hi主要是沒太多時間用來思考，所以很多東西都不是太清楚，只會裝逼，這樣不好，要從理論/代碼上理解到底說的是什麼事情，這是科學問題：是什麼和爲什麼？作爲搬磚工還是有必要深入瞭解，不然真的只能搬磚了。可在我的主頁搜索相關博文，不再挨個附上。【面試明明是技術面，卻總是在問科學的問題，這很扯淡】
For Recommendation in Deep learning QQ Group 102948747
For Visual in deep learning QQ Group 629530787
不接受這個網頁的私聊/私信！！！
今年的目標沒有實現——訪客50萬，總排名5000，明年三月份要實現這個東西。
之前實現了faiss召回，領導又問了，爲啥採用IP的方式？其意義在哪裏？臥槽，這是真的防不勝防啊，ytb中也是如此啊，但真的沒有細緻瞭解這玩意，這裏還是很真探討下這些問題。attention及GRU的作用是什麼？爲啥用GRU而不是LSTM，RNN？臥槽，隨便一問就是面試題中的致命點，既是送分題，也是送命題。
【20210103續】
只要糖放得多，你就不會覺得生活有多苦，幾塊錢的事，非要百萬來解決，何必爲難自己。
試了下RNN，結果稍微差點，但絕對差不到一個百分點（HR一個爲0.262，一個爲0.268，MAP0.0506，一個爲0.0532，好的結果是GRU的），昨晚也試了LSTM，但直接替換函數不對，臥槽，再改改吧，報錯如下：
Traceback (most recent call last):
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 854, in __call__
outputs = call_fn(cast_inputs, *args, **kwargs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py", line 237, in wrapper
raise e.ag_error_metadata.to_exception(e)
tensorflow.python.framework.errors_impl.OperatorNotAllowedInGraphError: in converted code:
relative to /data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python:
ops/rnn_cell_impl.py:1017 call
(c_prev, m_prev) = state
framework/ops.py:547 __iter__
self._disallow_iteration()
framework/ops.py:543 _disallow_iteration
self._disallow_in_graph_mode("iterating over `tf.Tensor`")
framework/ops.py:523 _disallow_in_graph_mode
" this function with @tf.function.".format(task))
OperatorNotAllowedInGraphError: iterating over `tf.Tensor` is not allowed in Graph execution. Use Eager execution or decorate this function with @tf.function.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/data/logs/xulm1/myconda/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/data/logs/xulm1/myconda/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "video_update_rec_items.py", line 801, in run_train
model = Model(node_count+1, checkpoint_path, **kwargs)
File "video_update_rec_items.py", line 304, in __init__
self.loss, self.session_state, self.logits = self.__forward()
File "video_update_rec_items.py", line 345, in __forward
node_state = self.ggnn(self.adj_in, self.adj_out, node_state, nw)
File "video_update_rec_items.py", line 216, in __call__
_, state = self.gru_cell(tf.reshape(gru_in, [-1, 2*self.hidden_size]), tf.reshape(state, [-1, self.hidden_size]))
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py", line 386, in __call__
self, inputs, state, scope=scope, *args, **kwargs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/layers/base.py", line 548, in __call__
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 861, in __call__
str(e) + '\n"""')
TypeError: You are attempting to use Python control flow in a layer that was not declared to be dynamic. Pass `dynamic=True` to the class constructor.我以爲直接按照提示改就行了，臥槽。還是不行啊。咋整啊，
>>> import tensorflow as tf
>>> tf.compat.v1.nn.rnn_cell.LSTMCell(32,dynamic=True)
WARNING:tensorflow:From <stdin>:1: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.
<tensorflow.python.ops.rnn_cell_impl.LSTMCell object at 0x7fdacf531550>
>>> quit()
報錯如下：這下好了，更沒法改了
Traceback (most recent call last):
File "/data/logs/xulm1/myconda/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/data/logs/xulm1/myconda/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "video_update_rec_items.py", line 801, in run_train
model = Model(node_count+1, checkpoint_path, **kwargs)
File "video_update_rec_items.py", line 304, in __init__
self.loss, self.session_state, self.logits = self.__forward()
File "video_update_rec_items.py", line 345, in __forward
node_state = self.ggnn(self.adj_in, self.adj_out, node_state, nw)
File "video_update_rec_items.py", line 216, in __call__
_, state = self.gru_cell(tf.reshape(gru_in, [-1, 2*self.hidden_size]), tf.reshape(state, [-1, self.hidden_size]))
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/ops/rnn_cell_impl.py", line 386, in __call__
self, inputs, state, scope=scope, *args, **kwargs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/layers/base.py", line 548, in __call__
outputs = super(Layer, self).__call__(inputs, *args, **kwargs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 869, in __call__
outputs = self._symbolic_call(inputs)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 2158, in _symbolic_call
output_shapes = self.compute_output_shape(input_shapes)
File "/data/logs/xulm1/myconda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/base_layer.py", line 646, in compute_output_shape
raise NotImplementedError
NotImplementedError還是搜索下LSTM與GRU的基本用法吧，看看是不是有所區別。好難哦，我看LSTM基本上都是和dynamic_rnn一起使用的，而我這裏沒有，加了這個就更難改了。
我看還是算了，這個就當實現了吧，問就是效果差點，，，，小明哥你不能這樣，不講武德。。。我特麼源碼給你，要不你幫我改成LSTM的，我跪謝。
實在是能力有限，改就傷筋動骨了，不好整。
【問題2：爲啥用IP求內積/相似度/距離？兩個embedding求IP的意義是什麼？】（這纔是領導問的，小聲bb，其他的是我自己發問的）
這其實是一個問題，下面從頭來過
將所有的會話圖喂到ggnn中，得到所有節點的embedding，然後通過聚合得到會話的全局embedding，採用的是self-attention機制更好的表達。會話局部的embedding可簡單直接認爲就是最後一個點擊的item的embedding。兩個會話的embedding通過concat後線性轉換得到最後會話表達。這是會話embedding的來源。
爲給每一個item計算score，只需將會話的embedding與所有的item的embedding相乘，然後取softmax就得到了下次點擊item出現的概率。這也是訓練和推理階段相同的。如果你還不懂，不妨留個坑，待我看完ytb的詳解後再次解答。
其實也無需再說，在ytb中得到的user和item的embedding，爲何他們要相乘再softmax呢？這是一樣的道理，只不過這裏是會話的表達，你可以將會話表達看做是用戶的興趣/偏好表達。
拜拜，願我們終有重逢之時，而你還記得我們曾經討論的話題。