這裏主要梳理一下作業的主要內容和思路，完整作業文件可參考:
https://github.com/pandenghuang/Andrew-Ng-Deep-Learning-notes/tree/master/assignments/C1W4
作業完整截圖，參考本文結尾：作業完整截圖。
使用深度神經網絡進行圖像分類
Deep Neural Network for Image Classification: Application
When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course!
You will use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation.
After this assignment you will be able to:
- Build and apply a deep neural network to supervised learning.
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所需軟件包
1 - Packages
Let's first import all the packages that you will need during this assignment.
- numpy is the fundamental package for scientific computing with Python.
- matplotlib is a library to plot graphs in Python.
- h5py is a common package to interact with a dataset that is stored on an H5 file.
- PIL and scipy are used here to test your model with your own picture at the end.
- dnn_app_utils provides the functions implemented in the "Building your Deep Neural Network: Step by Step" assignment to this notebook.
- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work.
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數據集
2 - Dataset
You will use the same "Cat vs non-Cat" dataset as in "Logistic Regression as a Neural Network" (Assignment 2). The model you had built had 70% test accuracy on classifying cats vs non-cats images. Hopefully, your new model will perform a better!
Problem Statement: You are given a dataset ("data.h5") containing:
- a training set of m_train images labelled as cat (1) or non-cat (0)
- a test set of m_test images labelled as cat and non-cat
- each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).
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模型結構
3 - Architecture of your model
Now that you are familiar with the dataset, it is time to build a deep neural network to distinguish cat images from non-cat images.
You will build two different models:
- A 2-layer neural network
- An L-layer deep neural network
You will then compare the performance of these models, and also try out different values for 𝐿L.
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3.1 - 2-layer neural network （2層神經網絡）
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3.2 - L-layer deep neural network （任意層神經網絡）
It is hard to represent an L-layer deep neural network with the above representation. However, here is a simplified network representation:
3.3 - General methodology （一般方法）
As usual you will follow the Deep Learning methodology to build the model:
1. Initialize parameters / Define hyperparameters
2. Loop for num_iterations:
a. Forward propagation
b. Compute cost function
c. Backward propagation
d. Update parameters (using parameters, and grads from backprop)
4. Use trained parameters to predict labels
4 - Two-layer neural network （2層神經網絡）
Question: Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: LINEAR -> RELU -> LINEAR -> SIGMOID. The functions you may need and their inputs are:
def initialize_parameters(n_x, n_h, n_y):
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return parameters
def linear_activation_forward(A_prev, W, b, activation):
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return A, cache
def compute_cost(AL, Y):
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return cost
def linear_activation_backward(dA, cache, activation):
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return dA_prev, dW, db
def update_parameters(parameters, grads, learning_rate):
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return parameters
5 - L-layer Neural Network （任意層神經網絡）
Question: Use the helper functions you have implemented previously to build an 𝐿L-layer neural network with the following structure: *[LINEAR -> RELU]××(L-1) -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:
def initialize_parameters_deep(layer_dims):
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return parameters
def L_model_forward(X, parameters):
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return AL, caches
def compute_cost(AL, Y):
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return cost
def L_model_backward(AL, Y, caches):
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return grads
def update_parameters(parameters, grads, learning_rate):
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return parameters
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6) Results Analysis （結果分析）
First, let's take a look at some images the L-layer model labeled incorrectly. This will show a few mislabeled images.
A few type of images the model tends to do poorly on include:（分類錯誤的一些原因）
- Cat body in an unusual position
- Cat appears against a background of a similar color
- Unusual cat color and species
- Camera Angle
- Brightness of the picture
- Scale variation (cat is very large or small in image)
7) Test with your own image (optional/ungraded exercise) （使用自己的圖片進行測試）
Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:
1. Click on "File" in the upper bar of this notebook, then click "Open" to go on your Coursera Hub.
2. Add your image to this Jupyter Notebook's directory, in the "images" folder
3. Change your image's name in the following code
4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!