--- /dev/null
+---
+layout: default
+title: Caffe
+---
+
+Alex's CIFAR-10 tutorial, Caffe style
+=====================================
+
+Alex Krizhevsky's [cuda-convnet](https://code.google.com/p/cuda-convnet/) details the model definitions, parameters, and training procedure for good performance on CIFAR-10. This example reproduces his results in Caffe.
+
+We will assume that you have Caffe successfully compiled. If not, please refer to the [Installation page](installation.html). In this tutorial, we will assume that your caffe installation is located at `CAFFE_ROOT`.
+
+We thank @chyojn for the pull request that defined the model schemas and solver configurations.
+
+*This example is a work-in-progress. It would be nice to further explain details of the network and training choices and benchmark the full training.*
+
+Prepare the Dataset
+-------------------
+
+You will first need to download and convert the data format from the [CIFAR-10 website](http://www.cs.toronto.edu/~kriz/cifar.html). To do this, simply run the following commands:
+
+ cd $CAFFE_ROOT/data/cifar10
+ ./get_cifar10.sh
+ cd $CAFFE_ROOT/examples/cifar10
+ ./create_cifar10.sh
+
+If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be the dataset, `./cifar10-leveldb`, and the data set image mean `./mean.binaryproto`.
+
+The Model
+---------
+
+The CIFAR-10 model is a CNN that composes layers of convolution, pooling, rectified linear unit (ReLU) nonlinearities, and local contrast normalization with a linear classifier on top of it all. We have defined the model in the `CAFFE_ROOT/examples/cifar10` directory's `cifar10_quick_train.prototxt`.
+
+Training and Testing the "Quick" Model
+--------------------------------------
+
+Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_quick.sh`, or the following command directly:
+
+ cd $CAFFE_ROOT/examples/cifar10
+ ./train_quick.sh
+
+`train_quick.sh` is a simple script, so have a look inside. `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.
+
+When you run the code, you will see a lot of messages flying by like this:
+
+ I0317 21:52:48.945710 2008298256 net.cpp:74] Creating Layer conv1
+ I0317 21:52:48.945716 2008298256 net.cpp:84] conv1 <- data
+ I0317 21:52:48.945725 2008298256 net.cpp:110] conv1 -> conv1
+ I0317 21:52:49.298691 2008298256 net.cpp:125] Top shape: 100 32 32 32 (3276800)
+ I0317 21:52:49.298719 2008298256 net.cpp:151] conv1 needs backward computation.
+
+These messages tell you the details about each layer, its connections and its output shape, which may be helpful in debugging. After the initialization, the training will start:
+
+ I0317 21:52:49.309370 2008298256 net.cpp:166] Network initialization done.
+ I0317 21:52:49.309376 2008298256 net.cpp:167] Memory required for Data 23790808
+ I0317 21:52:49.309422 2008298256 solver.cpp:36] Solver scaffolding done.
+ I0317 21:52:49.309447 2008298256 solver.cpp:47] Solving CIFAR10_quick_train
+
+Based on the solver setting, we will print the training loss function every 100 iterations, and test the network every 500 iterations. You will see messages like this:
+
+ I0317 21:53:12.179772 2008298256 solver.cpp:208] Iteration 100, lr = 0.001
+ I0317 21:53:12.185698 2008298256 solver.cpp:65] Iteration 100, loss = 1.73643
+ ...
+ I0317 21:54:41.150030 2008298256 solver.cpp:87] Iteration 500, Testing net
+ I0317 21:54:47.129461 2008298256 solver.cpp:114] Test score #0: 0.5504
+ I0317 21:54:47.129500 2008298256 solver.cpp:114] Test score #1: 1.27805
+
+For each training iteration, `lr` is the learning rate of that iteration, and `loss` is the training function. For the output of the testing phase, **score 0 is the accuracy**, and **score 1 is the testing loss function**.
+
+And after making yourself a cup of coffee, you are done!
+
+ I0317 22:12:19.666914 2008298256 solver.cpp:87] Iteration 5000, Testing net
+ I0317 22:12:25.580330 2008298256 solver.cpp:114] Test score #0: 0.7533
+ I0317 22:12:25.580379 2008298256 solver.cpp:114] Test score #1: 0.739837
+ I0317 22:12:25.587262 2008298256 solver.cpp:130] Snapshotting to cifar10_quick_iter_5000
+ I0317 22:12:25.590215 2008298256 solver.cpp:137] Snapshotting solver state to cifar10_quick_iter_5000.solverstate
+ I0317 22:12:25.592813 2008298256 solver.cpp:81] Optimization Done.
