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Fix:MINOR
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Lines changed: 154 additions & 145 deletions

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‎config/CNN/NP/README‎

Whitespace-only changes.

‎config/CNN/NP/data_spec.json‎

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@@ -1,33 +1,36 @@
11
{
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"validation": {
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"base_path" : "data/NPFILE",
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "val.dat",
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"partition" : 200,
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 512,
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"keep_flatten" : false,
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"reader_type" : "NP"
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},
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"training" : {
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"base_path" : "data/NPFILE",
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "train.dat",
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"partition" : 200,
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 512,
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"keep_flatten" : false,
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"reader_type" : "NP"
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},
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"testing" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "test.dat",
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"dim_shuffle" : [0,3,2,1],
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"batch_size": 512,
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"keep_flatten" : false,
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"reader_type" : "NP"
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}

‎config/CNN/NP/model_conf.json‎

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@@ -18,7 +18,7 @@
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"output_file" : "conv_out.model",
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"comment" : "input_shape :: (Mandatory) specify the input shape of a given feature vector" ,
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"input_shape" : [3,28,28],
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"input_shape" : [1,28,28],
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"comment" : "batch_size :: specify the mini batch size while training, default 256",
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"batch_size" : 256,
@@ -27,7 +27,7 @@
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"momentum" : 0.5,
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"comment" : "n_outs :: Specify the number of outputs",
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"n_outs" : 8,
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"n_outs" : 10,
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"comment" : "l_rate_method :: Two method l_rate method are supported C: Constant learning rate and E : Exponential decay",
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"l_rate_method" : "C",

‎config/CNN/NP/nnet_spec.json‎

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Original file line numberDiff line numberDiff line change
@@ -1,37 +1,37 @@
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{
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"comment" : "layers :: convolution layer configuration",
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"cnn": {
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"layers": [
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{
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"convmat_dim" : [5,5],
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"num_filters" : 100,
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"poolsize" : [2,2],
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"flatten" : false,
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"update" : true
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},
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{
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"convmat_dim" : [4,4],
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"num_filters" : 200,
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"poolsize" : [2,2],
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"flatten" : true,
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"update" : true
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}
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],
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"comment" : "layers :: convolution layer configuration",
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"cnn": {
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"layers": [
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{
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"convmat_dim" : [5,5],
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"num_filters" : 100,
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"poolsize" : [2,2],
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"flatten" : false,
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"update" : true
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},
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{
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"convmat_dim" : [4,4],
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"num_filters" : 200,
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"poolsize" : [2,2],
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"flatten" : true,
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"update" : true
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}
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],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh",
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh",
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"comment" : "use_fast :: To use pylearn2 library for faster computation or not",
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"use_fast" : false
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},
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"comment" : "use_fast :: To use pylearn2 library for faster computation or not",
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"use_fast" : false
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},
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"comment" : "layers :: hidden layer configuration",
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"mlp" : {
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"comment" : "layers :: hidden layer configuration",
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"mlp" : {
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"layers": [500,200],
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"layers": [500,200],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh"
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}
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "tanh"
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}
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}
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‎config/DBN/data_spec.json‎

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Original file line numberDiff line numberDiff line change
@@ -1,31 +1,34 @@
11
{
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"validation": {
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"base_path" : "data/NPFILE",
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"filename" : "val.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"validation": {
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "val.dat",
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"training" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"training" : {
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "train.dat",
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"testing" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"random" : true,
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"random_seed" : 123,
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"keep_flatten" : true,
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"reader_type" : "NP"
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}
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"testing" : {
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "test.dat",
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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}
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}

‎config/DBN/model_conf.json‎

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@@ -18,10 +18,10 @@
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"batch_size" : 128,
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"comment": "",
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"n_ins":2352,
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"n_ins":784,
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"comment":"",
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"n_outs":200,
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"n_outs":10,
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"comment" :"TODO",
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"gbrbm_learning_rate":0.005,
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"finetune_momentum": 0.5,
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"processes":{
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"pretraining":false,
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"finetuning":false,
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"pretraining":true,
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"finetuning":true,
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"testing":true,
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"export_data":false
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}

‎config/DBN/rbm_spec.json‎

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@@ -2,13 +2,13 @@
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{
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"comment" : "layers :: RBM layer configuration (No: of Nodes)",
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"hidden_layers": [ 2350,1024,1024,1024,1024,1901 ],
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"hidden_layers": [1000, 1000, 1000],
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"comment" : "activation :: sigmoid or tanh",
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"activation" : "sigmoid",
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"comment" : "pretrained_layers:number of layers to be pre-trained",
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"pretrained_layers" : 5,
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"pretrained_layers" : 3,
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"comment" : "first_layer_type::type for the first layer; either 'bb' (Bernoulli-Bernoulli) or 'gb' (Gaussian-Bernoulli)",
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"first_layer_type" : "gb",

