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View Code? Open in Web Editor NEWDeep Embedded Clustering with Data Augmentation (DEC-DA). Performance on MNIST (acc=0.985, nmi=0.960).
License: MIT License
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ValueError: Input contains NaN, infinity or a value too large for dtype('float32')
During training the model using custom dataset, loss will occur NaN.
I tried decrease the lr but the error still occurs.
The logging info of error is as follows.
Epoch 50/500
400/400 [==============================] - 0s 272us/step - loss: nan
Epoch 51/500
256/400 [==================>...........] -
ETA: 0s - loss: nanTraceback (most recent call last):
File ".\demo.py", line 148, in <module>
train(args)
File ".\demo.py", line 69, in train
save_dir=args.save_dir, verbose=args.verbose, aug_pretrain=args.aug_pretrain)
File "E:\Comparison_Experiments\DEC-DA\FcDEC.py", line 171, in pretrain
self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs, callbacks=cb, verbose=verbose)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1639, in fit
validation_steps=validation_steps)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 239, in fit_loop
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\callbacks.py", line 214, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "E:\Comparison_Experiments\DEC-DA\FcDEC.py", line 162, in on_epoch_end
y_pred = km.fit_predict(features)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 917, in fit_predict
return self.fit(X).labels_
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 887, in fit
X = self._check_fit_data(X)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 858, in _check_fit_data
X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32])
File "C:\Software\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 453, in check_array
_assert_all_finite(array)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 44, in _assert_all_finite
" or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
Epoch 50/500
400/400 [==============================] - 0s 272us/step - loss: nan
Epoch 51/500
256/400 [==================>...........] - ETA: 0s - loss: nanTraceback (most recent call last):
File ".\demo.py", line 148, in <module>
train(args)
File ".\demo.py", line 69, in train
save_dir=args.save_dir, verbose=args.verbose, aug_pretrain=args.aug_pretrain)
File "E:\Comparison_Experiments\DEC-DA\FcDEC.py", line 171, in pretrain
self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs, callbacks=cb, verbose=verbose)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py", line 1639, in fit
validation_steps=validation_steps)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training_arrays.py", line 239, in fit_loop
callbacks.on_epoch_end(epoch, epoch_logs)
File "C:\Software\Anaconda3\lib\site-packages\tensorflow\python\keras\callbacks.py", line 214, in on_epoch_end
callback.on_epoch_end(epoch, logs)
File "E:\Comparison_Experiments\DEC-DA\FcDEC.py", line 162, in on_epoch_end
y_pred = km.fit_predict(features)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 917, in fit_predict
return self.fit(X).labels_
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 887, in fit
X = self._check_fit_data(X)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\cluster\k_means_.py", line 858, in _check_fit_data
X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32])
File "C:\Software\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 453, in check_array
_assert_all_finite(array)
File "C:\Software\Anaconda3\lib\site-packages\sklearn\utils\validation.py", line 44, in _assert_all_finite
" or a value too large for %r." % X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').
Can anybody know what's wrong of the setting in this code?
Loss is nan, When I do not provide actually label
Hello
I tried to train on my own dataset. It is working fine, when I provide the actual (y). When y is None. its loss is nan.
So please fix it or tell me, where I am doing wrong?
Thanks
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