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I tried to launch this project vie TF 1.8, but found that rnn_cell._linear attribute is unavailable. So i googled until found the advice to replace this class method with tf.contrib.fully_connected layer. Then the next problem arose: how to transform arguments, because fully_connected layer has 14 and there are 4 args w/o keywirds in the code.
I think i succeeded in it, however, I was not able to map the last argument from phi_hs2d = tanh(rnn_cell._linear(hs2d, num_units, True, 1.0)) to any argument in fully_connected init. I suppose that True in the line above means trainable=True
Next problem is the shape mismatch: ValueError: Shapes must be equal rank, but are 2 and 3 for 'NLC/Decoder/DecoderAttnCell/DecoderAttnCell/while/Select' (op: 'Select') with input shapes: [?], [?,400], [2,?,400]. in nlc_model.py:166, so probably the model either can't be ported straightforwardly to the newest version of TF or has the size mismatch error inside it.
Could you help, e. g. provide complete environment where the model can be trained?
The text was updated successfully, but these errors were encountered:
I tried to launch this project vie TF 1.8, but found that
rnn_cell._linear
attribute is unavailable. So i googled until found the advice to replace this class method withtf.contrib.fully_connected
layer. Then the next problem arose: how to transform arguments, becausefully_connected
layer has 14 and there are 4 args w/o keywirds in the code.I think i succeeded in it, however, I was not able to map the last argument from
phi_hs2d = tanh(rnn_cell._linear(hs2d, num_units, True, 1.0))
to any argument in fully_connected init. I suppose that True in the line above meanstrainable=True
Next problem is the shape mismatch:
ValueError: Shapes must be equal rank, but are 2 and 3 for 'NLC/Decoder/DecoderAttnCell/DecoderAttnCell/while/Select' (op: 'Select') with input shapes: [?], [?,400], [2,?,400].
in nlc_model.py:166, so probably the model either can't be ported straightforwardly to the newest version of TF or has the size mismatch error inside it.Could you help, e. g. provide complete environment where the model can be trained?
The text was updated successfully, but these errors were encountered: