第一周作业 代码整理与分析。-可运行

相关的image 训练和测试集，和lr_utils文件

可以去 https://blog.youkuaiyun.com/thank_t_f/article/details/79867164 

里面找到第一周的作业下载下来。

#---------------------
#1导入各种包
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
%matplotlib inline
#------------------------------------------------------

#2数据准备。
#加载数据集 使用的是load_datase函数，这个函数自己定义的文件 lr_utils 中
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()

#通过得到的数据集得到训练集和测试集内容的个数，和图片的width和higth值
m_train = train_set_x_orig.shape[0] 
m_test = test_set_x_orig.shape[0]
num_px = train_set_x_orig.shape[1]

#将数据集编成m_train/m_test行 ，列数交由系统自己推算。 实际意义就是让每一列所有数据表示一张图片。
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

#为了方便计算，对数据进行集中和标准化。
train_set_x = train_set_x_flatten/255
test_set_x = test_set_x_flatten/255
#---------------------------------------------------------------------------
#-----------------------------------------------------------------------------

#3设计各种函数。

#定义辅助函数 sigmoid()
#    Compute the sigmoid of z
#   Arguments:
#            z -- A scalar or numpy array of any size.
#   Return:
#            s -- sigmoid(z)

def sigmoid(z):
    s = 1 / (1 + np.exp(-z))
    return s



#初始化w1,w2,d
# GRADED FUNCTION: initialize_with_zeros
#   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)
   
def initialize_with_zeros(dim):
    
    w = np.zeros((dim, 1))
    b = 0
    assert(w.shape == (dim, 1))
    assert(isinstance(b, float) or isinstance(b, int))
    return w, b


#对于单个样本的使用前向后向反馈更新w,d
# GRADED FUNCTION: propagate
#    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()

def propagate(w, b, X, Y):
    m = X.shape[1]
    # FORWARD PROPAGATION (FROM X TO COST)
    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
    
    # BACKWARD PROPAGATION (TO FIND GRAD)
    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



#梯度优化函数
# GRADED FUNCTION: optimize
#    This function optimizes w and b by running a gradient descent algorithm
#   Arguments:
#            w -- weights, a numpy array of size (num_px * num_px * 3, 1)
#            b -- bias, a scalar
#            X -- data of shape (num_px * num_px * 3, number of examples)
#            Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples)
#            num_iterations -- number of iterations of the optimization loop
#            learning_rate -- learning rate of the gradient descent update rule
#            print_cost -- True to print the loss every 100 steps
#    Returns:
#            params -- dictionary containing the weights w and bias b
#            grads -- dictionary containing the gradients of the weights and bias with respect to the cost function
#            costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.
#    Tips:
#        You basically need to write down two steps and iterate through them:
#            1) Calculate the cost and the gradient for the current parameters. Use propagate().
#            2) Update the parameters using gradient descent rule for w and b.

def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False): 
    costs = []
    for i in range(num_iterations):
        
        # Cost and gradient calculation (≈ 1-4 lines of code)
        grads, cost = propagate(w, b, X, Y)
        
        # Retrieve derivatives from grads
        dw = grads["dw"]
        db = grads["db"]
        
        # update rule (≈ 2 lines of code)
        w = w - learning_rate * dw
        b = b - learning_rate * db
        
        # Record the costs
        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


#使用测试集进行测试。
#    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
def predict(w, b, X):
        
    m = X.shape[1]
    Y_prediction = np.zeros((1,m))
    w = w.reshape(X.shape[0], 1)
    
    # Compute vector "A" predicting the probabilities of a cat being present in the picture
    A = sigmoid(np.dot(w.T, X) + b)


    for i in range(A.shape[1]):   
        # Convert probabilities A[0,i] to actual predictions p[0,i]
        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



#J将前几个函数集合起来使用构成训练模型
# GRADED FUNCTION: model
#        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.

def model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False):
    
    
    # initialize parameters with zeros (≈ 1 line of code)
    w, b = initialize_with_zeros(X_train.shape[0])

    # Gradient descent (≈ 1 line of code)
    parameters, grads, costs = optimize(w, b, X_train, Y_train, num_iterations, learning_rate, print_cost)
    
    # Retrieve parameters w and b from dictionary "parameters"
    w = parameters["w"]
    b = parameters["b"]
    
    # Predict test/train set examples (≈ 2 lines of code)
    Y_prediction_test = predict(w, b, X_test)
    Y_prediction_train = predict(w, b, X_train)


    # Print train/test Errors
    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

d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)