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[examples] sequence and revise notebooks
- combine classification + filter visualization - order by classification, learning LeNet, brewing logreg, and fine-tuning to flickr style - improve flow of content in classification + filter visualization - include solver needed for learning LeNet - edit notebook descriptions for site catalogue
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‎examples/00-classification.ipynb

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‎examples/python_solving.ipynb ‎examples/01-learning-lenet.ipynb

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‎examples/02-brewing-logreg.ipynb

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‎examples/03-fine-tuning.ipynb

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‎examples/Finetune with Flickr Style Data.ipynb

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‎examples/classification.ipynb

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‎examples/detection.ipynb

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"pygments_lexer": "ipython2",
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"version": "2.7.9"
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},
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"priority": 3
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"priority": 6
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},
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"nbformat": 4,
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"nbformat_minor": 0

‎examples/filter_visualization.ipynb

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‎examples/hdf5_classification.ipynb

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‎examples/hdf5_classification/nonlinear_auto_test.prototxt

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layer {
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name: "data"
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type: "HDF5Data"
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top: "data"
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top: "label"
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hdf5_data_param {
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source: "examples/hdf5_classification/data/test.txt"
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batch_size: 10
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}
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}
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layer {
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name: "ip1"
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type: "InnerProduct"
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bottom: "data"
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top: "ip1"
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inner_product_param {
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num_output: 40
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weight_filler {
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type: "xavier"
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "ip1"
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top: "ip1"
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}
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layer {
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name: "ip2"
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type: "InnerProduct"
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bottom: "ip1"
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top: "ip2"
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inner_product_param {
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num_output: 2
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weight_filler {
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type: "xavier"
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}
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}
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}
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layer {
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name: "accuracy"
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type: "Accuracy"
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bottom: "ip2"
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bottom: "label"
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top: "accuracy"
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}
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layer {
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name: "loss"
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type: "SoftmaxWithLoss"
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bottom: "ip2"
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bottom: "label"
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top: "loss"
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}

‎examples/hdf5_classification/nonlinear_auto_train.prototxt

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layer {
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name: "data"
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type: "HDF5Data"
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top: "data"
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top: "label"
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hdf5_data_param {
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source: "examples/hdf5_classification/data/train.txt"
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batch_size: 10
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}
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}
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layer {
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name: "ip1"
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type: "InnerProduct"
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bottom: "data"
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top: "ip1"
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inner_product_param {
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num_output: 40
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weight_filler {
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type: "xavier"
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}
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}
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}
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layer {
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name: "relu1"
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type: "ReLU"
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bottom: "ip1"
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top: "ip1"
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}
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layer {
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name: "ip2"
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type: "InnerProduct"
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bottom: "ip1"
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top: "ip2"
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inner_product_param {
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num_output: 2
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weight_filler {
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type: "xavier"
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}
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}
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}
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layer {
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name: "accuracy"
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type: "Accuracy"
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bottom: "ip2"
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bottom: "label"
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top: "accuracy"
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}
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layer {
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name: "loss"
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type: "SoftmaxWithLoss"
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bottom: "ip2"
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bottom: "label"
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top: "loss"
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}

‎examples/hdf5_classification/nonlinear_solver.prototxt

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train_net: "examples/hdf5_classification/nonlinear_auto_train.prototxt"
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test_net: "examples/hdf5_classification/nonlinear_auto_test.prototxt"
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test_iter: 250
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test_interval: 1000
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base_lr: 0.01
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lr_policy: "step"
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gamma: 0.1
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stepsize: 5000
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display: 1000
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max_iter: 10000
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momentum: 0.9
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weight_decay: 0.0005
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snapshot: 10000
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snapshot_prefix: "examples/hdf5_classification/data/train"
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solver_mode: CPU

‎examples/hdf5_classification/train_val2.prototxt ‎examples/hdf5_classification/nonlinear_train_val.prototxt

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phase: TRAIN
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}
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hdf5_data_param {
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source: "hdf5_classification/data/train.txt"
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source: "examples/hdf5_classification/data/train.txt"
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batch_size: 10
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}
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}
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phase: TEST
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}
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hdf5_data_param {
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source: "hdf5_classification/data/test.txt"
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source: "examples/hdf5_classification/data/test.txt"
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batch_size: 10
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}
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}
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inner_product_param {
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num_output: 40
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weight_filler {
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type: "gaussian"
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std: 0.01
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type: "xavier"
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}
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bias_filler {
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type: "constant"
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inner_product_param {
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num_output: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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type: "xavier"
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}
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bias_filler {
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type: "constant"

‎examples/hdf5_classification/solver.prototxt

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net: "hdf5_classification/train_val.prototxt"
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train_net: "examples/hdf5_classification/logreg_auto_train.prototxt"
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test_net: "examples/hdf5_classification/logreg_auto_test.prototxt"
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test_iter: 250
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test_interval: 1000
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base_lr: 0.01
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momentum: 0.9
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weight_decay: 0.0005
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snapshot: 10000
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snapshot_prefix: "hdf5_classification/data/train"
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snapshot_prefix: "examples/hdf5_classification/data/train"
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solver_mode: CPU

‎examples/hdf5_classification/solver2.prototxt

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‎examples/hdf5_classification/train_val.prototxt

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phase: TRAIN
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}
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hdf5_data_param {
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source: "hdf5_classification/data/train.txt"
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source: "examples/hdf5_classification/data/train.txt"
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batch_size: 10
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}
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}
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phase: TEST
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}
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hdf5_data_param {
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source: "hdf5_classification/data/test.txt"
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source: "examples/hdf5_classification/data/test.txt"
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batch_size: 10
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}
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}
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inner_product_param {
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num_output: 2
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weight_filler {
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type: "gaussian"
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std: 0.01
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type: "xavier"
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}
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bias_filler {
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type: "constant"

‎examples/mnist/lenet_auto_solver.prototxt

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# The train/test net protocol buffer definition
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train_net: "examples/mnist/lenet_auto_train.prototxt"
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test_net: "examples/mnist/lenet_auto_test.prototxt"
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# test_iter specifies how many forward passes the test should carry out.
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# In the case of MNIST, we have test batch size 100 and 100 test iterations,
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# covering the full 10,000 testing images.
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test_iter: 100
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# Carry out testing every 500 training iterations.
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test_interval: 500
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# The base learning rate, momentum and the weight decay of the network.
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base_lr: 0.01
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momentum: 0.9
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weight_decay: 0.0005
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# The learning rate policy
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lr_policy: "inv"
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gamma: 0.0001
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power: 0.75
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# Display every 100 iterations
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display: 100
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# The maximum number of iterations
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max_iter: 10000
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# snapshot intermediate results
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snapshot: 5000
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snapshot_prefix: "examples/mnist/lenet"

‎examples/net_surgery.ipynb

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}
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],
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"metadata": {
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"description": "How to do net surgery and manually change model parameters, making a fully-convolutional classifier for dense feature extraction.",
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"description": "How to do net surgery and manually change model parameters for custom use.",
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"example_name": "Editing model parameters",
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"include_in_docs": true,
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"kernelspec": {

‎examples/siamese/mnist_siamese.ipynb

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"pygments_lexer": "ipython2",
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"version": "2.7.9"
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},
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"priority": 6
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"priority": 7
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},
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"nbformat": 4,
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"nbformat_minor": 0

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