ADNC/scripts/config.yml

# Copyright 2018 Jörg Franke
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

#######################################
###       Global Configuration      ###
#######################################

global:
  batch_size:          &batch_size 32

#######################################
###     Training Configuration     ###
#######################################
training:
  epochs:               50          # epochs to train
  learn_rate:           0.00005     # learning reate for optimizer
  optimizer:            'rmsprop'   # optimizer [ rmsprop,, adam, momentum, adadelta, adagrad, sgd]
  optimizer_config:     {'momentum':0.9}  # config for optimizer [momentum, nesterov]
  gradient_clipping:    10          # gradient clipping value
  weight_decay:         False       # weight decay, False or float



#######################################
###        MANN Configuration       ###
#######################################
mann:
  name:                 'mann1'
  seed:                 245
  input_size:           0
  output_size:          0
  batch_size:           *batch_size
  input_embedding:      False
  architecture:         'uni' # bidirectional 172 384
  controller_config:    {"num_units":[128], "layer_norm":True, "activation":'tanh', 'cell_type':'clstm', 'connect':'sparse'}
  memory_unit_config:   {"cell_type":'cbmu', "memory_length":64, "memory_width":32, "read_heads":4, "write_heads": 2, "dnc_norm":True, "bypass_dropout":False, "wgate1":False}
  atop_rnn_config:      False # {"num_units":[32], "layer_norm":True, "activation":'tanh', 'cell_type':'clstm', 'connect':'sparse', 'attention':False}
  output_function:      "softmax"   # softmax tanh5 linear
  output_mask:          True
  loss_function:        'cross_entropy' # cross_entropy, mse
  bw_input_fw:          False


###################################################################
#######                     bAbI QA Task                     ######
###################################################################
babi_task:
  data_set:        'babi'

#  data_dir:       'data_babi/tasks_1-20_v1-2'
#  tmp_dir:        'data_dir'

  seed:           876
  valid_ratio:    0.1           # like nature paper
  batch_size:     *batch_size
  max_len:        1000

  set_type:       ['en-10k']    # ['hn-10k', 'en-10k', 'shuffled-10k']
  task_selection: ['1', '2', '12'] # list of number (1-20) or 'all'
  augment16:      False         # augmentation of task 16

  num_chached:    5             # number of cached samples
  threads:        1             # number of parallel threads



##################################################################
######                     Copy Task                        ######
##################################################################
copy_task:
  data_set:       'copy_task'

  seed:           125
  batch_size:     *batch_size

  set_list:
    train:
        quantity:       6000    # quantity of the training set
        min_length:     20      # min length of the training sample
        max_length:     50      # max length of a training sample
    valid:
        quantity:       600     # quantity of the validation set
        min_length:     50
        max_length:     100
#    test:
#        quantity:       100
#        min_length:     10
#        max_length:     10

  feature_width:  100           # width of the feature vector

  num_chached:    10            # number of cached samples
  threads:        1             # number of parallel threads