Jupyter kernel crashing when executing an RNN fitting
After loading and cleaning the dataset in the Jupyter notebook, I tried to run following code:
#Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
The output is:
Epoch 1/100
Any of the previous operation give me problems, including adding 4 LSTM layers, the output layer and compiling the RNN.
And then the kernel stops working and I can't go on.
Any suggestion or a way to understand what is going wrong?
Thanks!
python tensorflow machine-learning keras
edited Jan 19 at 13:55
Tiw
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
add a comment |
After loading and cleaning the dataset in the Jupyter notebook, I tried to run following code:
#Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
The output is:
Epoch 1/100
Any of the previous operation give me problems, including adding 4 LSTM layers, the output layer and compiling the RNN.
And then the kernel stops working and I can't go on.
Any suggestion or a way to understand what is going wrong?
Thanks!
python tensorflow machine-learning keras
edited Jan 19 at 13:55
Tiw
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
add a comment |
After loading and cleaning the dataset in the Jupyter notebook, I tried to run following code:
#Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
The output is:
Epoch 1/100
Any of the previous operation give me problems, including adding 4 LSTM layers, the output layer and compiling the RNN.
And then the kernel stops working and I can't go on.
Any suggestion or a way to understand what is going wrong?
Thanks!
python tensorflow machine-learning keras
edited Jan 19 at 13:55
Tiw
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
After loading and cleaning the dataset in the Jupyter notebook, I tried to run following code:
#Fitting the RNN to the Training set
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)
The output is:
Epoch 1/100
Any of the previous operation give me problems, including adding 4 LSTM layers, the output layer and compiling the RNN.
And then the kernel stops working and I can't go on.
Any suggestion or a way to understand what is going wrong?
Thanks!
python tensorflow machine-learning keras
python tensorflow machine-learning keras
edited Jan 19 at 13:55
Tiw
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
edited Jan 19 at 13:55
Tiw
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
edited Jan 19 at 13:55
Tiw
2,53121026
edited Jan 19 at 13:55
Tiw
2,53121026
edited Jan 19 at 13:55
Tiw
2,53121026
2,53121026
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
asked Jan 19 at 13:48
Ruben D'erricoRuben D'errico
13
13
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Ok, solved.
The dataset was too large for my local memory (around 3.5 million entries, lol).
I'd to split it in smaller chunks!
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
add a comment |
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Ok, solved.
The dataset was too large for my local memory (around 3.5 million entries, lol).
I'd to split it in smaller chunks!
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
add a comment |
Ok, solved.
The dataset was too large for my local memory (around 3.5 million entries, lol).
I'd to split it in smaller chunks!
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
add a comment |
Ok, solved.
The dataset was too large for my local memory (around 3.5 million entries, lol).
I'd to split it in smaller chunks!
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
Ok, solved.
The dataset was too large for my local memory (around 3.5 million entries, lol).
I'd to split it in smaller chunks!
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
answered Jan 19 at 14:11
Ruben D'erricoRuben D'errico
13
13
add a comment |
add a comment |
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