+
+Our model achieved ~75% test accuracy. The model parameters are stored in binary protobuf format in
+
+ cifar10_quick_iter_5000
+
+which is ready-to-deploy in CPU or GPU mode! Refer to the `CAFFE_ROOT/examples/cifar10/cifar10_quick.prototxt` for the deployment model definition that can be called on new data.
+
+Why train on a GPU?
+-------------------
+
+CIFAR-10, while still small, has enough data to make GPU training attractive.
+
+To compare CPU vs. GPU training speed, simply change one line in all the `cifar*solver.prototxt`:
+
+ # solver mode: 0 for CPU and 1 for GPU
+ solver_mode: 0
+
+and you will be using CPU for training.
+++ /dev/null
-# CIFAR-10
-
-Contributing a CIFAR-10 example would be welcome! A benchmark against
-cuda-convnet could be interesting too.
+++ /dev/null
-#!/usr/bin/env sh
-
-TOOLS=../../build/tools
-
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_18pct_solver.prototxt
-
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_18pct_solver_lr1.prototxt cifar10_18pct_iter_60000.solverstate
-
-#reduce learning rate by factor of 10
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_18pct_solver_lr2.prototxt cifar10_18pct_iter_65000.solverstate
+++ /dev/null
-#!/usr/bin/env sh
-
-TOOLS=../../build/tools
-
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_80sec_solver.prototxt
-
-#reduce learning rate by fctor of 10 after 8 epochs
-GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_80sec_solver_lr1.prototxt cifar10_80sec_iter_4000.solverstate
--- /dev/null
+name: "CIFAR10_full_deploy"
+# N.B. input image must be in CIFAR-10 format
+# as described at http://www.cs.toronto.edu/~kriz/cifar.html
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 32
+input_dim: 32
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "norm1"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool1"
+ top: "norm1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ blobs_lr: 1.
+ blobs_lr: 2.
+ }
+ bottom: "norm1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+layers {
+ layer {
+ name: "norm2"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool2"
+ top: "norm2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ }
+ bottom: "norm2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 10
+ blobs_lr: 1.
+ blobs_lr: 2.
+ weight_decay: 250.
+ weight_decay: 0.
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "prob"
+ type: "softmax"
+ }
+ bottom: "ip1"
+ top: "prob"
+}
# then another factor of 10 after 10 more epochs (5000 iters)
# The training protocol buffer definition
-train_net: "cifar10_18pct_train.prototxt"
+train_net: "cifar10_full_train.prototxt"
# The testing protocol buffer definition
-test_net: "cifar10_18pct_test.prototxt"
+test_net: "cifar10_full_test.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.
max_iter: 60000
# snapshot intermediate results
snapshot: 10000
-snapshot_prefix: "cifar10_18pct"
+snapshot_prefix: "cifar10_full"
# solver mode: 0 for CPU and 1 for GPU
solver_mode: 1
# then another factor of 10 after 10 more epochs (5000 iters)
# The training protocol buffer definition
-train_net: "cifar10_18pct_train.prototxt"
+train_net: "cifar10_full_train.prototxt"
# The testing protocol buffer definition
-test_net: "cifar10_18pct_test.prototxt"
+test_net: "cifar10_full_test.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.
max_iter: 65000
# snapshot intermediate results
snapshot: 5000
-snapshot_prefix: "cifar10_18pct"
+snapshot_prefix: "cifar10_full"
# solver mode: 0 for CPU and 1 for GPU
solver_mode: 1
# then another factor of 10 after 10 more epochs (5000 iters)
# The training protocol buffer definition
-train_net: "cifar10_18pct_train.prototxt"
+train_net: "cifar10_full_train.prototxt"
# The testing protocol buffer definition
-test_net: "cifar10_18pct_test.prototxt"
+test_net: "cifar10_full_test.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.
