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PaddleDetection
提交 c64cd6fe 编写于 作者: W wenboyang 提交者： Yu Yang
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Use diff to compare config unittest (#363) 

Fix #342
上级 93e4d0cc
隐藏空白更改
内联 并排
Showing with 4371 addition and 25 deletion
python/paddle/trainer_config_helpers/tests/configs/check.md5 已删除 100644 → 0
浏览文件 @ 93e4d0cc
86c0815275a9d5eb902e23c6a592f58a img_layers.protostr
a5d9259ff1fd7ca23d0ef090052cb1f2 last_first_seq.protostr
9c038249ec8ff719753a746cdb04c026 layer_activations.protostr
5913f87b39cee3b2701fa158270aca26 projections.protostr
7334ba0a4544f0623231330fc51d390d shared_fc.protostr
8b8b6bb128a7dfcc937be86145f53e2f shared_lstm.protostr
6b39e34beea8dfb782bee9bd3dea9eb5 simple_rnn_layers.protostr
4e78f0ded79f6fefb58ca0c104b57c79 test_bi_grumemory.protostr
0fc1409600f1a3301da994ab9d28b0bf test_cost_layers.protostr
6cd5f28a3416344f20120698470e0a4c test_cost_layers_with_weight.protostr
144bc6d3a509de74115fa623741797ed test_expand_layer.protostr
2378518bdb71e8c6e888b1842923df58 test_fc.protostr
8bb44e1e5072d0c261572307e7672bda test_grumemory_layer.protostr
1f3510672dce7a9ed25317fc58579ac7 test_hsigmoid.protostr
d350bd91a0dc13e854b1364c3d9339c6 test_lstmemory_layer.protostr
5433ed33d4e7414eaf658f2a55946186 test_maxout.protostr
251a948ba41c1071afcd3d9cf9c233f7 test_ntm_layers.protostr
e6ff04e70aea27c7b06d808cc49c9497 test_print_layer.protostr
2a75dd33b640c49a8821c2da6e574577 test_rnn_group.protostr
67d6fde3afb54f389d0ce4ff14726fe1 test_sequence_pooling.protostr
f586a548ef4350ba1ed47a81859a64cb unused_layers.protostr
8122477f4f65244580cec09edc590041 util_layers.protostr
dcd76bebb5f9c755f481c26192917818 math_ops.protostr
python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh
浏览文件 @ c64cd6fe
......@@ -4,6 +4,8 @@ set -e
cd `dirname $0`
export PYTHONPATH=$PWD/../../../../
protostr=$PWD/protostr
configs=(test_fc layer_activations projections test_print_layer
test_sequence_pooling test_lstmemory_layer test_grumemory_layer
last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
......@@ -15,5 +17,5 @@ test_maxout test_bi_grumemory math_ops)
for conf in ${configs[*]}
do
echo "Generating " $conf
python -m paddle.utils.dump_config $conf.py > $conf.protostr
python -m paddle.utils.dump_config $conf.py > $protostr/$conf.protostr.unitest
done
python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "image"
type: "data"
size: 65536
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 3297856
active_type: ""
inputs {
input_layer_name: "image"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 32
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 1
output_x: 227
img_size: 256
caffe_mode: true
filter_size_y: 32
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 64
shared_biases: true
}
layers {
name: "__batch_norm_0__"
type: "batch_norm"
size: 3297856
active_type: "relu"
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w0"
image_conf {
channels: 64
img_size: 227
}
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w1"
}
inputs {
input_layer_name: "__conv_0__"
input_parameter_name: "___batch_norm_0__.w2"
}
bias_parameter_name: "___batch_norm_0__.wbias"
moving_average_fraction: 0.9
}
layers {
name: "__crmnorm_0__"
type: "norm"
size: 3297856
active_type: ""
inputs {
input_layer_name: "__batch_norm_0__"
norm_conf {
norm_type: "cmrnorm-projection"
channels: 64
size: 32
scale: 0.0004
pow: 0.75
output_x: 227
img_size: 227
blocked: false
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 2458624
active_type: ""
inputs {
input_layer_name: "__conv_0__"
pool_conf {
pool_type: "max-projection"
channels: 64
size_x: 32
stride: 1
output_x: 196
img_size: 227
padding: 0
size_y: 32
stride_y: 1
output_y: 196
img_size_y: 227
padding_y: 0
}
}
}
parameters {
name: "___conv_0__.w0"
size: 65536
initial_mean: 0.0
initial_std: 0.0441941738242
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 64
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w0"
size: 64
initial_mean: 1.0
initial_std: 0.0
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___batch_norm_0__.w1"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.w2"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
is_static: true
is_shared: true
}
parameters {
name: "___batch_norm_0__.wbias"
size: 64
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 64
initial_strategy: 0
initial_smart: false
}
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
sub_models {
name: "root"
layer_names: "image"
layer_names: "__conv_0__"
layer_names: "__batch_norm_0__"
layer_names: "__crmnorm_0__"
layer_names: "__pool_0__"
input_layer_names: "image"
output_layer_names: "__pool_0__"
output_layer_names: "__crmnorm_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/last_first_seq.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 30
active_type: ""
}
layers {
name: "__first_seq_0__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "seq"
}
layers {
name: "__first_seq_1__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 30
active_type: "linear"
inputs {
input_layer_name: "data"
}
trans_type: "non-seq"
}
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__first_seq_0__"
layer_names: "__first_seq_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
input_layer_names: "data"
output_layer_names: "__first_seq_0__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_0__"
output_layer_names: "__last_seq_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/layer_activations.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "layer_0"
type: "fc"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_0.w0"
}
bias_parameter_name: "_layer_0.wbias"
}
layers {
name: "layer_1"
type: "fc"
size: 100
active_type: "sigmoid"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_1.w0"
}
bias_parameter_name: "_layer_1.wbias"
}
layers {
name: "layer_2"
type: "fc"
size: 100
active_type: "softmax"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_2.w0"
}
bias_parameter_name: "_layer_2.wbias"
}
layers {
name: "layer_3"
type: "fc"
size: 100
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_3.w0"
}
bias_parameter_name: "_layer_3.wbias"
}
layers {
name: "layer_4"
type: "fc"
size: 100
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_4.w0"
}
bias_parameter_name: "_layer_4.wbias"
}
layers {
name: "layer_5"
type: "fc"
size: 100
active_type: "exponential"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_5.w0"
}
bias_parameter_name: "_layer_5.wbias"
}
layers {
name: "layer_6"
type: "fc"
size: 100
active_type: "relu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_6.w0"
}
bias_parameter_name: "_layer_6.wbias"
}
layers {
name: "layer_7"
type: "fc"
size: 100
active_type: "brelu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_7.w0"
}
bias_parameter_name: "_layer_7.wbias"
}
layers {
name: "layer_8"
type: "fc"
size: 100
active_type: "softrelu"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_8.w0"
}
bias_parameter_name: "_layer_8.wbias"
}
layers {
name: "layer_9"
type: "fc"
size: 100
active_type: "stanh"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_9.w0"
}
bias_parameter_name: "_layer_9.wbias"
}
layers {
name: "layer_10"
type: "fc"
size: 100
active_type: "abs"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_10.w0"
}
bias_parameter_name: "_layer_10.wbias"
}
layers {
name: "layer_11"
type: "fc"
size: 100
active_type: "square"
inputs {
input_layer_name: "input"
input_parameter_name: "_layer_11.w0"
}
bias_parameter_name: "_layer_11.wbias"
}
parameters {
name: "_layer_0.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_0.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_1.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_1.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_2.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_2.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_3.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_3.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_4.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_4.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_5.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_5.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_6.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_6.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_7.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_7.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_8.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_8.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_9.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_9.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_10.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_10.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_layer_11.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_layer_11.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
