Deep learning framework by Created by Lead Developer • Interfaces Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping. While Caffe is a C++ library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation. The cmdcaffe, pycaffe, and matcaffe interfaces are here for you. Command Line The command line interface – cmdcaffe – is the caffe tool for model training, scoring, and diagnostics.
Deep learning toolboxes and Caffe. Learn more about deep learning, cnn, convolutional neural networks, caffe, matlab.
Run caffe without any arguments for help. This tool and others are found in caffe/build/tools. (The following example calls require completing the LeNet / MNIST example first.) Training: caffe train learns models from scratch, resumes learning from saved snapshots, and fine-tunes models to new data and tasks: • All training requires a solver configuration through the -solver solver.prototxt argument. • Resuming requires the -snapshot model_iter_1000.solverstate argument to load the solver snapshot. • Fine-tuning requires the -weights model.caffemodel argument for the model initialization.
For example, you can run. # train on GPUs 0 & 1 (doubling the batch size) caffe train -solver examples/mnist/lenet_solver.prototxt -gpu 0,1 # train on all GPUs (multiplying batch size by number of devices) caffe train -solver examples/mnist/lenet_solver.prototxt -gpu all Python The Python interface – pycaffe – is the caffe module and its scripts in caffe/python.
Import caffe to load models, do forward and backward, handle IO, visualize networks, and even instrument model solving. All model data, derivatives, and parameters are exposed for reading and writing. • caffe.Net is the central interface for loading, configuring, and running models. Caffe.Classifier and caffe.Detector provide convenience interfaces for common tasks. • caffe.SGDSolver exposes the solving interface. • caffe.io handles input / output with preprocessing and protocol buffers.
• caffe.draw visualizes network architectures. • Caffe blobs are exposed as numpy ndarrays for ease-of-use and efficiency. Tutorial IPython notebooks are found in caffe/examples: do ipython notebook caffe/examples to try them.
For developer reference docstrings can be found throughout the code. Compile pycaffe by make pycaffe. Add the module directory to your $PYTHONPATH by export PYTHONPATH=/path/to/caffe/python:$PYTHONPATH or the like for import caffe. MATLAB The MATLAB interface – matcaffe – is the caffe package in caffe/matlab in which you can integrate Caffe in your Matlab code.
Export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/lib64 export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6 Or the equivalent based on where things are installed on your system, and do make mattest again to see if the issue is fixed. Note: this issue is sometimes more complicated since during its startup Matlab may overwrite your LD_LIBRARY_PATH environment variable. You can run!ldd./matlab/+caffe/private/caffe_.mexa64 (the mex extension may differ on your system) in Matlab to see its runtime libraries, and preload your compile-time libraries by exporting them to your LD_PRELOAD environment variable. After successful building and testing, add this package to Matlab search PATH by starting matlab from caffe root folder and running the following commands in Matlab command window. Net.blobs('data').set_data(net.blobs('data').get_data() * 10); Be aware that since Matlab is 1-indexed and column-major, the usual 4 blob dimensions in Matlab are [width, height, channels, num], and width is the fastest dimension. Also be aware that images are in BGR channels. Also, Caffe uses single-precision float data.
D33011 Ati Drivers. If your data is not single, set_data will automatically convert it to single. You also have access to every layer, so you can do network surgery. For example, to multiply conv1 parameters by 10.
Layer_type = net.layers('conv1').type; Forward and backward Forward pass can be done using net.forward or net.forward_prefilled. Function net.forward takes in a cell array of N-D arrays containing data of input blob(s) and outputs a cell array containing data from output blob(s). Function net.forward_prefilled uses existing data in input blob(s) during forward pass, takes no input and produces no output.
After creating some data for input blobs like data = rand(net.blobs('data').shape); you can run. Net.blobs('prob').set_diff(prob_diff); net.backward_prefilled(); data_diff = net.blobs('data').get_diff(); However, the backward computation above doesn’t get correct results, because Caffe decides that the network does not need backward computation. To get correct backward results, you need to set 'force_backward: true' in your network prototxt. After performing forward or backward pass, you can also get the data or diff in internal blobs.
For example, to extract pool5 features after forward pass. Im_data = imread('./examples/images/cat.jpg');% read image im_data = im_data(:,:, [3, 2, 1]);% convert from RGB to BGR im_data = permute(im_data, [2, 1, 3]);% permute width and height im_data = single(im_data);% convert to single precision Also, you may take a look at caffe/matlab/demo/classification_demo.m to see how to prepare input by taking crops from an image. We show in caffe/matlab/hdf5creation how to read and write HDF5 data with Matlab. We do not provide extra functions for data output as Matlab itself is already quite powerful in output. Clear nets and solvers Call caffe.reset_all() to clear all solvers and stand-alone nets you have created.