Batch normalization layer with test and examples

examples/cifar10/cifar10_full_sigmoid_solver.prototxt

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+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid"
+# solver mode: CPU or GPU
+solver_mode: GPU

examples/cifar10/cifar10_full_sigmoid_solver_bn.prototxt

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+# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10
+# then another factor of 10 after 10 more epochs (5000 iters)
+
+# The train/test net protocol buffer definition
+net: "examples/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt"
+# test_iter specifies how many forward passes the test should carry out.
+# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,
+# covering the full 10,000 testing images.
+test_iter: 10
+# Carry out testing every 1000 training iterations.
+test_interval: 1000
+# The base learning rate, momentum and the weight decay of the network.
+base_lr: 0.001
+momentum: 0.9
+#weight_decay: 0.004
+# The learning rate policy
+lr_policy: "step"
+gamma: 1
+stepsize: 5000
+# Display every 200 iterations
+display: 100
+# The maximum number of iterations
+max_iter: 60000
+# snapshot intermediate results
+snapshot: 10000
+snapshot_prefix: "examples/cifar10_full_sigmoid_bn"
+# solver mode: CPU or GPU
+solver_mode: GPU

examples/cifar10/cifar10_full_sigmoid_train_test.prototxt

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+name: "CIFAR10_full"
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TRAIN
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_train_lmdb"
+    batch_size: 111
+    backend: LMDB
+    force_encoded_color: true
+  }
+}
+layer {
+  name: "cifar"
+  type: "Data"
+  top: "data"
+  top: "label"
+  include {
+    phase: TEST
+  }
+  transform_param {
+    mean_file: "examples/cifar10/mean.binaryproto"
+  }
+  data_param {
+    source: "examples/cifar10/cifar10_test_lmdb"
+    batch_size: 1000
+    backend: LMDB
+    force_encoded_color: true
+  }
+}
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.0001
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+
+
+layer {
+  name: "Sigmoid1"
+  type: "Sigmoid"
+  bottom: "pool1"
+  top: "Sigmoid1"
+}
+
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "Sigmoid1"
+  top: "conv2"
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 2
+  }
+  convolution_param {
+    num_output: 32
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+
+
+layer {
+  name: "Sigmoid2"
+  type: "Sigmoid"
+  bottom: "conv2"
+  top: "Sigmoid2"
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "Sigmoid2"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv3"
+  convolution_param {
+    num_output: 64
+    pad: 2
+    kernel_size: 5
+    stride: 1
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+  param {
+    lr_mult: 1
+  }
+  param {
+    lr_mult: 1
+  }
+
+}
+
+layer {
+  name: "Sigmoid3"
+  type: "Sigmoid"
+  bottom: "conv3"
+  top: "Sigmoid3"
+}
+
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "Sigmoid3"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 3
+    stride: 2
+  }
+}
+
+layer {
+  name: "ip1"
+  type: "InnerProduct"
+  bottom: "pool3"
+  top: "ip1"
+  param {
+    lr_mult: 1
+    decay_mult: 250
+  }
+  param {
+    lr_mult: 0.2
+    decay_mult: 0
+  }
+  inner_product_param {
+    num_output: 10
+    weight_filler {
+      type: "gaussian"
+      std: 0.01
+    }
+    bias_filler {
+      type: "constant"
+    }
+  }
+}
+layer {
+  name: "accuracy"
+  type: "Accuracy"
+  bottom: "ip1"
+  bottom: "label"
+  top: "accuracy"
+  include {
+    phase: TEST
+  }
+}
+layer {
+  name: "loss"
+  type: "SoftmaxWithLoss"
+  bottom: "ip1"
+  bottom: "label"
+  top: "loss"
+}