input_layer_names: "input"
output_layer_names: "layer_0"
output_layer_names: "layer_1"
output_layer_names: "layer_2"
output_layer_names: "layer_3"
output_layer_names: "layer_4"
output_layer_names: "layer_5"
output_layer_names: "layer_6"
output_layer_names: "layer_7"
output_layer_names: "layer_8"
output_layer_names: "layer_9"
output_layer_names: "layer_10"
output_layer_names: "layer_11"
sub_models {
name: "root"
layer_names: "input"
layer_names: "layer_0"
layer_names: "layer_1"
layer_names: "layer_2"
layer_names: "layer_3"
layer_names: "layer_4"
layer_names: "layer_5"
layer_names: "layer_6"
layer_names: "layer_7"
layer_names: "layer_8"
layer_names: "layer_9"
layer_names: "layer_10"
layer_names: "layer_11"
input_layer_names: "input"
output_layer_names: "layer_0"
output_layer_names: "layer_1"
output_layer_names: "layer_2"
output_layer_names: "layer_3"
output_layer_names: "layer_4"
output_layer_names: "layer_5"
output_layer_names: "layer_6"
output_layer_names: "layer_7"
output_layer_names: "layer_8"
output_layer_names: "layer_9"
output_layer_names: "layer_10"
output_layer_names: "layer_11"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/math_ops.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__exp_0__"
type: "mixed"
size: 100
active_type: "exponential"
inputs {
input_layer_name: "data"
proj_conf {
type: "identity"
name: "___exp_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__log_0__"
type: "mixed"
size: 100
active_type: "log"
inputs {
input_layer_name: "__exp_0__"
proj_conf {
type: "identity"
name: "___log_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__abs_0__"
type: "mixed"
size: 100
active_type: "abs"
inputs {
input_layer_name: "__log_0__"
proj_conf {
type: "identity"
name: "___abs_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__sigmoid_0__"
type: "mixed"
size: 100
active_type: "sigmoid"
inputs {
input_layer_name: "__abs_0__"
proj_conf {
type: "identity"
name: "___sigmoid_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__square_0__"
type: "mixed"
size: 100
active_type: "square"
inputs {
input_layer_name: "__sigmoid_0__"
proj_conf {
type: "identity"
name: "___square_0__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__square_1__"
type: "mixed"
size: 100
active_type: "square"
inputs {
input_layer_name: "__square_0__"
proj_conf {
type: "identity"
name: "___square_1__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_0__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
}
slope: 1.0
intercept: 1
}
layers {
name: "__slope_intercept_layer_1__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_0__"
}
slope: 1.0
intercept: 1
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
proj_conf {
type: "identity"
name: "___mixed_0__.w0"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__slope_intercept_layer_1__"
proj_conf {
type: "identity"
name: "___mixed_0__.w1"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_2__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__square_1__"
}
slope: -1.0
intercept: 0.0
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_0__"
proj_conf {
type: "identity"
name: "___mixed_1__.w0"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__slope_intercept_layer_2__"
proj_conf {
type: "identity"
name: "___mixed_1__.w1"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__slope_intercept_layer_3__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_1__"
}
slope: 1.0
intercept: 2
}
layers {
name: "__slope_intercept_layer_4__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_3__"
}
slope: -1.0
intercept: 0.0
}
layers {
name: "__slope_intercept_layer_5__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "__slope_intercept_layer_4__"
}
slope: 1.0
intercept: 2
}
input_layer_names: "data"
output_layer_names: "__slope_intercept_layer_5__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__exp_0__"
layer_names: "__log_0__"
layer_names: "__abs_0__"
layer_names: "__sigmoid_0__"
layer_names: "__square_0__"
layer_names: "__square_1__"
layer_names: "__slope_intercept_layer_0__"
layer_names: "__slope_intercept_layer_1__"
layer_names: "__mixed_0__"
layer_names: "__slope_intercept_layer_2__"
layer_names: "__mixed_1__"
layer_names: "__slope_intercept_layer_3__"
layer_names: "__slope_intercept_layer_4__"
layer_names: "__slope_intercept_layer_5__"
input_layer_names: "data"
output_layer_names: "__slope_intercept_layer_5__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/projections.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "test"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__embedding_0__"
type: "mixed"
size: 256
active_type: ""
inputs {
input_layer_name: "test"
input_parameter_name: "___embedding_0__.w0"
proj_conf {
type: "table"
name: "___embedding_0__.w0"
input_size: 100
output_size: 256
}
}
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__embedding_0__"
input_parameter_name: "___mixed_0__.w0"
proj_conf {
type: "fc"
name: "___mixed_0__.w0"
input_size: 256
output_size: 100
}
}
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_0__"
input_parameter_name: "___mixed_1__.w0"
proj_conf {
type: "table"
name: "___mixed_1__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_2__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_1__"
proj_conf {
type: "identity"
name: "___mixed_2__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_3__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_2__"
input_parameter_name: "___mixed_3__.w0"
proj_conf {
type: "dot_mul"
name: "___mixed_3__.w0"
input_size: 100
output_size: 100
}
}
}
layers {
name: "__mixed_4__"
type: "mixed"
size: 300
active_type: ""
inputs {
input_layer_name: "__mixed_3__"
input_parameter_name: "___mixed_4__.w0"
proj_conf {
type: "context"
name: "___mixed_4__.w0"
input_size: 100
output_size: 300
context_start: -1
context_length: 3
trainable_padding: true
}
}
}
layers {
name: "__mixed_5__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_2__"
}
inputs {
input_layer_name: "__mixed_3__"
}
operator_confs {
type: "dot_mul"
input_indices: 0
input_indices: 1
input_sizes: 100
input_sizes: 100
output_size: 100
dotmul_scale: 1
}
}
layers {
name: "img"
type: "data"
size: 1024
active_type: ""
}
layers {
name: "filter"
type: "data"
size: 576
active_type: ""
}
layers {
name: "__mixed_6__"
type: "mixed"
size: 57600
active_type: ""
inputs {
input_layer_name: "img"
}
inputs {
input_layer_name: "filter"
}
operator_confs {
type: "conv"
input_indices: 0
input_indices: 1
input_sizes: 1024
input_sizes: 576
output_size: 57600
conv_conf {
filter_size: 3
channels: 1
stride: 1
padding: 0
groups: 1
filter_channels: 1
output_x: 30
img_size: 32
caffe_mode: true
filter_size_y: 3
padding_y: 0
stride_y: 1
}
num_filters: 64
}
}
layers {
name: "__mixed_7__"
type: "mixed"
size: 100
active_type: ""
inputs {
input_layer_name: "__mixed_4__"
input_parameter_name: "___mixed_7__.w0"
proj_conf {
type: "fc"
name: "___mixed_7__.w0"
input_size: 300
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_5__"
input_parameter_name: "___mixed_7__.w1"
proj_conf {
type: "trans_fc"
name: "___mixed_7__.w1"
input_size: 100
output_size: 100
}
}
inputs {
input_layer_name: "__mixed_6__"
input_parameter_name: "___mixed_7__.w2"
proj_conf {
type: "fc"
name: "___mixed_7__.w2"
input_size: 57600
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}
}
drop_rate: 0.5
}
parameters {
name: "___embedding_0__.w0"
size: 25600
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initial_std: 0.1
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initial_strategy: 0
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}
parameters {
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size: 25600
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initial_strategy: 0
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}
parameters {
name: "___mixed_1__.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
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initial_strategy: 0
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}
parameters {
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size: 100
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}
parameters {
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size: 200
initial_mean: 0.0
initial_std: 0.0
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initial_strategy: 0
