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xuanjihe avatar xuanjihe commented on August 22, 2024 1

now i have updated the code, you can try again!

from speech-emotion-recognition.

xuanjihe avatar xuanjihe commented on August 22, 2024

print the shape of every tensorflow and you will find the issue!

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ramesh720 avatar ramesh720 commented on August 22, 2024

('valid_label==', (436, 4))
('valid_data==', (436, 300, 40, 3))
('layer1 shape', TensorShape([Dimension(None), Dimension(300), Dimension(40), Dimension(256)]))
('layer 2 shape', TensorShape([Dimension(None), Dimension(300), Dimension(10), Dimension(512)]))
(?, 300, 5, 512)
('layer2 shape', TensorShape([Dimension(None), Dimension(300), Dimension(5), Dimension(512)]))
('layer2 shape', TensorShape([Dimension(None), Dimension(200), Dimension(2560)]))
('layer2 shape', TensorShape([Dimension(None), Dimension(2560)]))
('linear1 shape', TensorShape([Dimension(None), Dimension(768)]))
('linear1 shape', TensorShape([Dimension(None), Dimension(768)]))
('linear1 shape', TensorShape([Dimension(None), Dimension(200), Dimension(768)]))
('outputs1 shape', (<tf.Tensor 'LSTM1/fw/fw/transpose:0' shape=(?, 200, 128) dtype=float32>, <tf.Tensor 'ReverseV2:0' shape=(?, 200, 128) dtype=float32>))
('outputs shape', TensorShape([Dimension(None), Dimension(200), Dimension(256)]))
('outputs shape', TensorShape([Dimension(None), Dimension(200), Dimension(256), Dimension(1)]))
('gru shape', TensorShape([Dimension(None), Dimension(256)]))
('fully1 shape', TensorShape([Dimension(None), Dimension(64)]))
('Ylogits shape', TensorShape([Dimension(None), Dimension(4)]))

i checked and i printed all shapes looking like shapes are good . but getting same error. why i am getting more loggits. many of them getting the same error as i get. can you please check sir. Thanking you sir

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ramesh720 avatar ramesh720 commented on August 22, 2024

thanking you sir for quick reply.
sir you need to change 2 things
load_validdata(FLAGS.traindata_path) from load_traindata(FLAGS.traindata_path) in model,py line 61
and
gru, alphas = attention(outputs1,1, return_alphas=True) from gru, alphas = attention(outputs1, return_alphas=True) in arcnn1.py line 150

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