Add siamese label functionality to hinge

examples/mnist/lenet_import_conv_pool.prototxt

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+layers {
+  name: "conv"
+  type: CONVOLUTION
+  bottom: "${bottom}"
+  top: "conv"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: ${num_output}
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "pool"
+  type: POOLING
+  bottom: "conv"
+  top: "pool"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}

examples/mnist/lenet_import_solver.prototxt

@@ -0,0 +1,25 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_import_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+solver_mode: GPU

examples/mnist/lenet_import_train_test.prototxt

@@ -0,0 +1,104 @@
+name: "LeNet"
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    backend: LMDB
+    batch_size: 64
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    backend: LMDB
+    batch_size: 100
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  include: { phase: TEST }
+}
+layers {
+  name: "cp1"
+  type: IMPORT
+  import_param {
+    net: "examples/mnist/lenet_import_conv_pool.prototxt"
+    var { name: "bottom" value: "/data" }
+    var { name: "num_output" value: "20" }
+  }
+}
+layers {
+  name: "cp2"
+  type: IMPORT
+  import_param {
+    net: "examples/mnist/lenet_import_conv_pool.prototxt"
+    var { name: "bottom" value: "../cp1/pool" }
+    var { name: "num_output" value: "50" }
+  }
+}
+layers {
+  name: "ip1"
+  type: INNER_PRODUCT
+  bottom: "cp2/pool"
+  top: "ip1"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "ip1"
+  top: "ip1"
+}
+layers {
+  name: "ip2"
+  type: INNER_PRODUCT
+  bottom: "ip1"
+  top: "ip2"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}

examples/mnist/lenet_local_solver.prototxt

@@ -0,0 +1,25 @@
+# The train/test net protocol buffer definition
+net: "examples/mnist/lenet_local_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0005
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 10000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/mnist/lenet"
+# solver mode: CPU or GPU
+solver_mode: GPU

examples/mnist/lenet_local_train_test.prototxt

@@ -0,0 +1,171 @@
+name: "LeNet"
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/mnist/mnist_train_lmdb"
+    backend: LMDB
+    batch_size: 64
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  include: { phase: TRAIN }
+}
+layers {
+  name: "mnist"
+  type: DATA
+  top: "data"
+  top: "label"
+  data_param {
+    source: "examples/mnist/mnist_test_lmdb"
+    backend: LMDB
+    batch_size: 100
+  }
+  transform_param {
+    scale: 0.00390625
+  }
+  include: { phase: TEST }
+}
+
+layers {
+  name: "conv1"
+  type: CONVOLUTION
+  bottom: "data"
+  top: "conv1"
+  blobs_lr: 1
+  blobs_lr: 2
+  convolution_param {
+    num_output: 20
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "pool1"
+  type: POOLING
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+  }
+}
+layers {
+  name: "local1"
+  type: LOCAL
+  bottom: "pool1"
+  top: "local1"
+  blobs_lr: 1
+  blobs_lr: 1
+  local_param {
+    num_output: 5
+    kernel_size: 5
+    stride: 1
+    pad: 0
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layers {
+  name: "relu1"
+  type: RELU
+  bottom: "local1"
+  top: "local1"
+}
+layers {
+  name: "local2"
+  type: LOCAL
+  bottom: "local1"
+  top: "local2"
+  blobs_lr: 1
+  blobs_lr: 1
+  local_param {
+    num_output: 10
+    kernel_size: 5
+    stride: 1
+    pad: 0
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+      value: 0.1
+    }
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "local2"
+  top: "local2"
+}
+layers {
+  name: "ip1"
+  type: INNER_PRODUCT
+  bottom: "local2"
+  top: "ip1"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 500
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "relu2"
+  type: RELU
+  bottom: "ip1"
+  top: "ip1"
+}
+layers {
+  name: "ip2"
+  type: INNER_PRODUCT
+  bottom: "ip1"
+  top: "ip2"
+  blobs_lr: 1
+  blobs_lr: 2
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "xavier"
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layers {
+  name: "accuracy"
+  type: ACCURACY
+  bottom: "ip2"
+  bottom: "label"
+  top: "accuracy"
+  include: { phase: TEST }
+}
+layers {
+  name: "loss"
+  type: SOFTMAX_LOSS
+  bottom: "ip2"
+  bottom: "label"
+  top: "loss"
+}

examples/mnist/train_lenet.sh

@@ -1,3 +1,3 @@
 #!/usr/bin/env sh
 
-./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt
+GLOG_logtostderr=0 GLOG_log_dir=examples/mnist/ ./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt

examples/mnist/train_lenet_import.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+GLOG_logtostderr=0 GLOG_log_dir=examples/mnist/ ./build/tools/caffe train --solver=examples/mnist/lenet_import_solver.prototxt --gpu=1

examples/mnist/train_lenet_local.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env sh
+
+GLOG_logtostderr=0 GLOG_log_dir=examples/mnist/ ./build/tools/caffe train --solver=examples/mnist/lenet_local_solver.prototxt --gpu=1

examples/siamese/mnist_siamese_local_solver.prototxt

@@ -0,0 +1,25 @@
+# The train/test net protocol buffer definition
+net: "examples/siamese/mnist_siamese_local_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of MNIST, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 100
+# Carry out testing every 500 training iterations.
+test_interval: 500
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.01
+momentum: 0.9
+weight_decay: 0.0000
+# The learning rate policy
+lr_policy: "inv"
+gamma: 0.0001
+power: 0.75
+# Display every 100 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 50000
+# snapshot intermediate results
+snapshot: 5000
+snapshot_prefix: "examples/siamese/mnist_siamese"
+# solver mode: CPU or GPU
+solver_mode: GPU