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}
parameters {
name: "___mixed_7__.w0"
size: 30000
initial_mean: 0.0
initial_std: 0.057735026919
dims: 300
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_7__.w1"
size: 10000
initial_mean: 0.0
initial_std: 0.1
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initial_strategy: 0
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}
parameters {
name: "___mixed_7__.w2"
size: 5760000
initial_mean: 0.0
initial_std: 0.00416666666667
dims: 57600
dims: 100
initial_strategy: 0
initial_smart: true
}
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
sub_models {
name: "root"
layer_names: "test"
layer_names: "__embedding_0__"
layer_names: "__mixed_0__"
layer_names: "__mixed_1__"
layer_names: "__mixed_2__"
layer_names: "__mixed_3__"
layer_names: "__mixed_4__"
layer_names: "__mixed_5__"
layer_names: "img"
layer_names: "filter"
layer_names: "__mixed_6__"
layer_names: "__mixed_7__"
input_layer_names: "test"
input_layer_names: "img"
input_layer_names: "filter"
output_layer_names: "__mixed_7__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_fc.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "feature_a"
type: "data"
size: 200
active_type: ""
}
layers {
name: "feature_b"
type: "data"
size: 200
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "feature_a"
input_parameter_name: "fc_param"
}
bias_parameter_name: "bias_param"
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "feature_b"
input_parameter_name: "fc_param"
}
bias_parameter_name: "bias_param"
}
layers {
name: "__fc_layer_2__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "softmax_param"
}
inputs {
input_layer_name: "__fc_layer_1__"
input_parameter_name: "softmax_param"
}
}
layers {
name: "label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__cost_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_2__"
}
inputs {
input_layer_name: "label"
}
coeff: 1.0
}
parameters {
name: "fc_param"
size: 40000
initial_mean: 0.0
initial_std: 1.0
dims: 200
dims: 200
initial_strategy: 1
initial_smart: false
}
parameters {
name: "bias_param"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "softmax_param"
size: 2000
initial_mean: 0.0
initial_std: 1.0
dims: 200
dims: 10
initial_strategy: 1
initial_smart: false
}
input_layer_names: "feature_a"
input_layer_names: "feature_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
input_layers: "__fc_layer_2__"
input_layers: "label"
}
sub_models {
name: "root"
layer_names: "feature_a"
layer_names: "feature_b"
layer_names: "__fc_layer_0__"
layer_names: "__fc_layer_1__"
layer_names: "__fc_layer_2__"
layer_names: "label"
layer_names: "__cost_0__"
input_layer_names: "feature_a"
input_layer_names: "feature_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/shared_lstm.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "recurrent_nn"
layers {
name: "data_a"
type: "data"
size: 100
active_type: ""
}
layers {
name: "data_b"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "data_a"
input_parameter_name: "mixed_param"
proj_conf {
type: "fc"
name: "___mixed_0__.w0"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "data_b"
input_parameter_name: "mixed_param"
proj_conf {
type: "fc"
name: "___mixed_1__.w0"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_0___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_0__@__lstm_group_0___recurrent_group"
type: "scatter_agent"
size: 400
active_type: ""
}
layers {
name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "__mixed_0__@__lstm_group_0___recurrent_group"
proj_conf {
type: "identity"
name: "___lstm_group_0___input_recurrent.w0"
input_size: 400
output_size: 400
}
}
inputs {
input_layer_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
input_parameter_name: "lstm_param"
proj_conf {
type: "fc"
name: "___lstm_group_0___input_recurrent.w1"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
type: "lstm_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
}
inputs {
input_layer_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
}
bias_parameter_name: "lstm_bias"
active_gate_type: "sigmoid"
active_state_type: "sigmoid"
}
layers {
name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
type: "get_output"
size: 100
active_type: ""
inputs {
input_layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
input_layer_argument: "state"
}
}
layers {
name: "__lstm_group_0__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_1__@__lstm_group_1___recurrent_group"
type: "scatter_agent"
size: 400
active_type: ""
}
layers {
name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "__mixed_1__@__lstm_group_1___recurrent_group"
proj_conf {
type: "identity"
name: "___lstm_group_1___input_recurrent.w0"
input_size: 400
output_size: 400
}
}
inputs {
input_layer_name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
input_parameter_name: "lstm_param"
proj_conf {
type: "fc"
name: "___lstm_group_1___input_recurrent.w1"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
type: "lstm_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
}
inputs {
input_layer_name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
}
bias_parameter_name: "lstm_bias"
active_gate_type: "sigmoid"
active_state_type: "sigmoid"
}
layers {
name: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
type: "get_output"
size: 100
active_type: ""
inputs {
input_layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
input_layer_argument: "state"
}
}
layers {
name: "__lstm_group_1__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__lstm_group_1__"
}
trans_type: "non-seq"
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "__last_seq_0__"
input_parameter_name: "softmax_param"
}
inputs {
input_layer_name: "__last_seq_1__"
input_parameter_name: "softmax_param"
}
}
layers {
name: "label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__cost_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "label"
}
coeff: 1.0
}
parameters {
name: "mixed_param"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "lstm_param"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "lstm_bias"
size: 300
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: false
}
parameters {
name: "softmax_param"
size: 1000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 10
initial_strategy: 0
initial_smart: true
}
input_layer_names: "data_a"
input_layer_names: "data_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
input_layers: "__fc_layer_0__"
input_layers: "label"
}
sub_models {
name: "root"
layer_names: "data_a"
layer_names: "data_b"
layer_names: "__mixed_0__"
layer_names: "__mixed_1__"
layer_names: "__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__"
layer_names: "__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__"
layer_names: "__last_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__fc_layer_0__"
layer_names: "label"
layer_names: "__cost_0__"
input_layer_names: "data_a"
input_layer_names: "data_b"
input_layer_names: "label"
output_layer_names: "__cost_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
sub_models {
name: "__lstm_group_0___recurrent_group"
layer_names: "__mixed_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
memories {
layer_name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_0__"
link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
sub_models {
name: "__lstm_group_1___recurrent_group"
layer_names: "__mixed_1__@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___input_recurrent@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1__@__lstm_group_1___recurrent_group"
layer_names: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1__+delay1@__lstm_group_1___recurrent_group"
is_sequence: false
}
memories {
layer_name: "__lstm_group_1___state@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1___state+delay1@__lstm_group_1___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_1__"
link_name: "__mixed_1__@__lstm_group_1___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_1__@__lstm_group_1___recurrent_group"
link_name: "__lstm_group_1__"
has_subseq: false
}
target_inlinkid: -1
}
python/paddle/trainer_config_helpers/tests/configs/protostr/simple_rnn_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 200
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "data"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__recurrent_layer_0__"
type: "recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___recurrent_layer_0__.w0"
}
bias_parameter_name: "___recurrent_layer_0__.wbias"
reversed: false
}
layers {
name: "__recurrent_layer_1__"
type: "recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___recurrent_layer_1__.w0"