max_iter: 70000
# snapshot intermediate results
snapshot: 5000
-snapshot_prefix: "cifar10_18pct"
+snapshot_prefix: "cifar10_full"
# solver mode: 0 for CPU and 1 for GPU
solver_mode: 1
-name: "CIFAR10_18pct_test"\r
-layers {\r
- layer {\r
- name: "cifar"\r
- type: "data"\r
- source: "cifar10-leveldb/cifar-test-leveldb"\r
- meanfile: "mean.binaryproto"\r
- batchsize: 100\r
- }\r
- top: "data"\r
- top: "label"\r
-}\r
-# ------------------------ layer 1 -----------------------------\r
-layers {\r
- layer {\r
- name: "conv1"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.0001\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- }\r
- bottom: "data"\r
- top: "conv1"\r
-}\r
-layers {\r
- layer {\r
- name: "pool1"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: MAX\r
- }\r
- bottom: "conv1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "relu1"\r
- type: "relu"\r
- }\r
- bottom: "pool1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "norm1"\r
- type: "lrn"\r
- local_size: 3\r
- alpha: 0.00005\r
- beta: 0.75\r
- }\r
- bottom: "pool1"\r
- top: "norm1"\r
-}\r
-# --------------------------- layer 2 ------------------------\r
-layers {\r
- layer {\r
- name: "conv2"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- }\r
- bottom: "norm1"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "relu2"\r
- type: "relu"\r
- }\r
- bottom: "conv2"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "pool2"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv2"\r
- top: "pool2"\r
-}\r
-layers {\r
- layer {\r
- name: "norm2"\r
- type: "lrn"\r
- local_size: 3\r
- alpha: 0.00005\r
- beta: 0.75\r
- }\r
- bottom: "pool2"\r
- top: "norm2"\r
-}\r
-#-----------------------layer 3-------------------------\r
-layers {\r
- layer {\r
- name: "conv3"\r
- type: "conv"\r
- num_output: 64\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- }\r
- bottom: "norm2"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "relu3"\r
- type: "relu"\r
- }\r
- bottom: "conv3"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "pool3"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv3"\r
- top: "pool3"\r
-}\r
-#--------------------------layer 4------------------------\r
-layers {\r
- layer {\r
- name: "ip1"\r
- type: "innerproduct"\r
- num_output: 10\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- weight_decay: 250.\r
- weight_decay: 0.\r
- }\r
- bottom: "pool3"\r
- top: "ip1"\r
-}\r
-#-----------------------output------------------------\r
-layers {\r
- layer {\r
- name: "prob"\r
- type: "softmax"\r
- }\r
- bottom: "ip1"\r
- top: "prob"\r
-}\r
-layers {\r
- layer {\r
- name: "accuracy"\r
- type: "accuracy"\r
- }\r
- bottom: "prob"\r
- bottom: "label"\r
- top: "accuracy"\r
-}\r
+name: "CIFAR10_full_test"
+layers {
+ layer {
+ name: "cifar"
+ type: "data"
+ source: "cifar10-leveldb/cifar-test-leveldb"
+ meanfile: "mean.binaryproto"
+ batchsize: 100
+ }
+ top: "data"
+ top: "label"
+}
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "norm1"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool1"
+ top: "norm1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ }
+ bottom: "norm1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+layers {
+ layer {
+ name: "norm2"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool2"
+ top: "norm2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ bottom: "norm2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ weight_decay: 250.
+ weight_decay: 0.
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "prob"
+ type: "softmax"
+ }
+ bottom: "ip1"
+ top: "prob"
+}
+layers {
+ layer {
+ name: "accuracy"
+ type: "accuracy"
+ }
+ bottom: "prob"
+ bottom: "label"
+ top: "accuracy"
+}
-name: "CIFAR10_18pct_train"\r
-layers {\r
- layer {\r
- name: "cifar"\r
- type: "data"\r
- source: "cifar10-leveldb/cifar-train-leveldb"\r
- meanfile: "mean.binaryproto"\r
- batchsize: 100\r
- }\r
- top: "data"\r
- top: "label"\r
-}\r
-# ------------------------ layer 1 -----------------------------\r
-layers {\r
- layer {\r
- name: "conv1"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.0001\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- }\r
- bottom: "data"\r
- top: "conv1"\r
-}\r
-layers {\r
- layer {\r
- name: "pool1"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: MAX\r
