Update paths

diff --git a/examples/mnist/readme.md b/examples/mnist/readme.md
index 975a77e..074923d 100644 (file)
--- a/examples/mnist/readme.md
+++ b/examples/mnist/readme.md
@@ -26,7 +26,7 @@ If it complains that `wget` or `gunzip` are not installed, you need to install t
 
 Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
 
-The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `CAFFE_ROOT/examples/lenet.prototxt`.
+The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet.prototxt`.
 
 ## Define the MNIST Network
 
@@ -102,7 +102,7 @@ Phew. Pooling layers are actually much easier to define:
 
 This says we will perform max pooling with a pool kernel size 2 and a stride of 2 (so no overlapping between neighboring pooling regions).
 
-Similarly, you can write up the second convolution and pooling layers. Check `data/lenet.prototxt` for details.
+Similarly, you can write up the second convolution and pooling layers. Check `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt` for details.
 
 ### Writing the Fully Connected Layer
 
@@ -176,7 +176,7 @@ The `softmax_loss` layer implements both the softmax and the multinomial logisti
 
 ## Define the MNIST Solver
 
-Check out the comments explaining each line in the prototxt:
+Check out the comments explaining each line in the prototxt `$CAFFE_ROOT/examples/mnist/lenet_solver.prototxt`:
 
     # The train/test net protocol buffer definition
     net: "lenet_train_test.prototxt"