MNIST autoencoder example

docs/mnist.md

@@ -15,7 +15,7 @@ You will first need to download and convert the data format from the MNIST websi
 
     cd $CAFFE_ROOT/data/mnist
     ./get_mnist.sh
-    cd $CAFFE_ROOT/examples/lenet
+    cd $CAFFE_ROOT/examples/mnist
     ./create_mnist.sh
 
 If it complains that `wget` or `gunzip` are not installed, you need to install them respectively. After running the script there should be two datasets, `mnist-train-leveldb`, and `mnist-test-leveldb`.
@@ -33,7 +33,7 @@ Training and Testing the Model
 
 Training the model is simple after you have written the network definition protobuf and solver protobuf files. Simply run `train_mnist.sh`, or the following command directly:
 
-    cd $CAFFE_ROOT/examples/lenet
+    cd $CAFFE_ROOT/examples/mnist
     ./train_lenet.sh
 
 `train_lenet.sh` is a simple script, but here are a few explanations: `GLOG_logtostderr=1` is the google logging flag that prints all the logging messages directly to stderr. The main tool for training is `train_net.bin`, with the solver protobuf text file as its argument.

examples/mnist/mnist_autoencoder_solver.prototxt

@@ -0,0 +1,14 @@
+train_net: "mnist_autoencoder_train.prototxt"
+test_net: "mnist_autoencoder_test.prototxt"
+test_iter: 50
+test_interval: 100
+test_compute_loss: true
+base_lr: 0.0001
+lr_policy: "fixed"
+display: 20
+max_iter: 4000000
+weight_decay: 0.0005
+snapshot: 10000
+snapshot_prefix: "mnist_autoencoder_train"
+momentum: 0.9
+solver_mode: 1

examples/mnist/mnist_autoencoder_test.prototxt

@@ -0,0 +1,145 @@
+name: "MNISTAutoencoder"
+layers {
+  top: "data"
+  name: "data"
+  type: DATA
+  data_param {
+    source: "mnist-test-leveldb"
+    scale: 0.0039215684
+    batch_size: 100
+  }
+}
+layers {
+  bottom: "data"
+  top: "flatdata"
+  name: "flatdata"
+  type: FLATTEN
+}
+layers {
+  bottom: "data"
+  top: "encode1"
+  name: "encode1"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 1000
+  }
+}
+layers {
+  bottom: "encode1"
+  top: "encode1neuron"
+  name: "encode1neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "encode1neuron"
+  top: "encode2"
+  name: "encode2"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 500
+  }
+}
+layers {
+  bottom: "encode2"
+  top: "encode2neuron"
+  name: "encode2neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "encode2neuron"
+  top: "encode3"
+  name: "encode3"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 250
+  }
+}
+layers {
+  bottom: "encode3"
+  top: "encode3neuron"
+  name: "encode3neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "encode3neuron"
+  top: "encode4"
+  name: "encode4"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 1
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 30
+  }
+}
+layers {
+  bottom: "encode4"
+  top: "decode4"
+  name: "decode4"
+  type: INNER_PRODUCT
+  blobs_lr: 1
+  blobs_lr: 1
+  weight_decay: 1
+  weight_decay: 0
+  inner_product_param {
+    num_output: 250
+  }
+}
+layers {
+  bottom: "decode4"
+  top: "decode4neuron"
+  name: "decode4neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "decode4neuron"
+  top: "decode3"
+  name: "decode3"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 500
+  }
+}
+layers {
+  bottom: "decode3"
+  top: "decode3neuron"
+  name: "decode3neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "decode3neuron"
+  top: "decode2"
+  name: "decode2"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 1000
+  }
+}
+layers {
+  bottom: "decode2"
+  top: "decode2neuron"
+  name: "decode2neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "decode2neuron"
+  top: "decode1"
+  name: "decode1"
+  type: INNER_PRODUCT
+  inner_product_param {
+    num_output: 784
+  }
+}
+layers {
+  bottom: "decode1"
+  top: "decode1neuron"
+  name: "decode1neuron"
+  type: SIGMOID
+}
+layers {
+  bottom: "decode1neuron"
+  bottom: "flatdata"
+  name: "loss"
+  type: EUCLIDEAN_LOSS
+}