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optimized solver for cifar10/cifar10_fast
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@ -14,11 +14,14 @@ base_lr: 0.001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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lr_policy: "multistep"
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gamma: 0.1
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stepvalue: 60000
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stepvalue: 65000
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 60000
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max_iter: 70000
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# snapshot intermediate results
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snapshot: 10000
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snapshot_format: HDF5
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@ -1,27 +0,0 @@
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
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# then another factor of 10 after 10 more epochs (5000 iters)
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# The train/test net protocol buffer definition
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net: "models/cifar10/cifar10_train_test.prototxt"
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# test_iter specifies how many forward passes the test should carry out.
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# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
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# covering the full 10,000 testing images.
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test_iter: 100
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# Carry out testing every 1000 training iterations.
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test_interval: 1000
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# The base learning rate, momentum and the weight decay of the network.
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base_lr: 0.0001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 65000
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# snapshot intermediate results
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snapshot: 5000
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snapshot_format: HDF5
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snapshot_prefix: "models/cifar10/cifar10"
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# solver mode: CPU or GPU
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solver_mode: GPU
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@ -1,27 +0,0 @@
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
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# then another factor of 10 after 10 more epochs (5000 iters)
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# The train/test net protocol buffer definition
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net: "models/cifar10/cifar10_train_test.prototxt"
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# test_iter specifies how many forward passes the test should carry out.
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# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
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# covering the full 10,000 testing images.
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test_iter: 100
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# Carry out testing every 1000 training iterations.
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test_interval: 1000
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# The base learning rate, momentum and the weight decay of the network.
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base_lr: 0.00001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 70000
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# snapshot intermediate results
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snapshot: 5000
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snapshot_format: HDF5
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snapshot_prefix: "models/cifar10/cifar10"
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# solver mode: CPU or GPU
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solver_mode: GPU
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@ -6,13 +6,3 @@ TOOLS=./caffe/build/tools
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$TOOLS/caffe train \
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--solver=models/cifar10/cifar10_solver.prototxt $@ 2>&1 | tee $DIR/training.log
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# reduce learning rate by factor of 10
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$TOOLS/caffe train \
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--solver=models/cifar10/cifar10_solver_lr1.prototxt \
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--snapshot=models/cifar10/cifar10_iter_60000.solverstate.h5 $@ 2>&1 | tee -a $DIR/training.log
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# reduce learning rate by factor of 10
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$TOOLS/caffe train \
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--solver=models/cifar10/cifar10_solver_lr2.prototxt \
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--snapshot=models/cifar10/cifar10_iter_65000.solverstate.h5 $@ 2>&1 | tee -a $DIR/training.log
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@ -14,11 +14,14 @@ base_lr: 0.001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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lr_policy: "multistep"
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gamma: 0.1
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stepvalue: 60000
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stepvalue: 65000
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 60000
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max_iter: 70000
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# snapshot intermediate results
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snapshot: 10000
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snapshot_format: HDF5
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@ -1,27 +0,0 @@
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
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# then another factor of 10 after 10 more epochs (5000 iters)
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# The train/test net protocol buffer definition
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net: "models/cifar10_fast/cifar10_fast_train_test.prototxt"
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# test_iter specifies how many forward passes the test should carry out.
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# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
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# covering the full 10,000 testing images.
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test_iter: 100
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# Carry out testing every 1000 training iterations.
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test_interval: 1000
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# The base learning rate, momentum and the weight decay of the network.
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base_lr: 0.0001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 65000
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# snapshot intermediate results
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snapshot: 5000
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snapshot_format: HDF5
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snapshot_prefix: "models/cifar10_fast/cifar10_fast"
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# solver mode: CPU or GPU
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solver_mode: GPU
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@ -1,27 +0,0 @@
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# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
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# then another factor of 10 after 10 more epochs (5000 iters)
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# The train/test net protocol buffer definition
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net: "models/cifar10_fast/cifar10_fast_train_test.prototxt"
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# test_iter specifies how many forward passes the test should carry out.
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# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
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# covering the full 10,000 testing images.
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test_iter: 100
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# Carry out testing every 1000 training iterations.
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test_interval: 1000
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# The base learning rate, momentum and the weight decay of the network.
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base_lr: 0.00001
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momentum: 0.9
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weight_decay: 0.004
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# The learning rate policy
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lr_policy: "fixed"
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# Display every 200 iterations
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display: 200
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# The maximum number of iterations
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max_iter: 70000
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# snapshot intermediate results
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snapshot: 5000
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snapshot_format: HDF5
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snapshot_prefix: "models/cifar10_fast/cifar10_fast"
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# solver mode: CPU or GPU
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solver_mode: GPU
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@ -6,13 +6,3 @@ TOOLS=./caffe/build/tools
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$TOOLS/caffe train \
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--solver=models/cifar10_fast/cifar10_fast_solver.prototxt $@ 2>&1 | tee $DIR/training.log
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# reduce learning rate by factor of 10
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$TOOLS/caffe train \
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--solver=models/cifar10_fast/cifar10_fast_solver_lr1.prototxt \
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--snapshot=models/cifar10_fast/cifar10_fast_iter_60000.solverstate.h5 $@ 2>&1 | tee -a $DIR/training.log
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# reduce learning rate by factor of 10
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$TOOLS/caffe train \
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--solver=models/cifar10_fast/cifar10_fast_solver_lr2.prototxt \
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--snapshot=models/cifar10_fast/cifar10_fast_iter_65000.solverstate.h5 $@ 2>&1 | tee -a $DIR/training.log
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