---
title: LeNet MNIST Tutorial
description: Train and test "LeNet" on the MNIST handwritten digit data.
category: example
include_in_docs: true
priority: 1
---
# Training LeNet on MNIST with 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`.
## 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 $CAFFE_ROOT
./data/mnist/get_mnist.sh
./examples/mnist/create_mnist.sh
If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist_train_lmdb`, and `mnist_test_lmdb`.
## LeNet: the MNIST Classification Model
Before we actually run the training program, let's explain what will happen. We will use the [LeNet](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) network, which is known to work well on digit classification tasks. We will use a slightly different version from the original LeNet implementation, replacing the sigmoid activations with Rectified Linear Unit (ReLU) activations for the neurons.
The design of LeNet contains the essence of CNNs that are still used in larger models such as the ones in ImageNet. In general, it consists of a convolutional layer followed by a pooling layer, another convolution layer followed by a pooling layer, and then two fully connected layers similar to the conventional multilayer perceptrons. We have defined the layers in `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt`.
## Define the MNIST Network
This section explains the `lenet_train_test.prototxt` model definition that specifies the LeNet model for MNIST handwritten digit classification. 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 `$CAFFE_ROOT/src/caffe/proto/caffe.proto`.
Specifically, we will write a `caffe::NetParameter` (or in python, `caffe.proto.caffe_pb2.NetParameter`) protobuf. We will start by giving the network a name:
name: "LeNet"
### Writing the Data Layer
Currently, we will read the MNIST data from the lmdb we created earlier in the demo. This is defined by a data layer:
layer {
name: "mnist"
type: "Data"
data_param {
source: "mnist_train_lmdb"
backend: LMDB
batch_size: 64
scale: 0.00390625
}
top: "data"
top: "label"
}
Specifically, this layer has name `mnist`, type `data`, and it reads the data from the given lmdb source. We will use a batch size of 64, and scale the incoming pixels so that they are in the range \[0,1\). Why 0.00390625? It is 1 divided by 256. And finally, this layer produces two blobs, one is the `data` blob, and one is the `label` blob.
### Writing the Convolution Layer
Let's define the first convolution layer:
layer {
name: "conv1"
type: "Convolution"
param { lr_mult: 1 }
param { lr_mult: 2 }
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
bottom: "data"
top: "conv1"
}
This layer takes the `data` blob (it is provided by the data layer), and produces the `conv1` layer. It produces outputs of 20 channels, with the convolutional kernel size 5 and carried out with stride 1.
The fillers allow us to randomly initialize the value of the weights and bias. For the weight filler, we will use the `xavier` algorithm that automatically determines the scale of initialization based on the number of input and output neurons. For the bias filler, we will simply initialize it as constant, with the default filling value 0.
`lr_mult`s are the learning rate adjustments for the layer's learnable parameters. In this case, we will set the weight learning rate to be the same as the learning rate given by the solver during runtime, and the bias learning rate to be twice as large as that - this usually leads to better convergence rates.
### Writing the Pooling Layer
Phew. Pooling layers are actually much easier to define:
layer {
name: "pool1"
type: "Pooling"
pooling_param {
kernel_size: 2
stride: 2
pool: MAX
}
bottom: "conv1"
top: "pool1"
}
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 `$CAFFE_ROOT/examples/mnist/lenet_train_test.prototxt` for details.
### Writing the Fully Connected Layer
Writing a fully connected layer is also simple:
layer {
name: "ip1"
type: "InnerProduct"
param { lr_mult: 1 }
param { lr_mult: 2 }
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
bottom: "pool2"
top: "ip1"
}
This defines a fully connected layer (known in Caffe as an `InnerProduct` layer) with 500 outputs. All other lines look familiar, right?
### Writing the ReLU Layer
A ReLU Layer is also simple:
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
Since ReLU is an element-wise operation, we can do *in-place* operations to save some memory. This is achieved by simply giving the same name to the bottom and top blobs. Of course, do NOT use duplicated blob names for other layer types!
After the ReLU layer, we will write another innerproduct layer:
layer {
name: "ip2"
type: "InnerProduct"
param { lr_mult: 1 }
param { lr_mult: 2 }
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
bottom: "ip1"
top: "ip2"
}
### Writing the Loss Layer
Finally, we will write the loss!
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
}
The `softmax_loss` layer implements both the softmax and the multinomial logistic loss (that saves time and improves numerical stability). It takes two blobs, the first one being the prediction and the second one being the `label` provided by the data layer (remember it?). It does not produce any outputs - all it does is to compute the loss function value, report it when backpropagation starts, and initiates the gradient with respect to `ip2`. This is where all magic starts.
