test_cost_layers.py

#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
#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.
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),
    warp_ctc_layer(
        input=seq_in, label=labels, blank=0),
    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_regression_cost(
        input=seq_in, label=labels),
    huber_classification_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),
    nce_layer(
        input=hidden, label=labels))