- }\r
- bottom: "conv1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "relu1"\r
- type: "relu"\r
- }\r
- bottom: "pool1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "norm1"\r
- type: "lrn"\r
- local_size: 3\r
- alpha: 0.00005\r
- beta: 0.75\r
- }\r
- bottom: "pool1"\r
- top: "norm1"\r
-}\r
-# --------------------------- layer 2 ------------------------\r
-layers {\r
- layer {\r
- name: "conv2"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- }\r
- bottom: "norm1"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "relu2"\r
- type: "relu"\r
- }\r
- bottom: "conv2"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "pool2"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv2"\r
- top: "pool2"\r
-}\r
-layers {\r
- layer {\r
- name: "norm2"\r
- type: "lrn"\r
- local_size: 3\r
- alpha: 0.00005\r
- beta: 0.75\r
- }\r
- bottom: "pool2"\r
- top: "norm2"\r
-}\r
-#-----------------------layer 3-------------------------\r
-layers {\r
- layer {\r
- name: "conv3"\r
- type: "conv"\r
- num_output: 64\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- }\r
- bottom: "norm2"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "relu3"\r
- type: "relu"\r
- }\r
- bottom: "conv3"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "pool3"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv3"\r
- top: "pool3"\r
-}\r
-#--------------------------layer 4------------------------\r
-layers {\r
- layer {\r
- name: "ip1"\r
- type: "innerproduct"\r
- num_output: 10\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.\r
- blobs_lr: 2.\r
- weight_decay: 250.\r
- weight_decay: 0.\r
- }\r
- bottom: "pool3"\r
- top: "ip1"\r
-}\r
-#-----------------------output------------------------\r
-layers {\r
- layer {\r
- name: "loss"\r
- type: "softmax_loss"\r
- }\r
- bottom: "ip1"\r
- bottom: "label"\r
-}\r
+name: "CIFAR10_full_train"
+layers {
+ layer {
+ name: "cifar"
+ type: "data"
+ source: "cifar10-leveldb/cifar-train-leveldb"
+ meanfile: "mean.binaryproto"
+ batchsize: 100
+ }
+ top: "data"
+ top: "label"
+}
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "norm1"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool1"
+ top: "norm1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ }
+ bottom: "norm1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+layers {
+ layer {
+ name: "norm2"
+ type: "lrn"
+ local_size: 3
+ alpha: 0.00005
+ beta: 0.75
+ }
+ bottom: "pool2"
+ top: "norm2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ bottom: "norm2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.
+ blobs_lr: 2.
+ weight_decay: 250.
+ weight_decay: 0.
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "loss"
+ type: "softmax_loss"
+ }
+ bottom: "ip1"
+ bottom: "label"
+}
--- /dev/null
+name: "CIFAR10_quick_test"
+# N.B. input image must be in CIFAR-10 format
+# as described at http://www.cs.toronto.edu/~kriz/cifar.html
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 32
+input_dim: 32
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 64
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#--------------------------layer 5------------------------
+layers {
+ layer {
+ name: "ip2"
+ type: "innerproduct"
+ num_output: 10
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "ip1"
+ top: "ip2"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "prob"
+ type: "softmax"
+ }
+ bottom: "ip2"
+ top: "prob"
+}
# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
# The training protocol buffer definition
-train_net: "cifar10_80sec_train.prototxt"
+train_net: "cifar10_quick_train.prototxt"
# The testing protocol buffer definition
-test_net: "cifar10_80sec_test.prototxt"
+test_net: "cifar10_quick_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.
max_iter: 4000
# snapshot intermediate results
snapshot: 4000
-snapshot_prefix: "cifar10_80sec"
+snapshot_prefix: "cifar10_quick"
# solver mode: 0 for CPU and 1 for GPU
solver_mode: 1
# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10
# The training protocol buffer definition
-train_net: "cifar10_80sec_train.prototxt"
+train_net: "cifar10_quick_train.prototxt"
# The testing protocol buffer definition
-test_net: "cifar10_80sec_test.prototxt"
+test_net: "cifar10_quick_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.