### Additional Notes: Writing Layer Rules
Layer definitions can include rules for whether and when they are included in the network definition, like the one below:
layer {
// ...layer definition...
include: { phase: TRAIN }
}
This is a rule, which controls layer inclusion in the network, based on current network's state.
You can refer to `$CAFFE_ROOT/src/caffe/proto/caffe.proto` for more information about layer rules and model schema.
In the above example, this layer will be included only in `TRAIN` phase.
If we change `TRAIN` with `TEST`, then this layer will be used only in test phase.
By default, that is without layer rules, a layer is always included in the network.
Thus, `lenet_train_test.prototxt` has two `DATA` layers defined (with different `batch_size`), one for the training phase and one for the testing phase.
Also, there is an `Accuracy` layer which is included only in `TEST` phase for reporting the model accuracy every 100 iteration, as defined in `lenet_solver.prototxt`.
## Define the MNIST Solver
Check out the c
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faster-rcnn(matlab)需要用到的caffe文件 （469个子文件） 
roi_pooling_layer.cpp.bak 5KB caffe.pb.cc 943KB gtest_main.cc 2KB caffe.cloc 1KB Utils.cmake 13KB Cuda.cmake 10KB Summary.cmake 7KB Targets.cmake 7KB FindLAPACK.cmake 7KB Dependencies.cmake 5KB ConfigGen.cmake 4KB ProtoBuf.cmake 4KB FindMKL.cmake 3KB FindNumPy.cmake 2KB gflags.cmake 2KB Misc.cmake 2KB FindMatlabMex.cmake 2KB FindLevelDB.cmake 2KB glog.cmake 2KB FindAtlas.cmake 2KB FindGFlags.cmake 2KB FindOpenBLAS.cmake 2KB lint.cmake 1KB FindGlog.cmake 1KB FindvecLib.cmake 1KB FindLMDB.cmake 1KB FindSnappy.cmake 1KB CNAME 25B gtest-all.cpp 329KB test_net.cpp 72KB test_upgrade_proto.cpp 69KB test_pooling_layer.cpp 50KB upgrade_proto.cpp 35KB net.cpp 34KB matcaffe_singlegpu.cpp 33KB test_neuron_layer.cpp 30KB solver.cpp 28KB test_convolution_layer.cpp 27KB test_split_layer.cpp 26KB caffe_.cpp 25KB test_gradient_based_solver.cpp 20KB data_transformer.cpp 17KB test_random_number_generator.cpp 17KB window_data_layer.cpp 17KB test_data_layer.cpp 15KB test_io.cpp 13KB blob.cpp 12KB _caffe.cpp 12KB test_data_transformer.cpp 12KB pooling_layer.cpp 11KB base_conv_layer.cpp 11KB test_memory_data_layer.cpp 11KB lrn_layer.cpp 11KB math_functions.cpp 11KB caffe.cpp 10KB io.cpp 10KB test_reshape_layer.cpp 10KB test_reduction_layer.cpp 9KB test_blob.cpp 9KB test_lrn_layer.cpp 9KB common.cpp 9KB classification.cpp 8KB test_math_functions.cpp 8KB test_eltwise_layer.cpp 8KB test_accuracy_layer.cpp 7KB test_dummy_data_layer.cpp 7KB test_filler.cpp 7KB test_slice_layer.cpp 7KB convert_mnist_data.cpp 7KB spp_layer.cpp 7KB test_concat_layer.cpp 6KB test_image_data_layer.cpp 6KB extract_features.cpp 6KB image_data_layer.cpp 6KB test_stochastic_pooling.cpp 6KB layer_factory.cpp 6KB insert_splits.cpp 6KB test_deconvolution_layer.cpp 6KB hdf5_data_layer.cpp 6KB softmax_loss_layer.cpp 6KB test_argmax_layer.cpp 5KB eltwise_layer.cpp 5KB mvn_layer.cpp 5KB test_contrastive_loss_layer.cpp 5KB test_mvn_layer.cpp 5KB roi_pooling_layer.cpp 5KB test_power_layer.cpp 5KB test_softmax_layer.cpp 5KB inner_product_layer.cpp 5KB convert_imageset.cpp 5KB dummy_data_layer.cpp 5KB prelu_layer.cpp 5KB filter_layer.cpp 5KB test_spp_layer.cpp 5KB test_util_blas.cpp 5KB test_maxpool_dropout_layers.cpp 5KB test_filter_layer.cpp 5KB slice_layer.cpp 4KB memory_data_layer.cpp 4KB test_hdf5data_layer.cpp 4KB共 469 条
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