bring mnist docs back to reality

winstywang · Feb 26, 2014 · cd6df9e · cd6df9e
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diff --git a/docs/mnist.md b/docs/mnist.md
@@ -13,8 +13,10 @@ Prepare Datasets
 
 You will first need to download and convert the data format from the MNIST website. To do this, simply run the following commands:
 
-    cd $CAFFE_ROOT/data
+    cd $CAFFE_ROOT/data/mnist
     ./get_mnist.sh
+    cd $CAFFE_ROOT/examples/lenet
+    ./create_mnist.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `CAFFE_ROOT/data/mnist-train-leveldb`, and `CAFFE_ROOT/data/mnist-test-leveldb`.
 
@@ -31,10 +33,10 @@ Training and Testing the Model
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_mnist.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/data
-    GLOG_logtostderr=1 ../examples/train_net.bin lenet_solver.prototxt
+    cd $CAFFE_ROOT/examples/lenet
+    ./train_lenet.sh
 
-A few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main executable for training is `examples/train_net.bin`, with the solver protobuf text file as its argument.
+`train_lenet.sh` is a simple script, but here are a few explanations: `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:
 
@@ -79,7 +81,7 @@ which you can deploy as a trained model in your application, if you are training
 Um... How about GPU training?
 -----------------------------
 
-You just did! All the training were carried out on the GPU. In fact, if you would like to do training on CPU, you can simply change one line in `lenet_solver.prototxt`:
+You just did! All the training was carried out on the GPU. In fact, if you would like to do training on CPU, you can simply change one line in `lenet_solver.prototxt`:
 
     # solver mode: 0 for CPU and 1 for GPU
     solver_mode: 0

diff --git a/docs/mnist_prototxt.md b/docs/mnist_prototxt.md
@@ -6,7 +6,7 @@ title: Caffe
 Define the MNIST Network
 =========================
 
-This page explains the prototxt file used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).
+This page explains the prototxt file `lenet_train.prototxt` used in the MNIST demo. We assume that you are familiar with [Google Protobuf](https://developers.google.com/protocol-buffers/docs/overview), and assume that you have read the protobuf definitions used by Caffe, which can be found at [src/caffe/proto/caffe.proto](https://github.com/Yangqing/caffe/blob/master/src/caffe/proto/caffe.proto).
 
 Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protubuf. We will start by giving the network a name:
 

diff --git a/docs/mnist_solver_prototxt.md b/docs/mnist_solver_prototxt.md
@@ -9,7 +9,7 @@ Define the MNIST Solver
 The page is under construction. For now, check out the comments in the solver prototxt file, which explains each line in the prototxt:
 
     # The training protocol buffer definition
-    train_net: "lenet.prototxt"
+    train_net: "lenet_train.prototxt"
     # The testing protocol buffer definition
     test_net: "lenet_test.prototxt"
     # test_iter specifies how many forward passes the test should carry out.