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SPConst.py
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SPConst.py
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CNNParams = {
'train_batch_size':16,
'train_epochs_num':75,
'train_base_learning_rate':0.00002,
'cnn_save_file':'SPBestCNN.h5',
'cnn_fc_hidden_layer_num':2,
'cnn_fc_hidden_layer_units_num':315,
'cnn_fc_dropout':0.5,
'cnn_filters_num':65,
'cnn_conv_num':3,
'cnn_last_layer_units_num':74
}
from keras.optimizers import *
RNNParams = {
'train_batch_size':128,
'train_epochs_num':1000,
'train_base_learning_rate':0.0001,
'optimizer':Adamax,
'rnn_save_file':'SPBestRNN.h5',
'rnn_window_size':8,
'rnn_embedding_output':75,
'rnn_last_activation':'sigmoid',
'rnn_use_context_state':True,
'rnn_last_use_bias':False,
'rnn_unit_num':200,
'rnn_last_dropout':0.50
}
EnsembleParams = {
'cnn_load_file':'SPBestCNN.h5',
'rnn_load_file':'SPBestRNN.h5',
'ensemble_save_file':'SPEnsemble.h5',
'train_batch_size':128,
'train_epochs_num':20,
'train_base_learning_rate':0.00005,
'bio_fc_hidden_layer_num':2,
'bio_fc_hidden_layer_units_num':150,
'bio_fc_dropout':0.05
}
FineTuning = {
'cnn_load_file':'SPBestCNN.h5',
'rnn_load_file':'SPBestRNN.h5',
'ensemble_load_file':'SPEnsemble.h5',
'ensemble_save_file':'SPFineTuning.h5',
'train_batch_size':128,
'train_epochs_num':100,
'train_base_learning_rate':0.00002,
'bio_fc_hidden_layer_num':2,
'bio_fc_hidden_layer_units_num':150,
'bio_fc_dropout':0.05
}
ParamsRanges = {
'CNNParams':
{
'cnn_fc_hidden_layer_units_num':[200,600],
'cnn_filters_num':[30,70],
'cnn_last_layer_units_num':[10,110]
},
'RNNParams':
{
'rnn_last_score_num':[100,400],
'rnn_embedding_output':[10,110],
'rnn_unit_num':[40,240]
}
}
Params = {
'data_file':'SPData.pkl',
'CNNParams':CNNParams,
'RNNParams':RNNParams,
'EnsembleParams':EnsembleParams,
'FineTuning':FineTuning,
'ParamsRanges':ParamsRanges
}