test_cost_layers.py

from paddle.trainer_config_helpers import *

settings(
    learning_rate=1e-4,
    batch_size=1000
)

seq_in = data_layer(name='input', size=200)
labels = data_layer(name='labels', size=5000)

probs = data_layer(name='probs', size=10)
xe_label = data_layer(name='xe-label', size=10)

hidden = fc_layer(input=seq_in, size=4)
outputs(ctc_layer(input=seq_in, label=labels),
        crf_layer(input=hidden,
                  label=data_layer(name='crf_label', size=4)),
        rank_cost(left=data_layer(name='left', size=1),
                  right=data_layer(name='right', size=1),
                  label=data_layer(name='label', size=1)),
        lambda_cost(input=data_layer(name='list_feature', size=100),
                    score=data_layer(name='list_scores', size=1)),
        cross_entropy(input=probs, label=xe_label),
        cross_entropy_with_selfnorm(input=probs, label=xe_label),
        huber_cost(input=data_layer(name='huber_probs', size=1),
                   label=data_layer(name='huber_label', size=1)),
        multi_binary_label_cross_entropy(input=probs, label=xe_label),
        sum_cost(input=hidden))