}
bias_parameter_name: "___recurrent_layer_1__.wbias"
reversed: true
}
layers {
name: "__fc_layer_1__"
type: "fc"
size: 800
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_1__.w0"
}
}
layers {
name: "__lstmemory_0__"
type: "lstmemory"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_1__"
input_parameter_name: "___lstmemory_0__.w0"
}
bias_parameter_name: "___lstmemory_0__.wbias"
reversed: false
active_gate_type: "sigmoid"
active_state_type: "tanh"
}
layers {
name: "__fc_layer_2__"
type: "fc"
size: 800
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_2__.w0"
}
}
layers {
name: "__lstmemory_1__"
type: "lstmemory"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_2__"
input_parameter_name: "___lstmemory_1__.w0"
}
bias_parameter_name: "___lstmemory_1__.wbias"
reversed: true
active_gate_type: "sigmoid"
active_state_type: "tanh"
}
layers {
name: "__fc_layer_3__"
type: "fc"
size: 600
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_3__.w0"
}
}
layers {
name: "__gru_0__"
type: "gated_recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_3__"
input_parameter_name: "___gru_0__.w0"
}
bias_parameter_name: "___gru_0__.wbias"
reversed: false
active_gate_type: "sigmoid"
}
layers {
name: "__fc_layer_4__"
type: "fc"
size: 600
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___fc_layer_4__.w0"
}
}
layers {
name: "__gru_1__"
type: "gated_recurrent"
size: 200
active_type: "sigmoid"
inputs {
input_layer_name: "__fc_layer_4__"
input_parameter_name: "___gru_1__.w0"
}
bias_parameter_name: "___gru_1__.wbias"
reversed: true
active_gate_type: "sigmoid"
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__recurrent_layer_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__recurrent_layer_1__"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__lstmemory_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_1__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__lstmemory_1__"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__last_seq_2__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__gru_0__"
}
trans_type: "non-seq"
}
layers {
name: "__first_seq_2__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "__gru_1__"
}
select_first: true
trans_type: "non-seq"
}
parameters {
name: "___fc_layer_0__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___recurrent_layer_0__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___recurrent_layer_0__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___recurrent_layer_1__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___recurrent_layer_1__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_1__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 800
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_0__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
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dims: 4
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_0__.wbias"
size: 1400
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1400
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_2__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
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initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_1__.w0"
size: 160000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
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initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_1__.wbias"
size: 1400
initial_mean: 0.0
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dims: 1
dims: 1400
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_3__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_0__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_0__.wbias"
size: 600
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 600
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_4__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_1__.w0"
size: 120000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 600
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_1__.wbias"
size: 600
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 600
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__first_seq_2__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__fc_layer_0__"
layer_names: "__recurrent_layer_0__"
layer_names: "__recurrent_layer_1__"
layer_names: "__fc_layer_1__"
layer_names: "__lstmemory_0__"
layer_names: "__fc_layer_2__"
layer_names: "__lstmemory_1__"
layer_names: "__fc_layer_3__"
layer_names: "__gru_0__"
layer_names: "__fc_layer_4__"
layer_names: "__gru_1__"
layer_names: "__last_seq_0__"
layer_names: "__first_seq_0__"
layer_names: "__last_seq_1__"
layer_names: "__first_seq_1__"
layer_names: "__last_seq_2__"
layer_names: "__first_seq_2__"
input_layer_names: "data"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__first_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__first_seq_2__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 120
active_type: ""
}
layers {
name: "__bidirectional_gru_0___fw_transform"
type: "mixed"
size: 120
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___bidirectional_gru_0___fw_transform.w0"
proj_conf {
type: "fc"
name: "___bidirectional_gru_0___fw_transform.w0"
input_size: 120
output_size: 120
}
}
}
layers {
name: "__bidirectional_gru_0___fw"
type: "gated_recurrent"
size: 40
active_type: "tanh"
inputs {
input_layer_name: "__bidirectional_gru_0___fw_transform"
input_parameter_name: "___bidirectional_gru_0___fw.w0"
}
bias_parameter_name: "___bidirectional_gru_0___fw.wbias"
reversed: false
active_gate_type: "sigmoid"
}
layers {
name: "__bidirectional_gru_0___bw_transform"
type: "mixed"
size: 120
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___bidirectional_gru_0___bw_transform.w0"
proj_conf {
type: "fc"
name: "___bidirectional_gru_0___bw_transform.w0"
input_size: 120
output_size: 120
}
}
}
layers {
name: "__bidirectional_gru_0___bw"
type: "gated_recurrent"
size: 40
active_type: "tanh"
inputs {
input_layer_name: "__bidirectional_gru_0___bw_transform"
input_parameter_name: "___bidirectional_gru_0___bw.w0"
}
bias_parameter_name: "___bidirectional_gru_0___bw.wbias"
reversed: true
active_gate_type: "sigmoid"
}
layers {
name: "__bidirectional_gru_0__"
type: "concat"
size: 80
active_type: ""
inputs {
input_layer_name: "__bidirectional_gru_0___fw"
}
inputs {
input_layer_name: "__bidirectional_gru_0___bw"
}
}
parameters {
name: "___bidirectional_gru_0___fw_transform.w0"
size: 14400
initial_mean: 0.0
initial_std: 0.0912870929175
dims: 120
dims: 120
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___bidirectional_gru_0___fw.w0"
size: 4800
initial_mean: 0.0
initial_std: 0.158113883008
dims: 40
dims: 120
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___bidirectional_gru_0___fw.wbias"
size: 120
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 120
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___bidirectional_gru_0___bw_transform.w0"
size: 14400
initial_mean: 0.0
initial_std: 0.0912870929175
dims: 120
dims: 120
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___bidirectional_gru_0___bw.w0"
size: 4800
initial_mean: 0.0
initial_std: 0.158113883008
dims: 40
dims: 120
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___bidirectional_gru_0___bw.wbias"
size: 120
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 120
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__bidirectional_gru_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__bidirectional_gru_0___fw_transform"
layer_names: "__bidirectional_gru_0___fw"
layer_names: "__bidirectional_gru_0___bw_transform"
layer_names: "__bidirectional_gru_0___bw"
layer_names: "__bidirectional_gru_0__"
input_layer_names: "data"
output_layer_names: "__bidirectional_gru_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "input"
type: "data"
size: 200
active_type: ""
}
layers {
name: "labels"
type: "data"
size: 5000
active_type: ""
}
layers {
name: "probs"
type: "data"
size: 10
active_type: ""
}
layers {
name: "xe-label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__ctc_layer_0__"
type: "ctc"
size: 5001
active_type: ""
inputs {
input_layer_name: "input"
}
inputs {
input_layer_name: "labels"
}
norm_by_times: false
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 4
active_type: "tanh"
inputs {
input_layer_name: "input"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "crf_label"
type: "data"
size: 4
active_type: ""
}
layers {
name: "__crf_layer_0__"
type: "crf"
size: 4
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
input_parameter_name: "___crf_layer_0__.w0"
}
inputs {
input_layer_name: "crf_label"
