吴恩达Coursera深度学习课程 DeepLearning第一课第二周编程作业

最近在学习吴恩达的Deep Learning 系列课程，首先在此对吴老师表示深深的谢意。第一次接触深度学习方面的知识，更是第一次用代码编程实现深度学习的算法。所以在完成老师的作业过程中，遇到很多问题，最终在度娘的帮助下，花了一天的时间，终于把编程实现了逻辑回归神经网络的算法，特此记录一下，当做学习笔记，方便以后查阅。

首先根据网上的资料，搭建了Python开发环境。第一次是在Windows 10系统下Pythong3环境下jupyter notebook中编写代码的，但是在导入第三方库matplotlib、from PIL import Image、from scipy import ndimage时总是报各种错误，之后又根据网上的各种解决方案，改为Python2，报错。最后实在Ubuntu系统，python3环境中实现的。

源码下载：链接: https://pan.baidu.com/s/1ttaeYhzvFP4581YcNmmEEg 密码: 26fm

环境：Ubuntu + python3 + jupyter notebook

逻辑回归模型的一般学习算法架构包括：

Build the general architecture of a learning algorithm, including:

Initializing parameters
Calculating the cost function and its gradient
Using an optimization algorithm (gradient descent)

Gather all three functions above into a main model function, in the right order.

首先，导入必要的第三个库（包）

import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset  #自定义的模块，需要与当前代码文本在同一文件夹下

第二步：数据预处理


# 加载给定的数据集
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()

m_train = train_set_x_orig.shape[0] #训练数据集的总数m
m_test = test_set_x_orig.shape[0]   #测试数据就的总数m
num_px = train_set_x_orig.shape[1]   #图片像素

# Reshape the training and test examples
train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T

#preprocessing & standardize dataset,
train_set_x = train_set_x_flatten/255.
test_set_x = test_set_x_flatten/255.

第三步：分步实现算法部分

1. 激活函数（sigmod）

def sigmoid(z):
    """
    Compute the sigmoid of z
    Arguments:
    z -- A scalar or numpy array of any size.
    Return:
    s -- sigmoid(z)
    """
    ### START CODE HERE ### (≈ 1 line of code)
    s = 1 / (1 + np.exp(-z))
    ### END CODE HERE ###
    
    return s

2.初始化参数

def initialize_with_zeros(dim):
    """
    This function creates a vector of zeros of shape (dim, 1) for w and initializes b to 0.
    
    Argument:
    dim -- size of the w vector we want (or number of parameters in this case)
    
    Returns:
    w -- initialized vector of shape (dim, 1)
    b -- initialized scalar (corresponds to the bias)
    """
    
    w = np.zeros((dim, 1))
    b = 0

    assert(w.shape == (dim, 1))
    assert(isinstance(b, float) or isinstance(b, int))
    
    return w, b

3.向前向后传播

def propagate(w, b, X, Y):
    """
    Implement the cost function and its gradient for the propagation explained above

    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples)

    Return:
    cost -- negative log-likelihood cost for logistic regression
    dw -- gradient of the loss with respect to w, thus same shape as w
    db -- gradient of the loss with respect to b, thus same shape as b
    
    Tips:
    - Write your code step by step for the propagation. np.log(), np.dot()
    """
    
    m = X.shape[1]
    
    
    A = sigmoid(np.dot(w.T, X) + b)            # compute activation
    cost = -1 / m * np.sum(Y * np.log(A) + (1 - Y) * np.log(1 - A))         # compute cost
  
    dw = 1 / m * np.dot(X, (A - Y).T)
    db = 1 / m * np.sum(A - Y)
   
    assert(dw.shape == w.shape)
    assert(db.dtype == float)
    cost = np.squeeze(cost)
    assert(cost.shape == ())
    
    grads = {"dw": dw,
             "db": db}
    
    return grads, cost

4.优化

def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
    
    costs = []
    
    for i in range(num_iterations):
   
        grads, cost = propagate(w, b, X, Y)
       
        dw = grads["dw"]
        db = grads["db"]
        
        w = w - learning_rate * dw
        b = b - learning_rate * db
      
        if i % 100 == 0:
            costs.append(cost)
        
        # Print the cost every 100 training examples
        if print_cost and i % 100 == 0:
            print ("Cost after iteration %i: %f" %(i, cost))
    
    params = {"w": w,
              "b": b}
    
    grads = {"dw": dw,
             "db": db}
    
    return params, grads, costs

第三步：预测

def predict(w, b, X):
    '''
    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)
    
    Arguments:
    w -- weights, a numpy array of size (num_px * num_px * 3, 1)
    b -- bias, a scalar
    X -- data of size (num_px * num_px * 3, number of examples)
    
    Returns:
    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X
    '''
    
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)
    
    A = sigmoid(np.dot(w.T, X) + b)

    for i in range(A.shape[1]):
        
        if A[0, i] <= 0.5:
            Y_prediction[0, i] = 0
        else:
            Y_prediction[0, i] = 1
    
    assert(Y_prediction.shape == (1, m))
    
    return Y_prediction

第四步：模块化

def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    """
    Builds the logistic regression model by calling the function you've implemented previously
    
    Arguments:
    X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train)
    Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train)
    X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test)
    Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test)
    num_iterations -- hyperparameter representing the number of iterations to optimize the parameters
    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()
    print_cost -- Set to true to print the cost every 100 iterations
    
    Returns:
    d -- dictionary containing information about the model.
    """
    
    w, b = initialize_with_zeros(X_train.shape[0])

    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    
    w = parameters["w"]
    b = parameters["b"]
    
    Y_prediction_test = predict(w, b, X_test)
    Y_prediction_train = predict(w, b, X_train)

    print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100))
    print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100))

    
    d = {"costs": costs,
         "Y_prediction_test": Y_prediction_test, 
         "Y_prediction_train" : Y_prediction_train, 
         "w" : w, 
         "b" : b,
         "learning_rate" : learning_rate,
         "num_iterations": num_iterations}
    
    return d

第五步：使用自己的图片来测试

my_image = "cat_in_iran.jpg"   # change this to the name of your image file 

fname = "images/" + my_image
image = np.array(ndimage.imread(fname, flatten=False))
my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((1, num_px*num_px*3)).T
my_predicted_image = predict(d["w"], d["b"], my_image)

plt.imshow(image)
print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[int(np.squeeze(my_predicted_image)),].decode("utf-8") +  "\" picture.")

吴恩达Coursera深度学习课程 DeepLearning第一课第二周编程作业

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