--- /dev/null
+# lenet_consolidated_solver.prototxt consolidates the lenet_solver, lenet_train,
+# and lenet_test prototxts into a single file. It also adds an additional test
+# net which runs on the training set, e.g., for the purpose of comparing
+# train/test accuracy (accuracy is computed only on the test set in the included
+# LeNet example. This is mainly included as an example of using these features
+# (specify NetParameters directly in the solver, specify multiple test nets)
+# if desired.
+#
+# 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: "lenet"
+# Set a random_seed for repeatable results.
+# (For results that vary due to random initialization, comment out the below
+# line, or set to a negative integer -- e.g. "random_seed: -1")
+random_seed: 1701
+# solver mode: CPU or GPU
+solver_mode: GPU
+# The training protocol buffer definition
+train_net_param {
+ name: "LeNet"
+ layers {
+ name: "mnist"
+ type: DATA
+ top: "data"
+ top: "label"
+ data_param {
+ source: "mnist-train-leveldb"
+ scale: 0.00390625
+ batch_size: 64
+ }
+ }
+ 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: "conv2"
+ type: CONVOLUTION
+ bottom: "pool1"
+ top: "conv2"
+ blobs_lr: 1
+ blobs_lr: 2
+ convolution_param {
+ num_output: 50
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "xavier"
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ }
+ layers {
+ name: "pool2"
+ type: POOLING
+ bottom: "conv2"
+ top: "pool2"
+ pooling_param {
+ pool: MAX
+ kernel_size: 2
+ stride: 2
+ }
+ }
+ layers {
+ name: "ip1"
+ type: INNER_PRODUCT
+ bottom: "pool2"
+ 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: "loss"
+ type: SOFTMAX_LOSS
+ bottom: "ip2"
+ bottom: "label"
+ }
+}
+# The testing protocol buffer definition
+test_net_param {
+ name: "LeNet-test"
+ layers {
+ name: "mnist"
+ type: DATA
+ top: "data"
+ top: "label"
+ data_param {
+ source: "mnist-test-leveldb"
+ scale: 0.00390625
+ batch_size: 100
+ }
+ }
+ layers {
+ name: "conv1"
+ type: CONVOLUTION
+ bottom: "data"
+ top: "conv1"
+ 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: "conv2"
+ type: CONVOLUTION
+ bottom: "pool1"
+ top: "conv2"
+ convolution_param {
+ num_output: 50
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "xavier"
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ }
+ layers {
+ name: "pool2"
+ type: POOLING
+ bottom: "conv2"
+ top: "pool2"
+ pooling_param {
+ pool: MAX
+ kernel_size: 2
+ stride: 2
+ }
+ }
+ layers {
+ name: "ip1"
+ type: INNER_PRODUCT
+ bottom: "pool2"
+ top: "ip1"
+ 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"
+ inner_product_param {
+ num_output: 10
+ weight_filler {
+ type: "xavier"
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ }
+ layers {
+ name: "prob"
+ type: SOFTMAX
+ bottom: "ip2"
+ top: "prob"
+ }
+ layers {
+ name: "accuracy"
+ type: ACCURACY
+ bottom: "prob"
+ bottom: "label"
+ top: "accuracy"
+ }
+}
+# The protocol buffer definition to test on the train set
+test_net_param {
+ name: "LeNet-test-on-train"
+ layers {
+ name: "mnist"
+ type: DATA
+ top: "data"
+ top: "label"
+ data_param {
+ source: "mnist-train-leveldb"
+ scale: 0.00390625
+ batch_size: 100
+ }
+ }
+ layers {
+ name: "conv1"
+ type: CONVOLUTION
+ bottom: "data"
+ top: "conv1"
+ 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: "conv2"
+ type: CONVOLUTION
+ bottom: "pool1"
+ top: "conv2"
+ convolution_param {
+ num_output: 50
+ kernel_size: 5
+ stride: 1
+ weight_filler {
+ type: "xavier"
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ }
+ layers {
+ name: "pool2"
+ type: POOLING
+ bottom: "conv2"
+ top: "pool2"
+ pooling_param {
+ pool: MAX
+ kernel_size: 2
+ stride: 2
+ }
+ }
+ layers {
+ name: "ip1"
+ type: INNER_PRODUCT
+ bottom: "pool2"
+ top: "ip1"
+ 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"
+ inner_product_param {
+ num_output: 10
+ weight_filler {
+ type: "xavier"
+ }
+ bias_filler {
+ type: "constant"
+ }
+ }
+ }
+ layers {
+ name: "prob"
+ type: SOFTMAX
+ bottom: "ip2"
+ top: "prob"
+ }
+ layers {
+ name: "accuracy"
+ type: ACCURACY
+ bottom: "prob"
+ bottom: "label"
+ top: "accuracy"
+ }
+}