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Matlab代写 | Project 1 Digit recognition with convolutional neural networks

Matlab代写 | Project 1 Digit recognition with convolutional neural networks



1. Instructions

These instructions are also true to all the remaining projects unless indicated. Please read them carefully.

1. Students are encouraged to discuss projects. However, each student needs to write code and a report all by him/herself. Code should NOT be shared or copied. Do NOT use external code unless permitted. Obviously, it is not OK to look at the code and just reimplement with very similar coding structure.

2. Post questions to Canvas:Discussions so that everybody can share, unless the questions are private. Please look at Canvas:Discussions first if similar questions have been posted. For private questions, please use Canvas:Inbox.

3. Two files need to be uploaded. First, your write-up (the main document) must be named as {Your-SFUID}.pdf and uploaded to Canvas. Second, a zip package must be uploaded to also Canvas with the following directory structure (they will be different for the other projects but will be similar):

○ {SFUID}/
■ ec
● ec.m
■ matlab/
● col2im_conv.m
● col2im_conv_matlab.m
● conv_layer_backward.m
● conv_layer_forward.m
● conv_net.m
● convnet_forward.m
● get_lenet.m
● get_lr.m
● im2col_conv.m
● im2col_conv_matlab.m
● init_convnet.m
● inner_product_backward.m
● inner_product_forward.m
● load_mnist.m
● mlrloss.m
● pooling_layer_backward.m
● pooling_layer_forward.m
● relu_forward.m
● relu_backward.m
● sgd_momentum.m
● test_components.m
● test_network.m
● train_lenet.m
● vis_data.m
● lenet_pretrained.mat
● mnist all.mat

○ Project 1 has 13 pts.

4. File paths: Make sure that any file paths that you use are relative and not absolute so that we can easily run code on our end. For instance, you cannot write “imread(‘/some/absolute/path/data/abc.jpg’)”. Write “imread(‘../data/abc.jpg’)” instead.

2. Overview

In this assignment you will implement a convolutional neural network (CNN). You will be building a numeric character recognition system trained on the MNIST dataset.

We begin with a brief description of the architecture and the functions. For more details,you can refer to online resources such as Note that the amount of coding in this assignment is a lot less than the other assignments. We will not provide detailed instructions, and one is expected to search online and/or reverse-engineer template code.

A typical convolutional neural network has four different types of layers.

Fully Connected Layer / Inner Product Layer (IP)

The fully connected or the inner product layer is the simplest layer which makes up neural networks. Each neuron of the layer is connected to all the neurons of the previous layer (See Fig 1). Mathematically it is modelled by a matrix multiplication and the addition of a bias term. For a given input x the output of the fully connected layer is given by the following equation,

f (x) = W x + b

W, b are the weights and biases of the layer. W is a two dimensional matrix of m × n size where n is the dimensionality of the previous layer and m is the number of neurons in this layer. b is a vector with size m × 1.