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MemoryError when running train.py #22

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7oranger opened this issue Feb 20, 2017 · 3 comments
Open

MemoryError when running train.py #22

7oranger opened this issue Feb 20, 2017 · 3 comments

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@7oranger
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7oranger commented Feb 20, 2017

G:\Anaconda2\lib\site-packages\gensim\utils.py:840: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
G:\Anaconda2\lib\site-packages\gensim\utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.
warnings.warn("Pattern library is not installed, lemmatization won't be available.")
INFO:summa.preprocessing.cleaner:'pattern' package not found; tag filters are not available for English
Using Theano backend.
INFO:lib.dialog_processor:Loading corpus data...
INFO:lib.dialog_processor:H:/EclipseWorkspace/NetFault_Analysis/keras-seq2seq/debug_seq2seq-master/corpora_processed\movie_lines_cleaned_m1.txt and H:/EclipseWorkspace/NetFault_Analysis/keras-seq2seq/debug_seq2seq-master/words_index\w_idx_movie_lines_cleaned_m1.txt exist, loading files from disk
INFO:main:-----
INFO:lib.w2v_model.w2v:Loading model from H:/EclipseWorkspace/NetFault_Analysis/keras-seq2seq/debug_seq2seq-master/w2v_models\movie_lines_cleaned_w5_m1_v128.bin
INFO:gensim.utils:loading Word2Vec object from H:/EclipseWorkspace/NetFault_Analysis/keras-seq2seq/debug_seq2seq-master/w2v_models\movie_lines_cleaned_w5_m1_v128.bin
INFO:gensim.utils:setting ignored attribute syn0norm to None
INFO:gensim.utils:setting ignored attribute cum_table to None
INFO:gensim.utils:loaded H:/EclipseWorkspace/NetFault_Analysis/keras-seq2seq/debug_seq2seq-master/w2v_models\movie_lines_cleaned_w5_m1_v128.bin
INFO:lib.w2v_model.w2v:Model "movie_lines_cleaned_w5_m1_v128.bin" has been loaded.
INFO:main:-----
INFO:lib.nn_model.model:Initializing NN model with the following params:
INFO:lib.nn_model.model:Input dimension: 128 (token vector size)
INFO:lib.nn_model.model:Hidden dimension: 128
INFO:lib.nn_model.model:Output dimension: 20001 (token dict size)
INFO:lib.nn_model.model:Input seq length: 16
INFO:lib.nn_model.model:Output seq length: 6
INFO:lib.nn_model.model:Batch size: 32
G:\Anaconda2\lib\site-packages\keras\engine\topology.py:379: UserWarning: The regularizers property of layers/models is deprecated. Regularization losses are now managed via the losses layer/model property.
warnings.warn('The regularizers property of layers/models '
Traceback (most recent call last):
File "H:\EclipseWorkspace\NetFault_Analysis\keras-seq2seq\debug_seq2seq-master\bin\train.py", line 37, in
learn()
File "H:\EclipseWorkspace\NetFault_Analysis\keras-seq2seq\debug_seq2seq-master\bin\train.py", line 30, in learn
nn_model = get_nn_model(token_dict_size=len(index_to_token))
File "H:\EclipseWorkspace\NetFault_Analysis\keras-seq2seq\debug_seq2seq-master\lib\nn_model\model.py", line 30, in get_nn_model
depth=1
File "build\bdist.win-amd64\egg\seq2seq\models.py", line 73, in SimpleSeq2Seq
File "build\bdist.win-amd64\egg\recurrentshop\engine.py", line 198, in add
File "G:\Anaconda2\lib\site-packages\keras\models.py", line 332, in add
output_tensor = layer(self.outputs[0])
File "G:\Anaconda2\lib\site-packages\keras\engine\topology.py", line 546, in call
self.build(input_shapes[0])
File "build\bdist.win-amd64\egg\recurrentshop\cells.py", line 121, in build
File "build\bdist.win-amd64\egg\recurrentshop\engine.py", line 83, in init
File "G:\Anaconda2\lib\site-packages\keras\initializations.py", line 95, in orthogonal
a = np.random.normal(0.0, 1.0, flat_shape)
File "mtrand.pyx", line 1636, in mtrand.RandomState.normal (numpy\random\mtrand\mtrand.c:20676)
File "mtrand.pyx", line 242, in mtrand.cont2_array_sc (numpy\random\mtrand\mtrand.c:7401)
MemoryError

I'm using anaconda2(python2 win 64 bit cpu only) and my colleague is using ubuntu with GPU (python 2). Does anybody meet this problem? How can I solve this? Many thanks.

@7633
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7633 commented Jul 26, 2017

I suppose you should use python 3, because this code and seq2seq use python 3.

@shluoyujun
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hi, did you solve this problem?

@hieuthehungry
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Did anyone solve this problem?

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