# Copyright (c) 2016 Baidu, Inc. All Rights Reserved
#
# 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 *

num_classes = 5

x = data_layer(name="input1", size=3)
y = data_layer(name="input2", size=5)

z = out_prod_layer(input1=x, input2=y)

x1 = fc_layer(input=x, size=5)
y1 = fc_layer(input=y, size=5)

z1 = mixed_layer(
    act=LinearActivation(),
    input=[
        conv_operator(
            img=x1,
            filter=y1,
            filter_size=1,
            num_filters=5,
            num_channels=5,
            stride=1)
    ])

assert z1.size > 0

y2 = fc_layer(input=y, size=15)

cos1 = cos_sim(a=x1, b=y1)
cos3 = cos_sim(a=x1, b=y2, size=3)

linear_comb = linear_comb_layer(weights=x1, vectors=y2, size=3)

out = fc_layer(
    input=[cos1, cos3, linear_comb, z, z1],
    size=num_classes,
    act=SoftmaxActivation())

print_layer(input=[out])

outputs(classification_cost(out, data_layer(name="label", size=num_classes)))

dotmul = mixed_layer(
    input=[dotmul_operator(
        a=x1, b=x1), dotmul_projection(input=y1)])

proj_with_attr_init = mixed_layer(
    input=full_matrix_projection(
        input=y1,
        param_attr=ParamAttr(
            learning_rate=0, initial_mean=0, initial_std=0)),
    bias_attr=ParamAttr(
        initial_mean=0, initial_std=0, learning_rate=0),
    act=LinearActivation(),
    size=5,
    name='proj_with_attr_init')

# for ctc
tmp = fc_layer(
    input=[x1, dotmul, proj_with_attr_init],
    size=num_classes + 1,
    act=SoftmaxActivation())
ctc = ctc_layer(input=tmp, label=y, size=num_classes + 1)
ctc_eval = ctc_error_evaluator(input=tmp, label=y)

settings(
    batch_size=10,
    learning_rate=2e-3,
    learning_method=AdamOptimizer(),
    regularization=L2Regularization(8e-4),
    gradient_clipping_threshold=25)
