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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deepmind_attentive_reader.py (882B)

      1 from blocks.bricks import Tanh
      2 from blocks.algorithms import BasicMomentum, AdaDelta, RMSProp, Adam, CompositeRule, StepClipping, Momentum
      3 from blocks.initialization import IsotropicGaussian, Constant
      4 
      5 from model.attentive_reader import Model
      6 
      7 
      8 batch_size = 32
      9 sort_batch_count = 20
     10 
     11 shuffle_questions = True
     12 
     13 concat_ctx_and_question = False
     14 
     15 n_entities = 550
     16 embed_size = 200
     17 
     18 ctx_lstm_size = [256]
     19 ctx_skip_connections = True
     20 
     21 question_lstm_size = [256]
     22 question_skip_connections = True
     23 
     24 attention_mlp_hidden = [100]
     25 attention_mlp_activations = [Tanh()]
     26 
     27 out_mlp_hidden = []
     28 out_mlp_activations = []
     29 
     30 step_rule = CompositeRule([RMSProp(decay_rate=0.95, learning_rate=5e-5),
     31                            BasicMomentum(momentum=0.9)])
     32 
     33 dropout = 0.2
     34 w_noise = 0.
     35 
     36 valid_freq = 1000
     37 save_freq = 1000
     38 print_freq = 100
     39 
     40 weights_init = IsotropicGaussian(0.01)
     41 biases_init = Constant(0.)
     42