Caffe Mnist Instance--lenet_train_test.prototxt network configuration detailed

1.mnist instances

# #1. The data download obtains mnist packets and executes the./data/mnist/get_mnist.sh script in the Caffe root directory. The get_mnist.sh script first downloads the sample library and unzip it to get four files.

2. Generate Lmdb

After successfully extracting the downloaded sample library, then execute the./examples/mnist/create_mnist.sh. The create_mnist.sh script first takes advantage of the Convert_mnist_data.bin tool in the caffe-master/build/examples/mnist/directory and will Mnist Data is converted to the Caffe available Lmdb format file, and the resulting mnist-train-lmdb and mnist-test-lmdb two files are placed under the Caffe-master/example/mnist directory.

3. Network Configuration

The Lenet network is defined in the./examples/mnist/lenet_train_test.prototxt file.

Name"LeNet"Layer {Name"Mnist"//Enter the name of the layer MnistType"Data"//The input layer type is dataLayerTop"Data"//Next connection data in this layerLayers and label blobsSpaceTop"Label"Include {Phase:train//Training Stage}Transform_param {Scale0.00390625//Input picture pixels to [0,1].1Divide by 256.Is 0.00390625}Data_param {Source"Examples/mnist/mnist_train_lmdb"//From Mnist_train_lmdbRead into data inBatch_size:64BatchSize is 64, one training 64Bar dataBackend:lmdb}}Layer {Name"Mnist"//Enter the name of the layer MnistType"Data"//The input layer type is dataLayerTop"Data"//Next connection data in this layerLayers and label blobsSpaceTop"Label"Include {Phase:test//Test phase}Transform_param {Scale0.00390625//Input picture pixels to [0,1].1Divide by 256.Is 0.00390625}Data_param {Source"Examples/mnist/mnist_test_lmdb"//From Mnist_test_lmdbRead into data inBatch_size:100BatchSize is 100, one training 100Bar dataBackend:lmdb}}Layer {Name"Conv1"//convolutional Layer Name Conv1Type"Convolution"//Layer type is convolution layerBottom"Data"//This layer uses the data from the previous layerTo generate the next layer of CONV1The BlobTop"Conv1"param {Lr_mult:1//Weight parameter WThe Learning rate Multiple}param {Lr_mult:2//Offset parameter BThe Learning rate Multiple}Convolution_param {Num_output:20//Number of output units 20Kernel_size:5//Convolution core size is 5*5Stride1//Step size is 1Weight_filler {//Allows the use of random values to initialize weights and bias valuesType"Xavier"//Using XavierThe algorithm automatically determines the initial size based on the number of input-output neurons}Bias_filler {Type"Constant"//Offset value initialized to constant, default = 0}}Layer {Name"Pool1"//Layer name is Pool1Type"Pooling"//Layer type is poolingBottom"Conv1"//The upper layer of this layer is CONV1To generate the next layer of pool1The BlobTop"Pool1"Pooling_param {PoolingThe parameters of the layerPool:maxPoolingIs the way that MaxKernel_size:2PoolingNuclear is 2*2.Stride2PoolingStep is 2}}Layer {Name"Conv2"//The second convolutional layer is the same as the first convolutional layer, except that the convolution core is 50Type"Convolution"Bottom"Pool1"Top"Conv2"param {Lr_mult:1}param {Lr_mult:2}Convolution_param {Num_output:50Kernel_size:5Stride1Weight_filler {Type"Xavier"}Bias_filler {Type"Constant"}}}Layer {Name"Pool2"//A second poolingLayer, with the first poolingSame layerType"Pooling"Bottom"Conv2"Top"Pool2"Pooling_param {Pool:maxKernel_size:2Stride2}}Layer {//Fully connected LayerName"Ip1"//Full Connection layer name Ip1Type"Innerproduct"//Layer type is fully connected layerBottom"Pool2"Top"Ip1"param {Lr_mult:1}param {Lr_mult:2}Inner_product_param {//Parameters of the fully connected layerNum_output:500//Output 500A nodeWeight_filler {Type"Xavier"}Bias_filler {Type"Constant"}}}Layer {Name"RELU1"ReLULayerType"ReLU"//Layer name is RELU1Bottom"Ip1"//Layer type is ReluTop"Ip1"}Layer {Name"IP2"//Second fully connected layerType"Innerproduct"Bottom"Ip1"Top"IP2"param {Lr_mult:1}param {Lr_mult:2}Inner_product_param {Num_output:10//Output 10Units ofWeight_filler {Type"Xavier"}Bias_filler {Type"Constant"}}}Layer {Name"Accuracy"Type"Accuracy"bottom: " ip2" bottom: " label" Top: "accuracy" include { phase:test}}layer { //l OSS layer, Softmax_loss layer realizes Softmax and multiple logistic losses name: " loss" type: " Softmaxwithloss " bottom: " ip2 " Bottom: " label " Top: " loss "}    

