Admittedly janky model.

model_store.py

def keras_avgpool_linear_pad_endtoend(word_map_emb_pair, pad_length,                          
                                      n_classes, random_seed, oov_thresh=0.9, embed_config={}, model_config={}):                                                                             
    # expect these in the order filter_embeddings returns them                                
    # nastyyy                                                                                 
    word_ind_map, embedding_mat = word_map_emb_pair                                           
    numpy.random.seed(random_seed)                                                            
                                                                                              
    inp = keras.layers.Input(shape=(1,), name='text', dtype='string')                         
                                                                                              
    # assume word_ind_map and embedding_mat has been fucked with accordingly                  
    pad_value = 0                                                                             
    oov_value = 1                                                                             
                                                                                              
    lookedup = ml_utils.TokenizeLookupLayer(word_ind_map, pad_length,                         
                                            pad_value=pad_value, oov_value=oov_value,         
                                            name='lookedup')(inp)                             
    # only makes sense if pad_value is 0                                                      
    lengths = ml_utils.CountNonZeroLayer(name='get_len')(lookedup)                            
                                                                                              
    oov_code = ml_utils.OOVCodeLayer(                                                         
        oov_value=oov_value,                                                                  
        oov_thresh=oov_thresh,                                                                
        name='oov_code'                                                                       
    )([lookedup, lengths])                                                                    
                                                                                              
    emb = keras.layers.Embedding(*(embedding_mat.shape), weights=[embedding_mat],             
                                 input_length=pad_length, name='embed',                       
                                 **embed_config)(lookedup)                                                       
    pooler = ml_utils.MaskedGlobalAveragePooling1D(name='avg')([emb, lengths])                
    model = keras.layers.core.Dense(n_classes, input_shape=(embedding_mat.shape[1],),         
                                    name='weights', **model_config)(pooler)                   
    softmax = keras.layers.core.Activation('softmax', name='softmax')(model)                  
    return keras.models.Model(inputs=[inp], outputs=[softmax, oov_code, pooler])