code work properly. For example, if parse_example_helper_csv needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z) after fix.

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

FM代码的一处细节疑问

你好!FM代码中“interaction_term = 0.5 * tf.reduce_mean(sum_square - square_sum, axis=1, keep_dims= True)”
论文中用的是求和,这里用的是求均值。是考虑到k不同,且k选取越大、二阶交叉项越大,所以才取的均值吗?为了消除不同k取值的影响?

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