4. Training Network

Run./examples/mnist/train_lenet.sh. The execution of this script is that the definition in Lenet_solver.prototxt is actually running.

# The Train/test net protocol buffer definitionNet"Examples/mnist/lenet_train_test.prototxt"//Network Specific definition# TEST_ITER Specifies how many forward passes the test should carry out.# in the case of MNIST, we have test batch size and test iterations,# covering the full testing images.Test_iter:100TestNumber of iterations, if batch_size=100, then 100A batch of pictures, training 100Times, can cover 1000Sketch# Carry out testing every training iterations.Test_interval:500//Training iterations 500Times, Test once# The base learning rate, momentum and the weight decay of the network.BASE_LR:0.01//Network parameters: Learning rate, momentum, weight attenuationMomentum0.9Weight_decay:0.0005# The Learning rate policy//Learning Strategies: Having a fixed learning rate and decreasing learning rate per stepLr_policy:"INV"//Decreasing learning rate for current useGamma0.0001Power0.75# Display every iterations// every iteration 100 times show 100# the maximum number of iterations // maximum iteration algebra max_iter: 10000< Span class= "Hljs-meta" ># snapshot Intermediate results // every 5000 Span class= "Zh-hans" > iterations store data snapshot: 5000snapshot_prefix: " examples/mnist/lenet "# Solver Mode:cpu or Gpusolver_mode:cpu // This example uses Cpu training            

After the data training is finished, the following four files are generated:

5. Test the network

Run./build/tools/caffe.bin Test-model=examples/mnist/lenet_train_test.prototxt-weights=examples/mnist/lenet_iter _10000.caffemodel

Test: Indicates a testing, not a training, of a well-trained model. Other parameters include train, time, Device_query.

-MODEL=XXX: Specifies the model Prototxt file, which is a text file detailing the network structure and data set information.

As can be seen from the printout above, the average success rate of accruacy in the test data is 98%.

mnist handwriting test handwritten number pictures must meet the following conditions:

• Must be 256 bits black and white
• Must be white on the black background
• The pixel size must be 28*28
• Number in the middle of the picture, up and down there is no too much blank.

Test picture

Handwritten digit recognition script

Import OSImport SysImport NumPyAs NPimport Matplotlib.pyplot as pltcaffe_root = '/home/lynn/caffe/' sys.  Path.insert (0, caffe_root + ' python ')import caffemodel_file = '/home/lynn/caffe/examples/mnist/ Lenet.prototxt ' pretrained = ' /home/lynn/caffe/examples/mnist/lenet_iter_10000.caffemodel ' IMAGE_FILE = '/ Home/lynn/test.bmp ' input_image = Caffe.io.load_image (image_file, color=false) #print input_imagenet = Caffe. Classifier (Model_file, pretrained) prediction = Net.predict ([input_image], oversample = False) caffe.set_mode_cpu () Print ' predicted class: ', prediction[0].argmax ()          

Test results

Caffe Mnist Instance--lenet_train_test.prototxt network configuration detailed