max_iter: 5000
# snapshot intermediate results
snapshot: 5000
-snapshot_prefix: "cifar10_80sec"
+snapshot_prefix: "cifar10_quick"
# solver mode: 0 for CPU and 1 for GPU
solver_mode: 1
-# 80sec config\r
-name: "CIFAR10_80sec_test"\r
-layers {\r
- layer {\r
- name: "cifar"\r
- type: "data"\r
- source: "cifar10-leveldb/cifar-test-leveldb"\r
- meanfile: "mean.binaryproto"\r
- batchsize: 100\r
- }\r
- top: "data"\r
- top: "label"\r
-}\r
-# ------------------------ layer 1 -----------------------------\r
-layers {\r
- layer {\r
- name: "conv1"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.0001\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "data"\r
- top: "conv1"\r
-}\r
-layers {\r
- layer {\r
- name: "pool1"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: MAX\r
- }\r
- bottom: "conv1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "relu1"\r
- type: "relu"\r
- }\r
- bottom: "pool1"\r
- top: "pool1"\r
-}\r
-# --------------------------- layer 2 ------------------------\r
-layers {\r
- layer {\r
- name: "conv2"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool1"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "relu2"\r
- type: "relu"\r
- }\r
- bottom: "conv2"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "pool2"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv2"\r
- top: "pool2"\r
-}\r
-#-----------------------layer 3-------------------------\r
-layers {\r
- layer {\r
- name: "conv3"\r
- type: "conv"\r
- num_output: 64\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool2"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "relu3"\r
- type: "relu"\r
- }\r
- bottom: "conv3"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "pool3"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv3"\r
- top: "pool3"\r
-}\r
-#--------------------------layer 4------------------------\r
-layers {\r
- layer {\r
- name: "ip1"\r
- type: "innerproduct"\r
- num_output: 64 \r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.1\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool3"\r
- top: "ip1"\r
-}\r
-#--------------------------layer 5------------------------\r
-layers {\r
- layer {\r
- name: "ip2"\r
- type: "innerproduct"\r
- num_output: 10\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.1\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "ip1"\r
- top: "ip2"\r
-}\r
-#-----------------------output------------------------\r
-layers {\r
- layer {\r
- name: "prob"\r
- type: "softmax"\r
- }\r
- bottom: "ip2"\r
- top: "prob"\r
-}\r
-layers {\r
- layer {\r
- name: "accuracy"\r
- type: "accuracy"\r
- }\r
- bottom: "prob"\r
- bottom: "label"\r
- top: "accuracy"\r
-}\r
+# quick config
+name: "CIFAR10_quick_test"
+layers {
+ layer {
+ name: "cifar"
+ type: "data"
+ source: "cifar10-leveldb/cifar-test-leveldb"
+ meanfile: "mean.binaryproto"
+ batchsize: 100
+ }
+ top: "data"
+ top: "label"
+}
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 64
+ weight_filler {
+ type: "gaussian"
+ std: 0.1
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#--------------------------layer 5------------------------
+layers {
+ layer {
+ name: "ip2"
+ type: "innerproduct"
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.1
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "ip1"
+ top: "ip2"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "prob"
+ type: "softmax"
+ }
+ bottom: "ip2"
+ top: "prob"
+}
+layers {
+ layer {
+ name: "accuracy"
+ type: "accuracy"
+ }
+ bottom: "prob"
+ bottom: "label"
+ top: "accuracy"
+}
-# 80sec config\r
-name: "CIFAR10_80sec_train"\r
-layers {\r
- layer {\r
- name: "cifar"\r
- type: "data"\r
- source: "cifar10-leveldb/cifar-train-leveldb"\r
- meanfile: "mean.binaryproto"\r
- batchsize: 100\r
- }\r
- top: "data"\r
- top: "label"\r
-}\r
-# ------------------------ layer 1 -----------------------------\r
-layers {\r
- layer {\r
- name: "conv1"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.0001\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "data"\r
- top: "conv1"\r
-}\r
-layers {\r
- layer {\r
- name: "pool1"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: MAX\r
- }\r
- bottom: "conv1"\r
- top: "pool1"\r
-}\r
-layers {\r
- layer {\r
- name: "relu1"\r
- type: "relu"\r
- }\r
- bottom: "pool1"\r
- top: "pool1"\r
-}\r
-# --------------------------- layer 2 ------------------------\r
-layers {\r
- layer {\r
- name: "conv2"\r
- type: "conv"\r
- num_output: 32\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool1"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "relu2"\r
- type: "relu"\r
- }\r
- bottom: "conv2"\r
- top: "conv2"\r
