optparser.add_option('--n-hidden', None, dest='n_hidden', default=3, help='num hidden nodes')
    optparser.add_option('--l1-reg', None, dest='l1_reg', default=0, help='L1 regression weight')
    optparser.add_option('--l2-reg', None, dest='l2_reg', default=0, help='L2 regression weight')
    optparser.add_option('--batch-size', None, dest='batch_size', default=100, help='mini batch size')
    optparser.add_option('--epochs', None, dest='n_epochs', default=10000, help='epochs to run')
    optparser.add_option('--learning-rate', None, dest='learning_rate', default=0.01, help='weight update learning rate')
    optparser.add_option('--seed', None, dest='seed', default=92374652, help='rng seed')
    optparser.add_option('--weights-file', None, dest='weights_file', default=None, help='pickle model params from previous run')
    optparser.add_option('--print-internal-vars', None, action="store_true", dest='print_internal_vars', default=False, help='whether to wrap selected symbolic nodes in a theano.print')
    optparser.add_option('--dump-hidden-weights', None, action="store_true", dest='dump_hidden', default=False, help='whether to write projection/hidden/softmax layer weights out to a file')
    options, arguments = optparser.parse_args()
    print "options", options

    # load data
    from load_data import load_trigram_data
    vocab_size, input_feature_names, feature_input_indexes, datasets = load_trigram_data(options.trigrams)
    print "vocab_size", vocab_size
    print "input_feature_names", input_feature_names
    print "feature_input_indexes", feature_input_indexes
    print "datasets", datasets

    # run
    run_nplm(datasets=datasets, 
             n_in=2,  # use bigram to pick next token
             vocab_size=vocab_size, projection_dim=int(options.n_projection), 
             n_hidden=int(options.n_hidden),
             input_feature_names=input_feature_names,
             feature_input_indexes=feature_input_indexes,
             L1_reg=float(options.l1_reg), L2_reg=float(options.l2_reg),
             batch_size=int(options.batch_size), n_epochs=int(options.n_epochs),
             learning_rate=float(options.learning_rate),
Пример #2
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# create output dir if requested (and write options to a file)
if opts.output_dir is not None:
    if not os.path.exists(opts.output_dir):
        os.makedirs(opts.output_dir)
    with open(opts.output_dir + "/options.json", 'w') as f:
        f.write(str(opts))

# create some timing gates for dumping checkpoints and costs
checkpointer_gate = FrequencyGate(opts.checkpoint_freq) if opts.checkpoint_freq else None
dump_cost_gate = FrequencyGate(opts.cost_progress_freq)

# slurp in training data, converting from "C A B" to idx "0 1 2" and storing in idxs
# idxs => (w1, w2, w3) for lr; (w1, w2) for sm
# label y => 1.0 or 0.0 for lr; w3 for sm
idxs, y, token_idx = load_trigram_data(opts.trigrams_file, opts.mode)
if opts.output_dir is not None:
   token_idx.write_to_file(opts.output_dir + "/vocab.tsv")
VOCAB_SIZE = token_idx.seq

# decide batching sizes
BATCH_SIZE = opts.batch_size
NUM_BATCHES = int(math.ceil(float(len(idxs)) / BATCH_SIZE))
print("#egs", len(idxs), "batch_size", BATCH_SIZE, "=> num_batches", NUM_BATCHES, file=sys.stderr)
EPOCHS = opts.epochs
LAMBDA1, LAMBDA2 = opts.lambda1, opts.lambda2

NUM_EMBEDDING_NODES = 3 if opts.mode=='lr' else 2
NUM_OUTPUT_NODES = 1 if opts.mode=='lr' else VOCAB_SIZE

# decide if we're going to dump cost progress
Пример #3
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# create output dir if requested (and write options to a file)
if opts.output_dir is not None:
    if not os.path.exists(opts.output_dir):
        os.makedirs(opts.output_dir)
    with open(opts.output_dir + "/options.json", 'w') as f:
        f.write(str(opts))

# create some timing gates for dumping checkpoints and costs
checkpointer_gate = FrequencyGate(
    opts.checkpoint_freq) if opts.checkpoint_freq else None
dump_cost_gate = FrequencyGate(opts.cost_progress_freq)

# slurp in training data, converting from "C A B" to idx "0 1 2" and storing in idxs
# idxs => (w1, w2, w3) for lr; (w1, w2) for sm
# label y => 1.0 or 0.0 for lr; w3 for sm
idxs, y, token_idx = load_trigram_data(opts.trigrams_file, opts.mode)
if opts.output_dir is not None:
    token_idx.write_to_file(opts.output_dir + "/vocab.tsv")
VOCAB_SIZE = token_idx.seq

# decide batching sizes
BATCH_SIZE = opts.batch_size
NUM_BATCHES = int(math.ceil(float(len(idxs)) / BATCH_SIZE))
print("#egs",
      len(idxs),
      "batch_size",
      BATCH_SIZE,
      "=> num_batches",
      NUM_BATCHES,
      file=sys.stderr)
EPOCHS = opts.epochs