}
coeff: 1.0
}
layers {
name: "left"
type: "data"
size: 1
active_type: ""
}
layers {
name: "right"
type: "data"
size: 1
active_type: ""
}
layers {
name: "label"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__rank_cost_0__"
type: "rank-cost"
size: 1
active_type: ""
inputs {
input_layer_name: "left"
}
inputs {
input_layer_name: "right"
}
inputs {
input_layer_name: "label"
}
coeff: 1.0
}
layers {
name: "list_feature"
type: "data"
size: 100
active_type: ""
}
layers {
name: "list_scores"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__lambda_cost_0__"
type: "lambda_cost"
size: 1
active_type: ""
inputs {
input_layer_name: "list_feature"
}
inputs {
input_layer_name: "list_scores"
}
NDCG_num: 5
max_sort_size: -1
}
layers {
name: "__cross_entropy_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "probs"
}
inputs {
input_layer_name: "xe-label"
}
coeff: 1.0
}
layers {
name: "__cross_entropy_with_selfnorm_0__"
type: "multi_class_cross_entropy_with_selfnorm"
active_type: ""
inputs {
input_layer_name: "probs"
}
inputs {
input_layer_name: "xe-label"
}
softmax_selfnorm_alpha: 0.1
coeff: 1.0
}
layers {
name: "huber_probs"
type: "data"
size: 1
active_type: ""
}
layers {
name: "huber_label"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__huber_cost_0__"
type: "huber"
size: 1
active_type: ""
inputs {
input_layer_name: "huber_probs"
}
inputs {
input_layer_name: "huber_label"
}
coeff: 1.0
}
layers {
name: "__multi_binary_label_cross_entropy_0__"
type: "multi_binary_label_cross_entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "probs"
}
inputs {
input_layer_name: "xe-label"
}
coeff: 1.0
}
parameters {
name: "___fc_layer_0__.w0"
size: 800
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 4
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 4
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 4
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___crf_layer_0__.w0"
size: 24
initial_mean: 0.0
initial_std: 0.5
dims: 4
dims: 6
initial_strategy: 0
initial_smart: true
}
input_layer_names: "input"
input_layer_names: "labels"
input_layer_names: "crf_label"
input_layer_names: "left"
input_layer_names: "right"
input_layer_names: "label"
input_layer_names: "list_feature"
input_layer_names: "list_scores"
input_layer_names: "probs"
input_layer_names: "xe-label"
input_layer_names: "huber_probs"
input_layer_names: "huber_label"
output_layer_names: "__ctc_layer_0__"
output_layer_names: "__crf_layer_0__"
output_layer_names: "__rank_cost_0__"
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "labels"
layer_names: "probs"
layer_names: "xe-label"
layer_names: "__ctc_layer_0__"
layer_names: "__fc_layer_0__"
layer_names: "crf_label"
layer_names: "__crf_layer_0__"
layer_names: "left"
layer_names: "right"
layer_names: "label"
layer_names: "__rank_cost_0__"
layer_names: "list_feature"
layer_names: "list_scores"
layer_names: "__lambda_cost_0__"
layer_names: "__cross_entropy_0__"
layer_names: "__cross_entropy_with_selfnorm_0__"
layer_names: "huber_probs"
layer_names: "huber_label"
layer_names: "__huber_cost_0__"
layer_names: "__multi_binary_label_cross_entropy_0__"
input_layer_names: "input"
input_layer_names: "labels"
input_layer_names: "crf_label"
input_layer_names: "left"
input_layer_names: "right"
input_layer_names: "label"
input_layer_names: "list_feature"
input_layer_names: "list_scores"
input_layer_names: "probs"
input_layer_names: "xe-label"
input_layer_names: "huber_probs"
input_layer_names: "huber_label"
output_layer_names: "__ctc_layer_0__"
output_layer_names: "__crf_layer_0__"
output_layer_names: "__rank_cost_0__"
output_layer_names: "__lambda_cost_0__"
output_layer_names: "__cross_entropy_0__"
output_layer_names: "__cross_entropy_with_selfnorm_0__"
output_layer_names: "__huber_cost_0__"
output_layer_names: "__multi_binary_label_cross_entropy_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers_with_weight.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "label"
type: "data"
size: 1
active_type: ""
}
layers {
name: "weight"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 10
active_type: "softmax"
inputs {
input_layer_name: "input"
input_parameter_name: "___fc_layer_0__.w0"
}
bias_parameter_name: "___fc_layer_0__.wbias"
}
layers {
name: "__cost_0__"
type: "multi-class-cross-entropy"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "label"
}
inputs {
input_layer_name: "weight"
}
coeff: 1.0
}
layers {
name: "__regression_cost_0__"
type: "square_error"
size: 1
active_type: ""
inputs {
input_layer_name: "__fc_layer_0__"
}
inputs {
input_layer_name: "label"
}
inputs {
input_layer_name: "weight"
}
coeff: 1.0
}
parameters {
name: "___fc_layer_0__.w0"
size: 3000
initial_mean: 0.0
initial_std: 0.057735026919
dims: 300
dims: 10
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___fc_layer_0__.wbias"
size: 10
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 10
initial_strategy: 0
initial_smart: false
}
input_layer_names: "input"
input_layer_names: "label"
input_layer_names: "weight"
output_layer_names: "__cost_0__"
output_layer_names: "__regression_cost_0__"
evaluators {
name: "classification_error_evaluator"
type: "classification_error"
input_layers: "__fc_layer_0__"
input_layers: "label"
input_layers: "weight"
}
sub_models {
name: "root"
layer_names: "input"
layer_names: "label"
layer_names: "weight"
layer_names: "__fc_layer_0__"
layer_names: "__cost_0__"
layer_names: "__regression_cost_0__"
input_layer_names: "input"
input_layer_names: "label"
input_layer_names: "weight"
output_layer_names: "__cost_0__"
output_layer_names: "__regression_cost_0__"
evaluator_names: "classification_error_evaluator"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_expand_layer.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 30
active_type: ""
}
layers {
name: "data_seq"
type: "data"
size: 30
active_type: ""
}
layers {
name: "__expand_layer_0__"
type: "expand"
size: 30
active_type: ""
inputs {
input_layer_name: "data"
}
inputs {
input_layer_name: "data_seq"
}
trans_type: "seq"
}
layers {
name: "__expand_layer_1__"
type: "expand"
size: 30
active_type: ""
inputs {
input_layer_name: "data"
}
inputs {
input_layer_name: "data_seq"
}
trans_type: "non-seq"
}
input_layer_names: "data"
input_layer_names: "data_seq"
output_layer_names: "__expand_layer_0__"
output_layer_names: "__expand_layer_1__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "data_seq"
layer_names: "__expand_layer_0__"
layer_names: "__expand_layer_1__"
input_layer_names: "data"
input_layer_names: "data_seq"
output_layer_names: "__expand_layer_0__"
output_layer_names: "__expand_layer_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_fc.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__trans_layer_0__"
type: "trans"
size: 100
active_type: ""
inputs {
input_layer_name: "data"
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__trans_layer_0__"
input_parameter_name: "___fc_layer_0__.w0"
}
}
layers {
name: "mask"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__selective_fc_layer_0__"
type: "selective_fc"
size: 100
active_type: "sigmoid"
inputs {
input_layer_name: "data"
input_parameter_name: "___selective_fc_layer_0__.w0"
}
inputs {
input_layer_name: "mask"
}
bias_parameter_name: "___selective_fc_layer_0__.wbias"
selective_fc_pass_generation: false
has_selected_colums: true
selective_fc_full_mul_ratio: 0.02
}
parameters {
name: "___fc_layer_0__.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___selective_fc_layer_0__.w0"
size: 10000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
initial_strategy: 0
initial_smart: true
is_sparse: false
}
parameters {
name: "___selective_fc_layer_0__.wbias"
size: 100
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 100
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
input_layer_names: "mask"
output_layer_names: "__fc_layer_0__"
output_layer_names: "__selective_fc_layer_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__trans_layer_0__"
layer_names: "__fc_layer_0__"
layer_names: "mask"
layer_names: "__selective_fc_layer_0__"
input_layer_names: "data"
input_layer_names: "mask"
output_layer_names: "__fc_layer_0__"
output_layer_names: "__selective_fc_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_grumemory_layer.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 120
active_type: ""
}
layers {
name: "__gru_0__"
type: "gated_recurrent"
size: 40
active_type: "sigmoid"
inputs {
input_layer_name: "data"
input_parameter_name: "___gru_0__.w0"
}
bias_parameter_name: "___gru_0__.wbias"
reversed: true
active_gate_type: "tanh"
}
parameters {
name: "___gru_0__.w0"
size: 4800