‎config/SDA/data_spec.json‎

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@@ -1,30 +1,33 @@
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{
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"validation": {
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"base_path" : "data/NPFILE",
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "val.dat",
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"partition" : 200,
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"training" : {
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"base_path" : "data/NPFILE",
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "train.dat",
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"partition" : 200,
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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},
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"testing" : {
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"base_path" : "data/NPFILE",
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"filename" : "train.dat",
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"partition" : 200,
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"base_path" : "/data/abil/MNIST/NP",
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"filename" : "test.dat",
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"partition" : 100,
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"random" : true,
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"random_seed" : 123,
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"batch_size": 512,
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"keep_flatten" : true,
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"reader_type" : "NP"
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}

‎config/SDA/model_conf.json‎

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@@ -1,58 +1,59 @@
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{
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"comment" : "nnetType :: (Mandatory) specify Type of Network (CNN,SDA) ",
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"nnetType" : "SDA",
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"comment" : "train_data :: (Mandatory) specify the working directory containing data configuration and output ",
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"wdir" : "wdir",
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"comment" : "valid_data (Mandatory) specify the path of the validation data relative to the working directory",
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"data_spec" : "data_spec.json",
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"comment" : "sda_nnet_spec:: (Mandatory) specify the path of SDA network configuration specification relative to working directory",
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"nnet_spec" : "sda_spec.json",
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"comment" : "output_file :: (Mandatory) specify the path of SDA network output file relative to working directory",
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"output_file" : "sda_out.model",
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"comment" : "batch_size :: specify the mini batch size while training, default 128",
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"batch_size" : 128,
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"comment": "",
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"n_ins":2352,
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"comment":"",
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"n_outs":200,
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"comment" :"pretraining_epochs: number of epoch to do pretraining",
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"pretraining_epochs":10,
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"comment" :"learning rate to be used during pre-training",
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"pretrain_lr":0.08,
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"comment" : "finetune_method:: Two methods are supported C: Constant learning rate and E : Exponential decay",
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"finetune_method":"C",
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"comment" : "finetune_rate :: learning rate configuration",
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"finetune_rate" : {
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"learning_rate" : 0.08,
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"epoch_num" : 10,
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"start_rate" : 0.08,
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"scale_by" : 0.5,
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"min_derror_decay_start" : 0.05,
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"min_derror_stop" : 0.05,
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"min_epoch_decay_start" : 15,
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"init_error" :100
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},
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"comment" : "finetune_momentum :: Specify the momentum factor while finetuning",
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"finetune_momentum": 0.5,
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"processes":{
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"pretraining":true,
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"finetuning":true,
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"testing":true,
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"export_data":false
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}
2+
"comment" : "nnetType :: (Mandatory) specify Type of Network (CNN,SDA) ",
3+
"nnetType" : "SDA",
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5+
"comment" : "train_data :: (Mandatory) specify the working directory containing data configuration and output ",
6+
"wdir" : "wdir",
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8+
"comment" : "valid_data (Mandatory) specify the path of the validation data relative to the working directory",
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"data_spec" : "data_spec.json",
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"comment" : "sda_nnet_spec:: (Mandatory) specify the path of SDA network configuration specification relative to working directory",
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"nnet_spec" : "sda_spec.json",
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"comment" : "output_file :: (Mandatory) specify the path of SDA network output file relative to working directory",
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"output_file" : "sda_out.model",
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"logger_level":"INFO",
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"comment" : "batch_size :: specify the mini batch size while training, default 128",
20+
"batch_size" : 128,
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"comment": "",
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"n_ins":784,
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"comment":"",
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"n_outs":10,
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"comment" :"pretraining_epochs: number of epoch to do pretraining",
29+
"pretraining_epochs":10,
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"comment" :"learning rate to be used during pre-training",
32+
"pretrain_lr":0.08,
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34+
"comment" : "finetune_method:: Two methods are supported C: Constant learning rate and E : Exponential decay",
35+
"finetune_method":"C",
36+
37+
"comment" : "finetune_rate :: learning rate configuration",
38+
"finetune_rate" : {
39+
"learning_rate" : 0.08,
40+
"epoch_num" : 10,
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42+
"start_rate" : 0.08,
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"scale_by" : 0.5,
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"min_derror_decay_start" : 0.05,
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"min_derror_stop" : 0.05,
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"min_epoch_decay_start" : 15,
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"init_error" :100
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},
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50+
"comment" : "finetune_momentum :: Specify the momentum factor while finetuning",
51+
"finetune_momentum": 0.5,
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"processes":{
54+
"pretraining":true,
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"finetuning":true,
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"testing":true,
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"export_data":false
58+
}
5859
}

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