Update

python/paddle/trainer_config_helpers/tests/configs/img_layers.protostr

@@ -0,0 +1,176 @@
+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: 0.899999976158
+}
+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: 0.000399999989895
+      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: 0.0441941730678
+  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/last_first_seq.protostr

@@ -0,0 +1,69 @@
+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/test_bilinear_interp.protostr

@@ -0,0 +1,125 @@
+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: "__bilinear_interp_layer_0__"
+  type: "bilinear_interp"
+  size: 36864
+  active_type: ""
+  inputs {
+    input_layer_name: "__conv_0__"
+    bilinear_interp_conf {
+      img_size_x: 32
+      img_size_y: 32
+      out_size_x: 64
+      out_size_y: 64
+      num_channels: 16
+    }
+  }
+}
+layers {
+  name: "__pool_0__"
+  type: "pool"
+  size: 9216
+  active_type: ""
+  inputs {
+    input_layer_name: "__bilinear_interp_layer_0__"
+    pool_conf {
+      pool_type: "max-projection"
+      channels: 4
+      size_x: 2
+      stride: 2
+      output_x: 48
+      img_size: 96
+      padding: 0
+      size_y: 2
+      stride_y: 2
+      output_y: 48
+      img_size_y: 96
+      padding_y: 0
+    }
+  }
+}
+layers {
+  name: "__fc_layer_0__"
+  type: "fc"
+  size: 384
+  active_type: "tanh"
+  inputs {
+    input_layer_name: "__pool_0__"
+    input_parameter_name: "___fc_layer_0__.w0"
+  }
+}
+parameters {
+  name: "___conv_0__.w0"
+  size: 144
+  initial_mean: 0.0
+  initial_std: 0.471404522657
+  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
+  initial_smart: false
+}
+parameters {
+  name: "___fc_layer_0__.w0"
+  size: 3538944
+  initial_mean: 0.0
+  initial_std: 0.0104166669771
+  dims: 9216
+  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: "__bilinear_interp_layer_0__"
+  layer_names: "__pool_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/test_cost_layers_with_weight.protostr

@@ -0,0 +1,111 @@
+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: 0.0577350258827
+  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/test_expand_layer.protostr

@@ -0,0 +1,56 @@
+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/test_fc.protostr

@@ -0,0 +1,98 @@
+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: 0.019999999553
+}
+parameters {
+  name: "___fc_layer_0__.w0"
+  size: 10000
+  initial_mean: 0.0
+  initial_std: 0.10000000149
+  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: 0.10000000149
+  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/test_grumemory_layer.protostr

@@ -0,0 +1,51 @@
+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: 0.158113881946
+  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/test_hsigmoid.protostr

@@ -0,0 +1,62 @@
+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: 0.333333343267
+  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/test_lstmemory_layer.protostr

@@ -0,0 +1,53 @@
+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: 0.176776692271
+  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/test_ntm_layers.protostr

@@ -0,0 +1,225 @@
+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: 5.0
+}
+layers {
+  name: "__cos_sim_1__"
+  type: "cos_vm"
+  size: 2
+  active_type: ""
+  inputs {
+    input_layer_name: "a"
+  }
+  inputs {
+    input_layer_name: "c"
+  }
+  cos_scale: 5.0
+}
+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: 0.699999988079
+  intercept: 0.899999976158
+}
+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: 0.10000000149
+  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/test_print_layer.protostr

@@ -0,0 +1,26 @@
+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/test_sequence_pooling.protostr

@@ -0,0 +1,111 @@
+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/unused_layers.protostr

@@ -0,0 +1,27 @@
+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/util_layers.protostr

@@ -0,0 +1,81 @@
+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
+}
+