initial_mean: 0.0
initial_std: 0.158113883008
dims: 40
dims: 120
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___gru_0__.wbias"
size: 120
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 120
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__gru_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__gru_0__"
input_layer_names: "data"
output_layer_names: "__gru_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_hsigmoid.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 100
active_type: ""
}
layers {
name: "label"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__hsigmoid_0__"
type: "hsigmoid"
size: 1
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___hsigmoid_0__.w0"
}
inputs {
input_layer_name: "label"
}
bias_parameter_name: "___hsigmoid_0__.wbias"
num_classes: 10
}
parameters {
name: "___hsigmoid_0__.w0"
size: 900
initial_mean: 0.0
initial_std: 0.333333333333
dims: 9
dims: 100
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___hsigmoid_0__.wbias"
size: 9
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 9
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
input_layer_names: "label"
output_layer_names: "__hsigmoid_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "label"
layer_names: "__hsigmoid_0__"
input_layer_names: "data"
input_layer_names: "label"
output_layer_names: "__hsigmoid_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_lstmemory_layer.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 128
active_type: ""
}
layers {
name: "__lstmemory_0__"
type: "lstmemory"
size: 32
active_type: "tanh"
inputs {
input_layer_name: "data"
input_parameter_name: "___lstmemory_0__.w0"
}
bias_parameter_name: "___lstmemory_0__.wbias"
reversed: true
active_gate_type: "tanh"
active_state_type: "tanh"
}
parameters {
name: "___lstmemory_0__.w0"
size: 4096
initial_mean: 0.0
initial_std: 0.176776695297
dims: 32
dims: 32
dims: 4
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstmemory_0__.wbias"
size: 224
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 224
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
output_layer_names: "__lstmemory_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__lstmemory_0__"
input_layer_names: "data"
output_layer_names: "__lstmemory_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "data"
type: "data"
size: 2304
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 36864
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 3
channels: 1
stride: 1
padding: 1
groups: 1
filter_channels: 1
output_x: 48
img_size: 48
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 16
shared_biases: true
}
layers {
name: "__maxout_layer_0__"
type: "maxout"
size: 18432
active_type: ""
inputs {
input_layer_name: "__conv_0__"
maxout_conf {
channels: 16
groups: 2
img_size_x: 0
img_size_y: 0
}
}
}
layers {
name: "__pool_0__"
type: "pool"
size: 4608
active_type: ""
inputs {
input_layer_name: "__maxout_layer_0__"
pool_conf {
pool_type: "max-projection"
channels: 8
size_x: 2
stride: 2
output_x: 24
img_size: 48
padding: 0
size_y: 2
stride_y: 2
output_y: 24
img_size_y: 48
padding_y: 0
}
}
}
layers {
name: "__conv_1__"
type: "exconv"
size: 18432
active_type: ""
inputs {
input_layer_name: "__pool_0__"
input_parameter_name: "___conv_1__.w0"
conv_conf {
filter_size: 3
channels: 32
stride: 1
padding: 1
groups: 1
filter_channels: 32
output_x: 12
img_size: 12
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 1
}
}
bias_parameter_name: "___conv_1__.wbias"
num_filters: 128
shared_biases: true
}
layers {
name: "__maxout_layer_1__"
type: "maxout"
size: 9216
active_type: ""
inputs {
input_layer_name: "__conv_0__"
maxout_conf {
channels: 128
groups: 4
img_size_x: 0
img_size_y: 0
}
}
}
layers {
name: "__block_expand_layer_0__"
type: "blockexpand"
size: 192
active_type: ""
inputs {
input_layer_name: "__maxout_layer_0__"
block_expand_conf {
channels: 32
stride_x: 1
stride_y: 1
padding_x: 0
padding_y: 0
block_x: 1
block_y: 6
output_x: 0
output_y: 0
img_size_x: 0
img_size_y: 0
}
}
}
layers {
name: "__fc_layer_0__"
type: "fc"
size: 384
active_type: "tanh"
inputs {
input_layer_name: "__block_expand_layer_0__"
input_parameter_name: "___fc_layer_0__.w0"
}
}
parameters {
name: "___conv_0__.w0"
size: 144
initial_mean: 0.0
initial_std: 0.471404520791
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
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}
parameters {
name: "___conv_1__.w0"
size: 36864
initial_mean: 0.0
initial_std: 0.0833333333333
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_1__.wbias"
size: 128
initial_mean: 0.0
initial_std: 0.0
dims: 128
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___fc_layer_0__.w0"
size: 73728
initial_mean: 0.0
initial_std: 0.0721687836487
dims: 192
dims: 384
initial_strategy: 0
initial_smart: true
}
input_layer_names: "data"
output_layer_names: "__fc_layer_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "__conv_0__"
layer_names: "__maxout_layer_0__"
layer_names: "__pool_0__"
layer_names: "__conv_1__"
layer_names: "__maxout_layer_1__"
layer_names: "__block_expand_layer_0__"
layer_names: "__fc_layer_0__"
input_layer_names: "data"
output_layer_names: "__fc_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_ntm_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "w"
type: "data"
size: 1
active_type: ""
}
layers {
name: "a"
type: "data"
size: 100
active_type: ""
}
layers {
name: "b"
type: "data"
size: 100
active_type: ""
}
layers {
name: "c"
type: "data"
size: 200
active_type: ""
}
layers {
name: "d"
type: "data"
size: 31
active_type: ""
}
layers {
name: "__interpolation_layer_0__"
type: "interpolation"
size: 100
active_type: ""
inputs {
input_layer_name: "w"
}
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "b"
}
}
layers {
name: "__power_layer_0__"
type: "power"
size: 100
active_type: ""
inputs {
input_layer_name: "w"
}
inputs {
input_layer_name: "a"
}
}
layers {
name: "__scaling_layer_0__"
type: "scaling"
size: 100
active_type: ""
inputs {
input_layer_name: "w"
}
inputs {
input_layer_name: "a"
}
}
layers {
name: "__cos_sim_0__"
type: "cos"
size: 1
active_type: ""
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "b"
}
cos_scale: 5
}
layers {
name: "__cos_sim_1__"
type: "cos_vm"
size: 2
active_type: ""
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "c"
}
cos_scale: 5
}
layers {
name: "__sum_to_one_norm_layer_0__"
type: "sum_to_one_norm"
size: 100
active_type: ""
inputs {
input_layer_name: "a"
}
}
layers {
name: "__conv_shift_layer_0__"
type: "conv_shift"
size: 100
active_type: ""
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "d"
}
}
layers {
name: "__tensor_layer_0__"
type: "tensor"
size: 1000
active_type: ""
inputs {
input_layer_name: "a"
input_parameter_name: "___tensor_layer_0__.w0"
}
inputs {
input_layer_name: "b"
}
bias_parameter_name: "___tensor_layer_0__.wbias"
}
layers {
name: "__slope_intercept_layer_0__"
type: "slope_intercept"
size: 100
active_type: ""
inputs {
input_layer_name: "a"
}
slope: 0.7
intercept: 0.9
}
layers {
name: "__linear_comb_layer_0__"
type: "convex_comb"
size: 2
active_type: ""
inputs {
input_layer_name: "b"
}
inputs {
input_layer_name: "c"
}
}
parameters {
name: "___tensor_layer_0__.w0"
size: 10000000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 100
dims: 1000
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___tensor_layer_0__.wbias"
size: 1000
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 1000
initial_strategy: 0
initial_smart: false
}
input_layer_names: "w"
input_layer_names: "a"
input_layer_names: "b"
input_layer_names: "c"
input_layer_names: "d"
output_layer_names: "__interpolation_layer_0__"
output_layer_names: "__power_layer_0__"
output_layer_names: "__scaling_layer_0__"
output_layer_names: "__cos_sim_0__"
output_layer_names: "__cos_sim_1__"
output_layer_names: "__sum_to_one_norm_layer_0__"
output_layer_names: "__conv_shift_layer_0__"
output_layer_names: "__tensor_layer_0__"
output_layer_names: "__slope_intercept_layer_0__"
output_layer_names: "__linear_comb_layer_0__"
sub_models {
name: "root"
layer_names: "w"
layer_names: "a"
layer_names: "b"
layer_names: "c"
layer_names: "d"
layer_names: "__interpolation_layer_0__"
layer_names: "__power_layer_0__"
layer_names: "__scaling_layer_0__"
layer_names: "__cos_sim_0__"
layer_names: "__cos_sim_1__"
layer_names: "__sum_to_one_norm_layer_0__"
layer_names: "__conv_shift_layer_0__"
layer_names: "__tensor_layer_0__"
layer_names: "__slope_intercept_layer_0__"
layer_names: "__linear_comb_layer_0__"
input_layer_names: "w"
input_layer_names: "a"
input_layer_names: "b"
input_layer_names: "c"
