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79 lines
2.7 KiB
79 lines
2.7 KiB
# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle.trainer_config_helpers import *
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num_classes = 5
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x = data_layer(name="input1", size=3)
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y = data_layer(name="input2", size=5)
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z = out_prod_layer(input1=x, input2=y)
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x1 = fc_layer(input=x, size=5)
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y1 = fc_layer(input=y, size=5)
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z1 = mixed_layer(act=LinearActivation(),
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input=[conv_operator(img=x1,
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filter=y1,
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filter_size=1,
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num_filters=5,
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num_channel=5,
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stride=1)])
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y2 = fc_layer(input=y, size=15)
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cos1 = cos_sim(a=x1, b=y1)
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cos3 = cos_sim(a=x1, b=y2, size=3)
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linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3)
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out = fc_layer(input=[cos1, cos3, linear_comb, z, z1],
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size=num_classes,
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act=SoftmaxActivation())
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print_layer(input=[out])
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outputs(classification_cost(out, data_layer(name="label", size=num_classes)))
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dotmul = mixed_layer(input=[dotmul_operator(a=x1, b=x1),
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dotmul_projection(input=y1)])
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proj_with_attr_init = mixed_layer(input=full_matrix_projection(input=y1,
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param_attr=ParamAttr(learning_rate = 0,
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initial_mean = 0,
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initial_std = 0)),
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bias_attr = ParamAttr(initial_mean=0, initial_std=0, learning_rate=0),
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act = LinearActivation(),
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size = 5,
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name='proj_with_attr_init')
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# for ctc
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tmp = fc_layer(input=[x1, dotmul, proj_with_attr_init],
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size=num_classes + 1,
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act=SoftmaxActivation())
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ctc = ctc_layer(input=tmp,
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label=y,
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size=num_classes + 1)
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ctc_eval = ctc_error_evaluator(input=tmp, label=y)
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settings(
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batch_size=10,
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learning_rate=2e-3,
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learning_method=AdamOptimizer(),
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regularization=L2Regularization(8e-4),
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gradient_clipping_threshold=25
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)
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