Introduction

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors.

The code structure of Caffe is as follows:

• The core of Caffe is the C++ library, which provides the basic building blocks for deep learning models.
• The Python API provides a high-level interface for using Caffe from Python.
• The Protobuf definition files specify the configuration of Caffe models.
• The data layer provides a mechanism for loading data into Caffe models.
• The loss layer defines the loss function that is used to train Caffe models.
• The optimizer implements the algorithm for updating the parameters of Caffe models.

Protobuf

Caffe uses Protocol Buffers to define the network architecture. The Protobuf definition file is called .prototxt. The following code shows a simple example of a .prototxt file:

net: "test"

layer {
  name: "data"
  type: "Data"
  top: "data"
  input_param {
    source: "mnist_train_lmdb"
    batch_size: 64
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
  }
}

layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool1"
  top: "ip1"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  inner_product_param {
    num_output: 100
  }
}

layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip1"
  bottom: "label"
  top: "loss"
}

This .prototxt file defines a simple neural network with three layers: a data layer, a convolutional layer, and a fully connected layer. The data layer loads the MNIST dataset, the convolutional layer performs a convolution operation, and the fully connected layer performs a fully connected operation. The loss layer is used to measure the error between the predicted output of the network and the ground truth.

To use the .prototxt file, you need to compile it using the Protobuf compiler. The following command will compile the .prototxt file to a C++ file:

protoc --cpp_out=. test.prototxt

The compiled C++ file can then be used to create a Caffe network.

Natvie API

To use the native Caffe API, you need to write C++ code. The following code shows how to create a simple Caffe model:

#include <caffe/caffe.hpp>

using namespace caffe;

int main() {
  // Create a Caffe Net.
  Net net("test");

  // Add a convolutional layer.
  ConvolutionLayer<float> conv1(net, "conv1");
  conv1.set_num_output(10);
  conv1.set_kernel_size(3, 3);

  // Add a pooling layer.
  MaxPoolingLayer<float> pool1(net, "pool1");
  pool1.set_kernel_size(2, 2);

  // Add a fully connected layer.
  FullyConnectedLayer<float> fc1(net, "fc1");
  fc1.set_num_output(100);

  // Add a loss layer.
  SoftmaxWithLossLayer<float> loss(net, "loss");

  // Compile the net.
  net.init();

  // Train the net.
  net.train();

  return 0;
}

Python API

To use the Python API, you need to install the pycaffe package. The following code shows how to create a simple Caffe model using Python:

import caffe

net = caffe.Net("test.prototxt", caffe.TEST)

# Add a convolutional layer.
conv1 = caffe.layers.Convolution(net, "conv1", num_output=10, kernel_size=3, 3)

# Add a pooling layer.
pool1 = caffe.layers.MaxPooling(net, "pool1", kernel_size=2, 2)

# Add a fully connected layer.
fc1 = caffe.layers.FullyConnected(net, "fc1", num_output=100)

# Add a loss layer.
loss = caffe.layers.SoftmaxWithLoss(net, "loss")

# Compile the net.
net.forward()

# Get the loss.
loss = net.blobs["loss"].data

The Caffe Protobuf definition files specify the configuration of Caffe models. These files are used by both the native Caffe API and the Python API.

The data layer provides a mechanism for loading data into Caffe models. The data layer can be used to load images, text, or other data types.

The loss layer defines the loss function that is used to train Caffe models. The loss function is used to measure the error between the predicted output of the model and the ground truth.

The optimizer implements the algorithm for updating the parameters of Caffe models. The optimizer is used to minimize the loss function.

Layer

Caffe’s layers are the basic building blocks of a neural network. Each layer performs a specific operation on the data, such as convolution, pooling, or fully connected.

The forward pass of a Caffe layer is the process of computing the output of the layer given the input. The backward pass is the process of computing the gradient of the loss function with respect to the parameters of the layer.

The forward pass of a Caffe layer is typically implemented in the Layer::Forward() method. The backward pass is typically implemented in the Layer::Backward() method.

The following is a brief overview of some of the most common Caffe layers:

• Data layers: Data layers load data into the network. They can be used to load images, text, or other data types.
• Convolutional layers: Convolutional layers perform convolution operations on the data. They are used to extract features from the data.
• Pooling layers: Pooling layers perform pooling operations on the data. They are used to reduce the size of the data while preserving the important features.
• Fully connected layers: Fully connected layers perform fully connected operations on the data. They are used to make predictions from the data.
• Loss layers: Loss layers measure the error between the predicted output of the network and the ground truth. They are used to train the network.

The forward and backward passes of Caffe layers are implemented using automatic differentiation. Automatic differentiation is a technique for computing the derivatives of a function without explicitly writing out the derivatives.

Caffe uses the Eigen library for automatic differentiation. Eigen is a C++ library for linear algebra.