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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

# 定义sigmoid函数

def sigmoid(x):

s = 1/(1+np.exp(-x))

return s

# 初始化w和b两个参数

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

# 计算正向传播和反向传播

def propagate(w,b,X,Y):

m = X.shape[1]

# 正向传播

A = sigmoid(np.dot(w.T,X)+b)

# 成本函数

cost = -np.sum(Y*np.log(A)+(1-Y)*np.log(1-A),axis=1)/m

# 反向传播

dw = np.dot(X,(A-Y).T)/m

db = np.sum(A-Y,axis=1)/m

# 断言

assert(dw.shape == w.shape)

assert(db.dtype == float)

cost = np.squeeze(cost)

assert(cost.shape == ())

grads = {"dw":dw,"db":db}

return cost,grads

# 使用梯度下降法优化得到参数w和b

def optimize(w,b,X,Y,num_iterations,learning_rate,print_cost=False):

costs = []

for i in range(num_iterations):

cost,grads = 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)

if(print_cost):

print("cost after iteration %i:%f"%(i,cost))

params = {'w':w,'b':b}

grads = {'dw':dw,'db':db}

return params,grads,costs

# 使用优化的到的参数进行预测

def predict(w,b,X):

m = X.shape[1]

Y_predictions = np.zeros([1,m])

assert(w.shape[0] == X.shape[0])

A = sigmoid(np.dot(w.T,X)+b)

for i in range(A.shape[1]):

Y_predictions[0,i] = 1 if A[0,i]>=0.5 else 0

return Y_predictions

# 最终的神经网络模型

def model(X_train,Y_train,X_test,Y_test,num_iterations=2000,learning_rate=0.5,print_cost=False):

w,b = initialize_with_zeros(X_train.shape[0])

params,grads,costs = optimize(w,b,X_train,Y_train,num_iterations,learning_rate,print_cost)

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 = {"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

# sigmoid函数测试

print(sigmoid(np.array([0,2])))

# propagate函数测试

w,b,X,Y = np.array([[1],[2]]),2,np.array([[1,2],[3,4]]),np.array([[1,0]])

cost,grads = propagate(w,b,X,Y)

print(cost,grads)

# optimize函数测试

params, grads, costs = optimize(w, b, X, Y, num_iterations= 100, learning_rate = 0.009, print_cost = False)

print(params,grads,costs)

# predict函数测试

print ("test predictions = " + str(predict(w, b, X)))

# 加载数据集

train_set_x_orig,train_set_y,test_set_x_orig,test_set_y,classes=load_dataset()

print("train_set_x_orig.shape: ",train_set_x_orig.shape)

print("test_set_x_orig.shape: ",test_set_x_orig.shape)

num_px = train_set_x_orig.shape[1]

# 显示训练集的一张图片

index = 25

plt.figure()

plt.imshow(train_set_x_orig[index])

plt.show()

# 序列化X矩阵：(num_px*num_px*3)

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

print("train_set_x_flatten.shape: ",train_set_x_flatten.shape)

print("test_set_x_flatten.shape: ",test_set_x_flatten.shape)

# 归一化

train_set_x = train_set_x_flatten/255

test_set_x = test_set_x_flatten/255

# 最终结果

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

# print(d)

# 显示原图和预测结果

index = 4

plt.imshow(test_set_x[:,index].reshape([num_px,num_px,3]))

print("real result = "+str(test_set_y[0,index])+"and your predict = "+str(d['Y_prediction_test'][0,index]))

plt.figure()

plt.plot(np.squeeze(d['costs']))

plt.ylabel('cost')

plt.xlabel('iterations(per hundreds)')

plt.title("learning rate = "+str(d['learning_rate']))

plt.show()

# 不同的学习率测试

learning_rates = [0.01, 0.001, 0.0001]

models = {}

for i in learning_rates:

print ("learning rate is: " + str(i))

models[str(i)] = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 1500, learning_rate = i, print_cost = False)

print ('\n' + "-------------------------------------------------------" + '\n')

for i in learning_rates:

plt.plot(np.squeeze(models[str(i)]["costs"]), label= str(models[str(i)]["learning_rate"]))

plt.ylabel('cost')

plt.xlabel('iterations')

legend = plt.legend(loc='upper center', shadow=True)

frame = legend.get_frame()

frame.set_facecolor('0.90')

plt.show()