input_layer_names: "d"
output_layer_names: "__interpolation_layer_0__"
output_layer_names: "__power_layer_0__"
output_layer_names: "__scaling_layer_0__"
output_layer_names: "__cos_sim_0__"
output_layer_names: "__cos_sim_1__"
output_layer_names: "__sum_to_one_norm_layer_0__"
output_layer_names: "__conv_shift_layer_0__"
output_layer_names: "__tensor_layer_0__"
output_layer_names: "__slope_intercept_layer_0__"
output_layer_names: "__linear_comb_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_print_layer.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__print_0__"
type: "print"
active_type: ""
inputs {
input_layer_name: "input"
}
}
input_layer_names: "input"
output_layer_names: "input"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__print_0__"
input_layer_names: "input"
output_layer_names: "input"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_rnn_group.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "recurrent_nn"
layers {
name: "seq_input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "sub_seq_input"
type: "data"
size: 100
active_type: ""
}
layers {
name: "label"
type: "data"
size: 1
active_type: ""
}
layers {
name: "__mixed_0__"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "seq_input"
input_parameter_name: "___mixed_0__.w0"
proj_conf {
type: "fc"
name: "___mixed_0__.w0"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__mixed_1__"
type: "mixed"
size: 300
active_type: ""
inputs {
input_layer_name: "seq_input"
input_parameter_name: "___mixed_1__.w0"
proj_conf {
type: "fc"
name: "___mixed_1__.w0"
input_size: 100
output_size: 300
}
}
}
layers {
name: "__recurrent_group_0__"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "seq_input@__recurrent_group_0__"
type: "scatter_agent"
size: 100
active_type: ""
}
layers {
name: "rnn_forward+delay1@__recurrent_group_0__"
type: "agent"
size: 200
active_type: ""
}
layers {
name: "rnn_forward@__recurrent_group_0__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "seq_input@__recurrent_group_0__"
input_parameter_name: "_rnn_forward@__recurrent_group_0__.w0"
}
inputs {
input_layer_name: "rnn_forward+delay1@__recurrent_group_0__"
input_parameter_name: "_rnn_forward@__recurrent_group_0__.w1"
}
bias_parameter_name: "_rnn_forward@__recurrent_group_0__.wbias"
}
layers {
name: "rnn_forward"
type: "gather_agent"
size: 200
active_type: ""
}
layers {
name: "__last_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "rnn_forward"
}
trans_type: "non-seq"
}
layers {
name: "__recurrent_group_1__"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "seq_input@__recurrent_group_1__"
type: "scatter_agent"
size: 100
active_type: ""
}
layers {
name: "rnn_back+delay1@__recurrent_group_1__"
type: "agent"
size: 200
active_type: ""
}
layers {
name: "rnn_back@__recurrent_group_1__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "seq_input@__recurrent_group_1__"
input_parameter_name: "_rnn_back@__recurrent_group_1__.w0"
}
inputs {
input_layer_name: "rnn_back+delay1@__recurrent_group_1__"
input_parameter_name: "_rnn_back@__recurrent_group_1__.w1"
}
bias_parameter_name: "_rnn_back@__recurrent_group_1__.wbias"
}
layers {
name: "rnn_back"
type: "gather_agent"
size: 200
active_type: ""
}
layers {
name: "__first_seq_0__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "rnn_back"
}
select_first: true
trans_type: "non-seq"
}
layers {
name: "__recurrent_group_2__"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "sub_seq_input@__recurrent_group_2__"
type: "sequence_scatter_agent"
size: 100
active_type: ""
}
layers {
name: "rnn_subseq_forward+delay1@__recurrent_group_2__"
type: "agent"
size: 200
active_type: ""
}
layers {
name: "rnn_subseq_forward@__recurrent_group_2__"
type: "fc"
size: 200
active_type: "tanh"
inputs {
input_layer_name: "sub_seq_input@__recurrent_group_2__"
input_parameter_name: "_rnn_subseq_forward@__recurrent_group_2__.w0"
}
inputs {
input_layer_name: "rnn_subseq_forward+delay1@__recurrent_group_2__"
input_parameter_name: "_rnn_subseq_forward@__recurrent_group_2__.w1"
}
bias_parameter_name: "_rnn_subseq_forward@__recurrent_group_2__.wbias"
}
layers {
name: "rnn_subseq_forward"
type: "sequence_gather_agent"
size: 200
active_type: ""
}
layers {
name: "__last_seq_1__"
type: "seqlastins"
size: 200
active_type: "linear"
inputs {
input_layer_name: "rnn_subseq_forward"
}
trans_type: "non-seq"
}
layers {
name: "__lstm_group_0___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_0__@__lstm_group_0___recurrent_group"
type: "scatter_agent"
size: 400
active_type: ""
}
layers {
name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
type: "mixed"
size: 400
active_type: ""
inputs {
input_layer_name: "__mixed_0__@__lstm_group_0___recurrent_group"
proj_conf {
type: "identity"
name: "___lstm_group_0___input_recurrent.w0"
input_size: 400
output_size: 400
}
}
inputs {
input_layer_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
input_parameter_name: "___lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group.w1"
proj_conf {
type: "fc"
name: "___lstm_group_0___input_recurrent.w1"
input_size: 100
output_size: 400
}
}
}
layers {
name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
type: "lstm_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
}
inputs {
input_layer_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
}
bias_parameter_name: "___lstm_group_0__@__lstm_group_0___recurrent_group.wbias"
active_gate_type: "sigmoid"
active_state_type: "sigmoid"
}
layers {
name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
type: "get_output"
size: 100
active_type: ""
inputs {
input_layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
input_layer_argument: "state"
}
}
layers {
name: "__lstm_group_0__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__last_seq_2__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__lstm_group_0__"
}
trans_type: "non-seq"
}
layers {
name: "__gru_group_0___recurrent_group"
type: "recurrent_layer_group"
active_type: ""
}
layers {
name: "__mixed_1__@__gru_group_0___recurrent_group"
type: "scatter_agent"
size: 300
active_type: ""
}
layers {
name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
type: "agent"
size: 100
active_type: ""
}
layers {
name: "__gru_group_0__@__gru_group_0___recurrent_group"
type: "gru_step"
size: 100
active_type: "tanh"
inputs {
input_layer_name: "__mixed_1__@__gru_group_0___recurrent_group"
input_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.w0"
}
inputs {
input_layer_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
}
bias_parameter_name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias"
active_gate_type: "sigmoid"
}
layers {
name: "__gru_group_0__"
type: "gather_agent"
size: 100
active_type: ""
}
layers {
name: "__last_seq_3__"
type: "seqlastins"
size: 100
active_type: "linear"
inputs {
input_layer_name: "__gru_group_0__"
}
trans_type: "non-seq"
}
parameters {
name: "___mixed_0__.w0"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___mixed_1__.w0"
size: 30000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 300
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_forward@__recurrent_group_0__.w0"
size: 20000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_forward@__recurrent_group_0__.w1"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_forward@__recurrent_group_0__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_rnn_back@__recurrent_group_1__.w0"
size: 20000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_back@__recurrent_group_1__.w1"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_back@__recurrent_group_1__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "_rnn_subseq_forward@__recurrent_group_2__.w0"
size: 20000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_subseq_forward@__recurrent_group_2__.w1"
size: 40000
initial_mean: 0.0
initial_std: 0.0707106781187
dims: 200
dims: 200
initial_strategy: 0
initial_smart: true
}
parameters {
name: "_rnn_subseq_forward@__recurrent_group_2__.wbias"
size: 200
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 200
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group.w1"
size: 40000
initial_mean: 0.0
initial_std: 0.1
dims: 100
dims: 400
initial_strategy: 0
initial_smart: true
}
parameters {
name: "___lstm_group_0__@__lstm_group_0___recurrent_group.wbias"
size: 300
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___gru_group_0__@__gru_group_0___recurrent_group.w0"
size: 30000
initial_mean: 0.0
initial_std: 0.01
dims: 100
dims: 300
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___gru_group_0__@__gru_group_0___recurrent_group.wbias"
size: 300
initial_mean: 0.0
