Andrew Ng Coursera week2 Logistic Regression with a Neural Network mindset 编程作业代码祥注与数据集

这是coursea吴恩达老师的Neural Networks and Deep Learning week2的编程作业和离线数据

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

#读入数据库，如图像，训练集，测试集
train_set_x_orig,train_set_y,test_set_x_orig,test_set_y,classes=load_dataset()


#此乃训练集数量
m_train=train_set_x_orig.shape[0]
#此乃测试集数量
m_test=train_set_x_orig.shape[0]
#此乃图像的点阵规模
num_px=train_set_x_orig.shape[1]
#将矩阵标准化
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

train_set_x=train_set_x_flatten/255
test_set_x=test_set_x_flatten/255

#sigmoid函数，亦称为逻辑回归函数
def sigmoid(z):
	s=1/(1+np.exp(-z))
	return s
#print("sigmoid"+str(sigmoid(np.array([0,2]))))


def init(dim):
	w=np.zeros((dim,1))
	b=0
	return w,b

#前向传播
def pro(w,b,X,Y):
	m=X.shape[1]
	A=sigmoid(np.dot(w.T ,X)+b)
	#损失函数
	cost=(-1/m)*np.sum(Y*np.log(A)+(1-Y)*np.log(1-A))
	#此处记录梯度
	dw=(1/m)*np.dot(X,(A-Y).T)
	db=(1/m)*np.sum(A-Y)
	cost=np.squeeze(cost)
	#梯度cache
	grads={"dw":dw,"db":db}

	return grads,cost
#反向传播和梯度下降
def opt(w,b,X,Y,num_ite,learning_rate,print_cost=False):
	costs=[]
	#不断迭代进行梯度下降
	for i in range(num_ite):
		grads,cost=pro(w,b,X,Y)
		dw=grads["dw"]
		db=grads["db"]
		w=w-learning_rate*dw 
		b=b-learning_rate*db 
		#每100次迭代输出损失函数作为对比
		if i%100==0:
			costs.append(cost)
		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 
#预测函数
def predict(w,b,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)
#二元分类
	for i in range(A.shape[1]):
		if A[0,i]>0.5 :
			Y_prediction[0,i]=1
		else:
			Y_prediction[0,i]=0

	return Y_prediction

def model(X_train,Y_train,X_test,Y_test,num_ite=2000,learning_rate=0.5,print_cost=True):
	w,b=init(X_train.shape[0])

	params,gards,costs=opt(w,b,X_train,Y_train,num_ite,learning_rate,print_cost=True)

	w=params["w"]
	b=params["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={"cost":costs,"Y_prediction_train":Y_prediction_train,"Y_prediction_test":Y_prediction_test,"w":w,"b":b,"learning_rate":learning_rate,"num_ite":num_ite}

	return d

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

index = 25
plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3)))
plt.show()
print ("y = " + str(test_set_y[0,index]) + ", you predicted that  ")

if d["Y_prediction_test"][0,index]==1:
	print("it's a cat.")
else :
	print("it'not a cat")

https://github.com/ZeroT900sk/week2