CAFFE CIFAR10 MODEL IMAGE 之 cifar10 full
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cifar10_full_train_test.prototxt:
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: "relu1" type: "ReLU" 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: "relu2" type: "ReLU" 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: "relu3" type: "ReLU" 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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