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: false
}
input_layer_names: "seq_input"
input_layer_names: "sub_seq_input"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__last_seq_3__"
sub_models {
name: "root"
layer_names: "seq_input"
layer_names: "sub_seq_input"
layer_names: "label"
layer_names: "__mixed_0__"
layer_names: "__mixed_1__"
layer_names: "__recurrent_group_0__"
layer_names: "rnn_forward"
layer_names: "__last_seq_0__"
layer_names: "__recurrent_group_1__"
layer_names: "rnn_back"
layer_names: "__first_seq_0__"
layer_names: "__recurrent_group_2__"
layer_names: "rnn_subseq_forward"
layer_names: "__last_seq_1__"
layer_names: "__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__"
layer_names: "__last_seq_2__"
layer_names: "__gru_group_0___recurrent_group"
layer_names: "__gru_group_0__"
layer_names: "__last_seq_3__"
input_layer_names: "seq_input"
input_layer_names: "sub_seq_input"
output_layer_names: "__last_seq_0__"
output_layer_names: "__first_seq_0__"
output_layer_names: "__last_seq_1__"
output_layer_names: "__last_seq_2__"
output_layer_names: "__last_seq_3__"
is_recurrent_layer_group: false
}
sub_models {
name: "__recurrent_group_0__"
layer_names: "seq_input@__recurrent_group_0__"
layer_names: "rnn_forward+delay1@__recurrent_group_0__"
layer_names: "rnn_forward@__recurrent_group_0__"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "rnn_forward@__recurrent_group_0__"
link_name: "rnn_forward+delay1@__recurrent_group_0__"
is_sequence: false
}
in_links {
layer_name: "seq_input"
link_name: "seq_input@__recurrent_group_0__"
has_subseq: false
}
out_links {
layer_name: "rnn_forward@__recurrent_group_0__"
link_name: "rnn_forward"
has_subseq: false
}
target_inlinkid: -1
}
sub_models {
name: "__recurrent_group_1__"
layer_names: "seq_input@__recurrent_group_1__"
layer_names: "rnn_back+delay1@__recurrent_group_1__"
layer_names: "rnn_back@__recurrent_group_1__"
is_recurrent_layer_group: true
reversed: true
memories {
layer_name: "rnn_back@__recurrent_group_1__"
link_name: "rnn_back+delay1@__recurrent_group_1__"
is_sequence: false
}
in_links {
layer_name: "seq_input"
link_name: "seq_input@__recurrent_group_1__"
has_subseq: false
}
out_links {
layer_name: "rnn_back@__recurrent_group_1__"
link_name: "rnn_back"
has_subseq: false
}
target_inlinkid: -1
}
sub_models {
name: "__recurrent_group_2__"
layer_names: "sub_seq_input@__recurrent_group_2__"
layer_names: "rnn_subseq_forward+delay1@__recurrent_group_2__"
layer_names: "rnn_subseq_forward@__recurrent_group_2__"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "rnn_subseq_forward@__recurrent_group_2__"
link_name: "rnn_subseq_forward+delay1@__recurrent_group_2__"
is_sequence: false
}
in_links {
layer_name: "sub_seq_input"
link_name: "sub_seq_input@__recurrent_group_2__"
has_subseq: true
}
out_links {
layer_name: "rnn_subseq_forward@__recurrent_group_2__"
link_name: "rnn_subseq_forward"
has_subseq: true
}
target_inlinkid: -1
}
sub_models {
name: "__lstm_group_0___recurrent_group"
layer_names: "__mixed_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___input_recurrent@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0__@__lstm_group_0___recurrent_group"
layer_names: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
memories {
layer_name: "__lstm_group_0___state@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0___state+delay1@__lstm_group_0___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_0__"
link_name: "__mixed_0__@__lstm_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__lstm_group_0__@__lstm_group_0___recurrent_group"
link_name: "__lstm_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
sub_models {
name: "__gru_group_0___recurrent_group"
layer_names: "__mixed_1__@__gru_group_0___recurrent_group"
layer_names: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
layer_names: "__gru_group_0__@__gru_group_0___recurrent_group"
is_recurrent_layer_group: true
reversed: false
memories {
layer_name: "__gru_group_0__@__gru_group_0___recurrent_group"
link_name: "__gru_group_0__+delay1@__gru_group_0___recurrent_group"
is_sequence: false
}
in_links {
layer_name: "__mixed_1__"
link_name: "__mixed_1__@__gru_group_0___recurrent_group"
has_subseq: false
}
out_links {
layer_name: "__gru_group_0__@__gru_group_0___recurrent_group"
link_name: "__gru_group_0__"
has_subseq: false
}
target_inlinkid: -1
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_sequence_pooling.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "dat_in"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__seq_pooling_0__"
type: "max"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
trans_type: "seq"
}
layers {
name: "__seq_pooling_1__"
type: "max"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
trans_type: "non-seq"
}
layers {
name: "__seq_pooling_2__"
type: "average"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
average_strategy: "average"
trans_type: "seq"
}
layers {
name: "__seq_pooling_3__"
type: "average"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
average_strategy: "average"
trans_type: "non-seq"
}
layers {
name: "__seq_pooling_4__"
type: "average"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
average_strategy: "sum"
trans_type: "seq"
}
layers {
name: "__seq_pooling_5__"
type: "average"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
average_strategy: "sum"
trans_type: "non-seq"
}
layers {
name: "__seq_pooling_6__"
type: "max"
size: 100
active_type: "linear"
inputs {
input_layer_name: "dat_in"
}
output_max_index: true
trans_type: "non-seq"
}
input_layer_names: "dat_in"
output_layer_names: "__seq_pooling_0__"
output_layer_names: "__seq_pooling_1__"
output_layer_names: "__seq_pooling_2__"
output_layer_names: "__seq_pooling_3__"
output_layer_names: "__seq_pooling_4__"
output_layer_names: "__seq_pooling_5__"
output_layer_names: "__seq_pooling_6__"
sub_models {
name: "root"
layer_names: "dat_in"
layer_names: "__seq_pooling_0__"
layer_names: "__seq_pooling_1__"
layer_names: "__seq_pooling_2__"
layer_names: "__seq_pooling_3__"
layer_names: "__seq_pooling_4__"
layer_names: "__seq_pooling_5__"
layer_names: "__seq_pooling_6__"
input_layer_names: "dat_in"
output_layer_names: "__seq_pooling_0__"
output_layer_names: "__seq_pooling_1__"
output_layer_names: "__seq_pooling_2__"
output_layer_names: "__seq_pooling_3__"
output_layer_names: "__seq_pooling_4__"
output_layer_names: "__seq_pooling_5__"
output_layer_names: "__seq_pooling_6__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/unused_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "probs"
type: "data"
size: 100
active_type: ""
}
layers {
name: "__sampling_id_layer_0__"
type: "sampling_id"
size: 100
active_type: ""
inputs {
input_layer_name: "probs"
}
}
input_layer_names: "probs"
output_layer_names: "__sampling_id_layer_0__"
sub_models {
name: "root"
layer_names: "probs"
layer_names: "__sampling_id_layer_0__"
input_layer_names: "probs"
output_layer_names: "__sampling_id_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr 0 → 100644
浏览文件 @ c64cd6fe
type: "nn"
layers {
name: "a"
type: "data"
size: 10
active_type: ""
}
layers {
name: "b"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__addto_0__"
type: "addto"
size: 10
active_type: ""
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "b"
}
}
layers {
name: "__concat_0__"
type: "concat"
size: 20
active_type: ""
inputs {
input_layer_name: "a"
}
inputs {
input_layer_name: "b"
}
}
layers {
name: "__concat_1__"
type: "concat2"
size: 20
active_type: ""
inputs {
input_layer_name: "a"
proj_conf {
type: "identity"
name: "___concat_1__.w0"
input_size: 10
output_size: 10
}
}
inputs {
input_layer_name: "b"
proj_conf {
type: "identity"
name: "___concat_1__.w1"
input_size: 10
output_size: 10
}
}
}
input_layer_names: "a"
input_layer_names: "b"
output_layer_names: "__addto_0__"
output_layer_names: "__concat_0__"
output_layer_names: "__concat_1__"
sub_models {
name: "root"
layer_names: "a"
layer_names: "b"
layer_names: "__addto_0__"
layer_names: "__concat_0__"
layer_names: "__concat_1__"
input_layer_names: "a"
input_layer_names: "b"
output_layer_names: "__addto_0__"
output_layer_names: "__concat_0__"
output_layer_names: "__concat_1__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/run_tests.sh
浏览文件 @ c64cd6fe
#!/bin/bash
cd `dirname $0`
set -e
protostr=`dirname $0`/protostr
files=`ls $protostr | grep -v "unitest"`
./generate_protostr.sh
md5sum -c check.md5
for file in $files
do
base_protostr=$protostr/$file
new_protostr=$protostr/$file.unitest
diff $base_protostr $new_protostr
done
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