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March 14, 2016 19:17
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| name: "MNISTAutoencoder" | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| scale: 0.0039215684 | |
| } | |
| data_param { | |
| source: "examples/mnist/mnist_train_lmdb" | |
| batch_size: 100 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| stage: "test-on-train" | |
| } | |
| transform_param { | |
| scale: 0.0039215684 | |
| } | |
| data_param { | |
| source: "examples/mnist/mnist_train_lmdb" | |
| batch_size: 100 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| stage: "test-on-test" | |
| } | |
| transform_param { | |
| scale: 0.0039215684 | |
| } | |
| data_param { | |
| source: "examples/mnist/mnist_test_lmdb" | |
| batch_size: 100 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "flatdata" | |
| type: "Flatten" | |
| bottom: "data" | |
| top: "flatdata" | |
| } | |
| layer { | |
| name: "encode1" | |
| type: "InnerProduct" | |
| bottom: "data" | |
| top: "encode1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1000 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "encode1neuron" | |
| type: "Sigmoid" | |
| bottom: "encode1" | |
| top: "encode1neuron" | |
| } | |
| layer { | |
| name: "encode2" | |
| type: "InnerProduct" | |
| bottom: "encode1neuron" | |
| top: "encode2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 500 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "encode2neuron" | |
| type: "Sigmoid" | |
| bottom: "encode2" | |
| top: "encode2neuron" | |
| } | |
| layer { | |
| name: "encode3" | |
| type: "InnerProduct" | |
| bottom: "encode2neuron" | |
| top: "encode3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 250 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "encode3neuron" | |
| type: "Sigmoid" | |
| bottom: "encode3" | |
| top: "encode3neuron" | |
| } | |
| layer { | |
| name: "encode4" | |
| type: "InnerProduct" | |
| bottom: "encode3neuron" | |
| top: "encode4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 30 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "decode4" | |
| type: "InnerProduct" | |
| bottom: "encode4" | |
| top: "decode4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 250 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "decode4neuron" | |
| type: "Sigmoid" | |
| bottom: "decode4" | |
| top: "decode4neuron" | |
| } | |
| layer { | |
| name: "decode3" | |
| type: "InnerProduct" | |
| bottom: "decode4neuron" | |
| top: "decode3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 500 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "decode3neuron" | |
| type: "Sigmoid" | |
| bottom: "decode3" | |
| top: "decode3neuron" | |
| } | |
| layer { | |
| name: "decode2" | |
| type: "InnerProduct" | |
| bottom: "decode3neuron" | |
| top: "decode2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 1000 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "decode2neuron" | |
| type: "Sigmoid" | |
| bottom: "decode2" | |
| top: "decode2neuron" | |
| } | |
| layer { | |
| name: "decode1" | |
| type: "InnerProduct" | |
| bottom: "decode2neuron" | |
| top: "decode1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 784 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 1 | |
| sparse: 15 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SigmoidCrossEntropyLoss" | |
| bottom: "decode1" | |
| bottom: "flatdata" | |
| top: "cross_entropy_loss" | |
| loss_weight: 1 | |
| } | |
| layer { | |
| name: "decode1neuron" | |
| type: "Sigmoid" | |
| bottom: "decode1" | |
| top: "decode1neuron" | |
| } | |
| layer { | |
| name: "loss" | |
| type: "EuclideanLoss" | |
| bottom: "decode1neuron" | |
| bottom: "flatdata" | |
| top: "l2_error" | |
| loss_weight: 0 | |
| } |
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