зеркало из https://github.com/microsoft/caffe.git
add script to run lenet_consolidated_solver and add comment with results
for first/last 500 iterations
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@ -2,10 +2,10 @@
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# and lenet_test prototxts into a single file. It also adds an additional test
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# net which runs on the training set, e.g., for the purpose of comparing
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# train/test accuracy (accuracy is computed only on the test set in the included
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# LeNet example. This is mainly included as an example of using these features
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# LeNet example). This is mainly included as an example of using these features
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# (specify NetParameters directly in the solver, specify multiple test nets)
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# if desired.
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#
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#
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# Carry out testing every 500 training iterations.
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test_interval: 500
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# The base learning rate, momentum and the weight decay of the network.
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@ -399,3 +399,54 @@ test_net_param {
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top: "accuracy"
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}
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}
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# Expected results for first and last 500 iterations:
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# (with portions of log omitted for brevity)
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#
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# Iteration 0, Testing net (#0)
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# Test score #0: 0.067
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# Test score #1: 2.30256
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# Iteration 0, Testing net (#1)
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# Test score #0: 0.0670334
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# Test score #1: 2.30258
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# Iteration 100, lr = 0.00992565
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# Iteration 100, loss = 0.280585
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# Iteration 200, lr = 0.00985258
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# Iteration 200, loss = 0.345601
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# Iteration 300, lr = 0.00978075
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# Iteration 300, loss = 0.172217
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# Iteration 400, lr = 0.00971013
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# Iteration 400, loss = 0.261836
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# Iteration 500, lr = 0.00964069
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# Iteration 500, loss = 0.157803
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# Iteration 500, Testing net (#0)
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# Test score #0: 0.968
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# Test score #1: 0.0993772
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# Iteration 500, Testing net (#1)
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# Test score #0: 0.965883
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# Test score #1: 0.109374
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#
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# [...]
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#
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# Iteration 9500, Testing net (#0)
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# Test score #0: 0.9899
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# Test score #1: 0.0308299
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# Iteration 9500, Testing net (#1)
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# Test score #0: 0.996816
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# Test score #1: 0.0118238
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# Iteration 9600, lr = 0.00603682
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# Iteration 9600, loss = 0.0126215
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# Iteration 9700, lr = 0.00601382
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# Iteration 9700, loss = 0.00579304
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# Iteration 9800, lr = 0.00599102
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# Iteration 9800, loss = 0.00500633
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# Iteration 9900, lr = 0.00596843
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# Iteration 9900, loss = 0.00796607
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# Iteration 10000, lr = 0.00594604
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# Iteration 10000, loss = 0.00271736
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# Iteration 10000, Testing net (#0)
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# Test score #0: 0.9914
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# Test score #1: 0.0276671
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# Iteration 10000, Testing net (#1)
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# Test score #0: 0.997782
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# Test score #1: 0.00908085
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@ -0,0 +1,5 @@
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#!/usr/bin/env sh
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TOOLS=../../build/tools
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GLOG_logtostderr=1 $TOOLS/train_net.bin lenet_consolidated_solver.prototxt
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