-}\r
-layers {\r
- layer {\r
- name: "pool2"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv2"\r
- top: "pool2"\r
-}\r
-#-----------------------layer 3-------------------------\r
-layers {\r
- layer {\r
- name: "conv3"\r
- type: "conv"\r
- num_output: 64\r
- kernelsize: 5\r
- pad: 2\r
- stride: 1\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.01\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool2"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "relu3"\r
- type: "relu"\r
- }\r
- bottom: "conv3"\r
- top: "conv3"\r
-}\r
-layers {\r
- layer {\r
- name: "pool3"\r
- type: "pool"\r
- kernelsize: 3\r
- stride: 2\r
- pool: AVE\r
- }\r
- bottom: "conv3"\r
- top: "pool3"\r
-}\r
-#--------------------------layer 4------------------------\r
-layers {\r
- layer {\r
- name: "ip1"\r
- type: "innerproduct"\r
- num_output: 64 \r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.1\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "pool3"\r
- top: "ip1"\r
-}\r
-#--------------------------layer 5------------------------\r
-layers {\r
- layer {\r
- name: "ip2"\r
- type: "innerproduct"\r
- num_output: 10\r
- weight_filler {\r
- type: "gaussian"\r
- std: 0.1\r
- }\r
- bias_filler {\r
- type: "constant"\r
- }\r
- blobs_lr: 1.0\r
- blobs_lr: 2.0\r
- }\r
- bottom: "ip1"\r
- top: "ip2"\r
-}\r
-#-----------------------output------------------------\r
-layers {\r
- layer {\r
- name: "loss"\r
- type: "softmax_loss"\r
- }\r
- bottom: "ip2"\r
- bottom: "label"\r
-}\r
+# quick config
+name: "CIFAR10_quick_train"
+layers {
+ layer {
+ name: "cifar"
+ type: "data"
+ source: "cifar10-leveldb/cifar-train-leveldb"
+ meanfile: "mean.binaryproto"
+ batchsize: 100
+ }
+ top: "data"
+ top: "label"
+}
+# ------------------------ layer 1 -----------------------------
+layers {
+ layer {
+ name: "conv1"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.0001
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "data"
+ top: "conv1"
+}
+layers {
+ layer {
+ name: "pool1"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: MAX
+ }
+ bottom: "conv1"
+ top: "pool1"
+}
+layers {
+ layer {
+ name: "relu1"
+ type: "relu"
+ }
+ bottom: "pool1"
+ top: "pool1"
+}
+# --------------------------- layer 2 ------------------------
+layers {
+ layer {
+ name: "conv2"
+ type: "conv"
+ num_output: 32
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool1"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "relu2"
+ type: "relu"
+ }
+ bottom: "conv2"
+ top: "conv2"
+}
+layers {
+ layer {
+ name: "pool2"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv2"
+ top: "pool2"
+}
+#-----------------------layer 3-------------------------
+layers {
+ layer {
+ name: "conv3"
+ type: "conv"
+ num_output: 64
+ kernelsize: 5
+ pad: 2
+ stride: 1
+ weight_filler {
+ type: "gaussian"
+ std: 0.01
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool2"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "relu3"
+ type: "relu"
+ }
+ bottom: "conv3"
+ top: "conv3"
+}
+layers {
+ layer {
+ name: "pool3"
+ type: "pool"
+ kernelsize: 3
+ stride: 2
+ pool: AVE
+ }
+ bottom: "conv3"
+ top: "pool3"
+}
+#--------------------------layer 4------------------------
+layers {
+ layer {
+ name: "ip1"
+ type: "innerproduct"
+ num_output: 64
+ weight_filler {
+ type: "gaussian"
+ std: 0.1
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "pool3"
+ top: "ip1"
+}
+#--------------------------layer 5------------------------
+layers {
+ layer {
+ name: "ip2"
+ type: "innerproduct"
+ num_output: 10
+ weight_filler {
+ type: "gaussian"
+ std: 0.1
+ }
+ bias_filler {
+ type: "constant"
+ }
+ blobs_lr: 1.0
+ blobs_lr: 2.0
+ }
+ bottom: "ip1"
+ top: "ip2"
+}
+#-----------------------output------------------------
+layers {
+ layer {
+ name: "loss"
+ type: "softmax_loss"
+ }
+ bottom: "ip2"
+ bottom: "label"
+}
--- /dev/null
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_full_solver.prototxt
+
+#reduce learning rate by factor of 10
+GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_full_solver_lr1.prototxt cifar10_full_iter_60000.solverstate
+
+#reduce learning rate by factor of 10
+GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_full_solver_lr2.prototxt cifar10_full_iter_65000.solverstate
--- /dev/null
+#!/usr/bin/env sh
+
+TOOLS=../../build/tools
+
+GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver.prototxt
+
+#reduce learning rate by fctor of 10 after 8 epochs
+GLOG_logtostderr=1 $TOOLS/train_net.bin cifar10_quick_solver_lr1.prototxt cifar10_quick_iter_4000.solverstate
projects / platform / upstream / caffe.git / commitdiff