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October 15, 2015 20:44
caffe cifar10 example modified for PReLU
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| # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10 | |
| # then another factor of 10 after 10 more epochs (5000 iters) | |
| # The train/test net protocol buffer definition | |
| net: "examples/cifar10/cifar10_full_train_test_prelu.prototxt" | |
| # test_iter specifies how many forward passes the test should carry out. | |
| # In the case of CIFAR10, we have test batch size 100 and 100 test iterations, | |
| # covering the full 10,000 testing images. | |
| test_iter: 100 | |
| # Carry out testing every 1000 training iterations. | |
| test_interval: 1000 | |
| # The base learning rate, momentum and the weight decay of the network. | |
| base_lr: 0.0001 | |
| momentum: 0.9 | |
| weight_decay: 0.004 | |
| # The learning rate policy | |
| lr_policy: "fixed" | |
| # Display every 200 iterations | |
| display: 200 | |
| # The maximum number of iterations | |
| max_iter: 65000 | |
| # snapshot intermediate results | |
| snapshot: 5000 | |
| snapshot_prefix: "examples/cifar10/cifar10_full_prelu" | |
| # solver mode: CPU or GPU | |
| solver_mode: GPU | |
| device_id: 0 |
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| # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10 | |
| # then another factor of 10 after 10 more epochs (5000 iters) | |
| # The train/test net protocol buffer definition | |
| net: "examples/cifar10/cifar10_full_train_test_prelu.prototxt" | |
| # test_iter specifies how many forward passes the test should carry out. | |
| # In the case of CIFAR10, we have test batch size 100 and 100 test iterations, | |
| # covering the full 10,000 testing images. | |
| test_iter: 100 | |
| # Carry out testing every 1000 training iterations. | |
| test_interval: 1000 | |
| # The base learning rate, momentum and the weight decay of the network. | |
| base_lr: 0.00001 | |
| momentum: 0.9 | |
| weight_decay: 0.004 | |
| # The learning rate policy | |
| lr_policy: "fixed" | |
| # Display every 200 iterations | |
| display: 200 | |
| # The maximum number of iterations | |
| max_iter: 70000 | |
| # snapshot intermediate results | |
| snapshot: 5000 | |
| snapshot_prefix: "examples/cifar10/cifar10_full_prelu" | |
| # solver mode: CPU or GPU | |
| solver_mode: GPU | |
| device_id: 0 |
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| # reduce learning rate after 120 epochs (60000 iters) by factor 0f 10 | |
| # then another factor of 10 after 10 more epochs (5000 iters) | |
| # The train/test net protocol buffer definition | |
| net: "examples/cifar10/cifar10_full_train_test_prelu.prototxt" | |
| # test_iter specifies how many forward passes the test should carry out. | |
| # In the case of CIFAR10, we have test batch size 100 and 100 test iterations, | |
| # covering the full 10,000 testing images. | |
| test_iter: 100 | |
| # Carry out testing every 1000 training iterations. | |
| test_interval: 1000 | |
| # The base learning rate, momentum and the weight decay of the network. | |
| base_lr: 0.001 | |
| momentum: 0.9 | |
| weight_decay: 0.004 | |
| # The learning rate policy | |
| lr_policy: "fixed" | |
| # Display every 200 iterations | |
| display: 200 | |
| # The maximum number of iterations | |
| max_iter: 60000 | |
| # snapshot intermediate results | |
| snapshot: 10000 | |
| snapshot_prefix: "examples/cifar10/cifar10_full_prelu" | |
| # solver mode: CPU or GPU | |
| solver_mode: GPU | |
| device_id: 0 |
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| name: "CIFAR10_full" | |
| layer { | |
| name: "cifar" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mean_file: "examples/cifar10/mean.binaryproto" | |
| } | |
| data_param { | |
| source: "examples/cifar10/cifar10_train_lmdb" | |
| batch_size: 100 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "cifar" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| transform_param { | |
| mean_file: "examples/cifar10/mean.binaryproto" | |
| } | |
| data_param { | |
| source: "examples/cifar10/cifar10_test_lmdb" | |
| batch_size: 100 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.0001 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "prelu1" | |
| type: "PReLU" | |
| bottom: "pool1" | |
| top: "pool1" | |
| } | |
| layer { | |
| name: "norm1" | |
| type: "LRN" | |
| bottom: "pool1" | |
| top: "norm1" | |
| lrn_param { | |
| local_size: 3 | |
| alpha: 5e-05 | |
| beta: 0.75 | |
| norm_region: WITHIN_CHANNEL | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "norm1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "prelu2" | |
| type: "PReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "norm2" | |
| type: "LRN" | |
| bottom: "pool2" | |
| top: "norm2" | |
| lrn_param { | |
| local_size: 3 | |
| alpha: 5e-05 | |
| beta: 0.75 | |
| norm_region: WITHIN_CHANNEL | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "norm2" | |
| top: "conv3" | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| stride: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "prelu3" | |
| type: "PReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: AVE | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "ip1" | |
| type: "InnerProduct" | |
| bottom: "pool3" | |
| top: "ip1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 250 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 10 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "ip1" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { | |
| phase: TEST | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "ip1" | |
| bottom: "label" | |
| top: "loss" | |
| } |
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| #!/usr/bin/env sh | |
| TOOLS=../<change me to point to build dir>/tools | |
| $TOOLS/caffe train \ | |
| --solver=examples/cifar10/cifar10_full_solver_prelu.prototxt | |
| # reduce learning rate by factor of 10 | |
| $TOOLS/caffe train \ | |
| --solver=examples/cifar10/cifar10_full_solver_lr1_prelu.prototxt \ | |
| --snapshot=examples/cifar10/cifar10_full_prelu_iter_60000.solverstate | |
| # reduce learning rate by factor of 10 | |
| $TOOLS/caffe train \ | |
| --solver=examples/cifar10/cifar10_full_solver_lr2_prelu.prototxt \ | |
| --snapshot=examples/cifar10/cifar10_full_prelu_iter_65000.solverstate |
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