卷积神经网络(Convolutional Neural Network, CNN)是一种前馈神经网络,它的人工神经元可以响应一部分覆盖范围内的周围单元,[1]对于大型图像处理有出色表现。
Deep Neural Network(DNN)模型是基本的深度学习框架
# bottom = last top
name: "LeNet"
# 数据层
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
# 数据层
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}
# 卷积层
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 20
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
# 池化层
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
# 卷积层
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: 50
kernel_size: 5
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
# 池化层
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
# 全连接层
layer {
name: "ip1"
type: "InnerProduct"
bottom: "pool2"
top: "ip1"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 500
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
# ReLU层
layer {
name: "relu1"
type: "ReLU"
bottom: "ip1"
top: "ip1"
}
# 全连接层
layer {
name: "ip2"
type: "InnerProduct"
bottom: "ip1"
top: "ip2"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: 10
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
}
}
}
# 损失层/预测精度
layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
# 损失层
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}
数据层 Data Layers
- Image Data - read raw images.
- Database - read data from LEVELDB or LMDB.
- HDF5 Input - read HDF5 data, allows data of arbitrary dimensions.
- HDF5 Output - write data as HDF5.
- Input - typically used for networks that are being deployed.
- Window Data - read window data file.
- Memory Data - read data directly from memory.
- Dummy Data - for static data and debugging.
视觉层 Vision Layers
- Convolution Layer - convolves the input image with a set of learnable filters, each producing one feature map in the output image.
- Pooling Layer - max, average, or stochastic pooling.
- Spatial Pyramid Pooling (SPP)
- Crop - perform cropping transformation.
- Deconvolution Layer - transposed convolution.
- Im2Col - relic helper layer that is not used much anymore.
经常层 Recurrent Layers
- Recurrent
- RNN
- Long-Short Term Memory (LSTM)
普通层 Common Layers
- Inner Product - fully connected layer.
- Dropout
- Embed - for learning embeddings of one-hot encoded vector (takes index as input).
归一化层 Normalization Layers
- Local Response Normalization (LRN) - performs a kind of “lateral inhibition” by normalizing over local input regions.
- Mean Variance Normalization (MVN) - performs contrast normalization / instance normalization.
- Batch Normalization - performs normalization over mini-batches.
激活/神经元层 Activation / Neuron Layers
- ReLU / Rectified-Linear and Leaky-ReLU - ReLU and Leaky-ReLU rectification.
- PReLU - parametric ReLU.
- ELU - exponential linear rectification.
- Sigmoid
- TanH
- Absolute Value
- Power - f(x) = (shift + scale * x) ^ power.
- Exp - f(x) = base ^ (shift + scale * x).
- Log - f(x) = log(x).
- BNLL - f(x) = log(1 + exp(x)).
- Threshold - performs step function at user defined threshold.
- Bias - adds a bias to a blob that can either be learned or fixed.
- Scale - scales a blob by an amount that can either be learned or fixed.
实用层 Utility Layers
- Flatten
*Reshape - Batch Reindex
- Split
- Concat
- Slicing
- Eltwise - element-wise operations such as product or sum between two blobs.
- Filter / Mask - mask or select output using last blob.
- Parameter - enable parameters to be shared between layers.
- Reduction - reduce input blob to scalar blob using operations such as sum or mean.
- Silence - prevent top-level blobs from being printed during training.
- ArgMax
- Softmax
- Python - allows custom Python layers.
损失层 Loss Layers
- Multinomial Logistic Loss
- Infogain Loss - a generalization of MultinomialLogisticLossLayer.
- Softmax with Loss - computes the multinomial logistic loss of the softmax of its inputs. It’s conceptually identical to a softmax layer followed by a multinomial logistic loss layer, but provides a more numerically stable gradient.
- Sum-of-Squares / Euclidean - computes the sum of squares of differences of its two inputs, 12N∑Ni=1∥x1i−x2i∥2212N∑i=1N‖xi1−xi2‖22
- Hinge / Margin - The hinge loss layer computes a one-vs-all hinge (L1) or squared hinge loss (L2).
- Sigmoid Cross-Entropy Loss - computes the cross-entropy (logistic) loss, often used for predicting targets interpreted as probabilities.
- Accuracy / Top-k layer - scores the output as an accuracy with respect to target – it is not actually a loss and has no backward step.
- Contrastive Loss
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