【吴恩达课后编程学习笔记】Course 1 - 第三周作业

抄了一遍带有一个隐藏层的平面数据分类
个人认为自己开展深度学习程序编写的话，主要难点就在下图了，也就是反向传播公式的推演了，所以抽几个重点公式推，不会用****的编辑器，直接截word笔记里的图了(还是MathType好用！！！
【吴恩达课后编程学习笔记】Course 1 - 第三周作业

import numpy as np
import matplotlib.pyplot as plt
from testCase import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

np.random.seed(1)

X,Y=load_planar_dataset()

shape_X=X.shape
shape_Y=Y.shape
m=Y.shape[1]

print("X dimension:"+str(shape_X))
print("Y dimension:"+str(shape_Y))
print("number:"+str(m))

def layer_sizes(X,Y):
	n_x=X.shape[0]
	n_h=4
	n_y=Y.shape[0]
	
	return(n_x,n_h,n_y)

def initialize_parameters(n_x,n_h,n_y):
	np.random.seed(2)
	W1=np.random.randn(n_h,n_x)*0.01
	b1=np.zeros(shape=(n_h,1))
	W2=np.random.randn(n_y,n_h)*0.01
	b2=np.zeros(shape=(n_y,1))
	
	parameters={
				"W1":W1,
				"b1":b1,
				"W2":W2,
				"b2":b2}
				
	return parameters
	
def forward_propagation(X,parameters):
	W1=parameters["W1"]
	b1=parameters["b1"]
	W2=parameters["W2"]
	b2=parameters["b2"]
	
	Z1=np.dot(W1,X)+b1
	A1=np.tanh(Z1)
	Z2=np.dot(W2,A1)
	A2=sigmoid(Z2)
	
	cache={
			"Z1":Z1,
			"A1":A1,
			"Z2":Z2,
			"A2":A2}
	return (A2,cache)
	
def compute_cost(A2,Y,parameters):
	m=Y.shape[1]
	W1=parameters["W1"]
	W2=parameters["W2"]
	
	logprobs=np.multiply(np.log(A2),Y)+np.multiply((1-Y),np.log(1-A2))
	cost=-np.sum(logprobs)/m
	cost=float(np.squeeze(cost))
	
	return cost
	
def backward_propagation(parameters,cache,X,Y):
	m=X.shape[1]
	
	W1=parameters["W1"]
	W2=parameters["W2"]
	
	A1=cache["A1"]
	A2=cache["A2"]
	
	dZ2=A2-Y
	dW2=(1/m)*np.dot(dZ2,A1.T)
	#S'=S(1-S)
	db2=(1/m)*np.sum(dZ2,axis=1,keepdims=True)
	dZ1 = np.multiply(np.dot(W2.T, dZ2), 1 - np.power(A1, 2))
	#Tanh'=1-tanh^2
	dW1=(1/m)*np.dot(dZ1,X.T)
	db1=(1/m)*np.sum(dZ1,axis=1,keepdims=True)
	grads={
			"dW1":dW1,
			"db1":db1,
			"dW2":dW2,
			"db2":db2
			}
	return grads
	
def update_parameters(parameters,learning_rate=1.2):
	W1,W2=parameters["W1"],parameters["W2"]
	b1,b2=parameters["b1"],parameters["b2"]
	
	dW1,dW2=grads["dW1"],grads["dW2"]
	db1,db2=grads["db1"],grads["db2"]
	
	W1=W1-learning_rate*dW1
	b1=b1-learning_rate*db1
	W2=W2-learning_rate*dW2
	b2=b2-learning_rate*db2
	
	parameters={
				"W1":W1,
				"b1":b1,
				"W2":W2,
				"b2":b2}
				
	return parameters
	
def nn_model(X,Y,n_h,num_iterations,print_cost=False):
	np.random.seed(3)
	n_x=layer_sizes(X,Y)[0]
	n_y=layer_sizes(X,Y)[2]
	
	parameters=initialize_parameters(n_x,n_h,n_y)
	W1=parameters["W1"]
	b1=parameters["b1"]
	W2=parameters["W2"]
	b2=parameters["b2"]
	
	for i in range(num_iterations):
		A2,cache=forward_propagation(X,parameters)
		cost=compute_cost(A2,Y,parameters)
		grads=backward_propagation(parameters,cache,X,Y)
		parameters=update_parameters(parameters,grads,learning_rate=0.5)
		if print_cost:
			if i%1000==0:
				print("第 ",i," 次循环，成本为："+str(cost))
	return parameters

def predict(parameters,X):
	A2,cache=forward_propagation(X,parameters)
	predictions=np.round(A2)
	return predictions
	

parameters = nn_model(X, Y, n_h = 4, num_iterations=10000, print_cost=True)
plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
plt.title("Decision Boundary for hidden layer size " + str(4))

predictions = predict(parameters, X)
print ('准确率: %d' % float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100) + '%')

【吴恩达课后编程学习笔记】Course 1 - 第三周作业

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