deep_question_answering

Implementation of "Teaching Machines to Read and Comprehend" proposed by Google DeepMind
git clone https://esimon.eu/repos/deep_question_answering.git
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deep_bidir_lstm_2x128.py (843B)

      1 from blocks.algorithms import BasicMomentum, AdaDelta, RMSProp, Adam, CompositeRule, StepClipping
      2 from blocks.initialization import IsotropicGaussian, Constant
      3 from blocks.bricks import Tanh
      4 
      5 from model.deep_bidir_lstm import Model
      6 
      7 
      8 batch_size = 32
      9 sort_batch_count = 20
     10 
     11 shuffle_questions = True
     12 shuffle_entities = True
     13 
     14 concat_ctx_and_question = True
     15 concat_question_before = True		## should not matter for bidirectionnal network
     16 
     17 embed_size = 200
     18 
     19 lstm_size = [128, 128]
     20 skip_connections = True
     21 
     22 n_entities = 550
     23 out_mlp_hidden = []
     24 out_mlp_activations = []
     25 
     26 step_rule = CompositeRule([RMSProp(decay_rate=0.95, learning_rate=5e-5),
     27                            BasicMomentum(momentum=0.9)])
     28 
     29 dropout = 0.1
     30 w_noise = 0.05
     31 
     32 valid_freq = 1000
     33 save_freq = 1000
     34 print_freq = 100
     35 
     36 weights_init = IsotropicGaussian(0.01)
     37 biases_init = Constant(0.)