Hi,Kim.Thank you for sharing your code for us. I am using the same code and corpus with you and Runing the models in cpu , the command is THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec ,but the val perf always stay with 75%, cannot be reached the 80% or even more. so I want to know what factors can make this result ?
model's result:
loading data... data loaded!
model architecture: CNN-non-static
using: word2vec vectors
[('image shape', 64, 300), ('filter shape', [(100, 1, 3, 300), (100, 1, 4, 300), (100, 1, 5, 300)]), ('hidden_units', [100, 2]), ('dropout', [0.5]), ('batch_size', 50), ('non_static', True), ('learn_decay', 0.95), ('conv_non_linear', 'relu'), ('non_static', True), ('sqr_norm_lim', 9), ('shuffle_batch', True)]
... training
epoch: 1, training time: 336.80 secs, train perf: 54.66 %, val perf: 54.95 %
epoch: 2, training time: 336.82 secs, train perf: 65.70 %, val perf: 65.79 %
epoch: 3, training time: 336.72 secs, train perf: 66.45 %, val perf: 63.26 %
epoch: 4, training time: 336.60 secs, train perf: 72.57 %, val perf: 68.21 %
epoch: 5, training time: 336.53 secs, train perf: 74.38 %, val perf: 68.74 %
Hi,Kim.Thank you for sharing your code for us. I am using the same code and corpus with you and Runing the models in cpu , the command is THEANO_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32 python conv_net_sentence.py -nonstatic -word2vec ,but the val perf always stay with 75%, cannot be reached the 80% or even more. so I want to know what factors can make this result ?
model's result: loading data... data loaded! model architecture: CNN-non-static using: word2vec vectors [('image shape', 64, 300), ('filter shape', [(100, 1, 3, 300), (100, 1, 4, 300), (100, 1, 5, 300)]), ('hidden_units', [100, 2]), ('dropout', [0.5]), ('batch_size', 50), ('non_static', True), ('learn_decay', 0.95), ('conv_non_linear', 'relu'), ('non_static', True), ('sqr_norm_lim', 9), ('shuffle_batch', True)] ... training epoch: 1, training time: 336.80 secs, train perf: 54.66 %, val perf: 54.95 % epoch: 2, training time: 336.82 secs, train perf: 65.70 %, val perf: 65.79 % epoch: 3, training time: 336.72 secs, train perf: 66.45 %, val perf: 63.26 % epoch: 4, training time: 336.60 secs, train perf: 72.57 %, val perf: 68.21 % epoch: 5, training time: 336.53 secs, train perf: 74.38 %, val perf: 68.74 %