zhaifly_个人页

net 网络模型：name: 'LeNet'layer { name: 'data' type: 'Input' top: 'data' input_param { shape: { dim: 64 dim: 1 dim: 28 dim: 28 } }}layer { name: 'conv1' type: 'Convolution' bottom: 'data' top: 'conv1' param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 20 kernel_size: 5 stride: 1 weight_filler { type: 'xavier' } bias_filler { type: 'constant' } }}layer { name: 'pool1' type: 'Pooling' bottom: 'conv1' top: 'pool1' pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: 'conv2' type: 'Convolution' bottom: 'pool1' top: 'conv2' param { lr_mult: 1 } param { lr_mult: 2 } convolution_param { num_output: 50 kernel_size: 5 stride: 1 weight_filler { type: 'xavier' } bias_filler { type: 'constant' } }}layer { name: 'pool2' type: 'Pooling' bottom: 'conv2' top: 'pool2' pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: 'ip1' type: 'InnerProduct' bottom: 'pool2' top: 'ip1' param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 500 weight_filler { type: 'xavier' } bias_filler { type: 'constant' } }}layer { name: 'relu1' type: 'ReLU' bottom: 'ip1' top: 'ip1'}layer { name: 'ip2' type: 'InnerProduct' bottom: 'ip1' top: 'ip2' param { lr_mult: 1 } param { lr_mult: 2 } inner_product_param { num_output: 10 weight_filler { type: 'xavier' } bias_filler { type: 'constant' } }}layer { name: 'prob' type: 'Softmax' bottom: 'ip2' top: 'prob'} solver: The train/test net protocol buffer definition net: 'examples/mnist/lenet_train_test.prototxt' test_iter specifies how many forward passes the test should carry out. In the case of MNIST, we have test batch size 100 and 100 test iterations, covering the full 10,000 testing images. test_iter: 100 Carry out testing every 500 training iterations. test_interval: 500 The base learning rate, momentum and the weight decay of the network. base_lr: 0.01momentum: 0.9weight_decay: 0.0005 The learning rate policy lr_policy: 'inv'gamma: 0.0001power: 0.75 Display every 100 iterations display: 100 The maximum number of iterations max_iter: 10000 snapshot intermediate results snapshot: 5000snapshot_prefix: 'examples/mnist/lenet' solver mode: CPU or GPU solver_mode: CPU