第3周 用1层隐藏层的神经网络分类二维数据

课程1 神经网络和深度学习

第2周 用1层隐藏层的神经网络分类二维数据

我是参考此博文，完成该作业。

https://www.heywhale.com/mw/project/5dd3946900b0b900365f3a48

我是在这个博主下的资源

https://blog.csdn.net/u013733326/article/details/79702148

完整的代码实现（过程讲解和练习看上面的链接）

cd D:\software\OneDrive\桌面\吴恩达深度学习课后作业\第三周- 用1层隐藏层的神经网络分类二维数据\resource

D:\software\OneDrive\桌面\吴恩达深度学习课后作业\第三周- 用1层隐藏层的神经网络分类二维数据\resource

# 引包
#sklearn：为数据挖掘和数据分析提供的简单高效的工具。
import numpy as np
import matplotlib.pyplot as plt
from testCases import *
import sklearn
import sklearn.datasets
import sklearn.linear_model
from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets

%matplotlib inline

np.random.seed(1) # 设定种子使结果一致

#加载数据
X,Y = load_planar_dataset()

#scatter:用于生成一个scatter散点图
#X[0,:]:用于获取X的第一行数据
#s:标量 散点的面积
#c:散点的颜色
#cmap:colormap实例；plt.cm.Spectral:在画图时为不同类别的样本分别分配不同的颜色
plt.scatter(X[0,:],X[1,:],c=Y.reshape(X[0,:].shape),s=40,cmap=plt.cm.Spectral)

#练习：数据集中有多少个训练示例？ 另外，变量“ X”和“ Y”的“shape”是什么？
shape_X = X.shape
shape_Y = Y.shape

m = shape_X[1]
print ('The shape of X is: ' + str(shape_X))
print ('The shape of Y is: ' + str(shape_Y))
print ('I have m = %d training examples!' % (m))

The shape of X is: (2, 400)
The shape of Y is: (1, 400)
I have m = 400 training examples!

1、简单Logistic回归（效果不好）

#sklearn.linear_model.LogisticRegressionCV(): Logistic回归（aka logit，MaxEnt）分类器
#fit(X,Y):监督学习算法,拟合分类器
clf = sklearn.linear_model.LogisticRegressionCV()
clf.fit(X.T, Y.T)

LogisticRegressionCV(Cs=10, class_weight=None, cv=None, dual=False,
fit_intercept=True, intercept_scaling=1.0, max_iter=100,
multi_class=‘ovr’, n_jobs=1, penalty=‘l2’, random_state=None,
refit=True, scoring=None, solver=‘lbfgs’, tol=0.0001, verbose=0)

#绘制此模型的决策边界
#float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100)是默认的公式？
#plot_decision_boundary(lambda x: clf.predict(x), X, Y),出错：将Y改为 np.squeeze(Y)
plot_decision_boundary(lambda x: clf.predict(x), X, np.squeeze(Y)) #绘制决策边界
plt.title("Logistic Regression")

LR_predictions = clf.predict(X.T)
print ('逻辑回归的准确性: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +
       '% ' + "(percentage of correctly labelled datapoints)")

逻辑回归的准确性: 47 % (percentage of correctly labelled datapoints)

2、神经网络模型

“训练带有单个隐藏层的神经网络”

提示：
建立神经网络的一般方法是：

1. 定义神经网络结构（输入单元数，隐藏单元数等）。
2. 初始化模型的参数
3. 循环：
   • 实现前向传播
   • 计算损失
   • 后向传播以获得梯度
   • 更新参数（梯度下降）

(1) 定义神经网络结构

练习：定义三个变量：
- n_x：输入层的大小
- n_h：隐藏层的大小（将其设置为4）
- n_y：输出层的大小

提示：使用shape来找到n_x和n_y。 另外，将隐藏层大小硬编码为4。

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)

# 测试layer_sizes函数
x_assess,y_assess = layer_sizes_test_case()
(n_x,n_h,n_y) = layer_sizes(x_assess,y_assess)
print("The size of the input layer is: n_x = " + str(n_x))
print("The size of the hidden layer is: n_h = " + str(n_h))
print("The size of the output layer is: n_y = " + str(n_y))

The size of the input layer is: n_x = 5
The size of the hidden layer is: n_h = 4
The size of the output layer is: n_y = 2

(2) 初始化模型的参数

说明：
请确保参数大小正确。 如果需要，也可参考上面的神经网络图。
使用随机值初始化权重矩阵。
- 使用：np.random.randn（a，b）* 0.01随机初始化维度为（a，b）的矩阵。
将偏差向量初始化为零。
- 使用：np.zeros((a,b)) 初始化维度为（a，b）零的矩阵

# np.random.seed(n)函数用于生成指定随机数。
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((n_h,1))
    W2 = np.random.randn(n_y,n_h)*0.01
    b2 = np.zeros((n_y,1))
    
    assert(W1.shape == (n_h,n_x))
    assert(b1.shape == (n_h,1))
    assert(W2.shape == (n_y,n_h))
    assert(b2.shape == (n_y,1))
    
    parameters = {
        "W1":W1,
        "b1":b1,
        "W2":W2,
        "b2":b2
    }
    return parameters

# 测试initialize_parameters函数
n_x,n_h,n_y = initialize_parameters_test_case()
parameters = initialize_parameters(n_x,n_h,n_y)

print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))

W1 = [[-0.00416758 -0.00056267]
[-0.02136196 0.01640271]
[-0.01793436 -0.00841747]
[ 0.00502881 -0.01245288]]
b1 = [[0.]
[0.]
[0.]
[0.]]
W2 = [[-0.01057952 -0.00909008 0.00551454 0.02292208]]
b2 = [[0.]]

3、循环

说明：
在上方查看分类器的数学表示形式。
你可以使用内置在笔记本中的sigmoid()函数。
你也可以使用numpy库中的np.tanh（）函数。

必须执行以下步骤：
1.使用parameters [“ …”]从字典“ parameters”（这是initialize_parameters（）的输出）中检索出每个参数。
2.实现正向传播，计算Z[1]A[1] 和 Z[2],A[2] （所有训练数据的预测结果向量）。
向后传播所需的值存储在cache中， cache将作为反向传播函数的输入。

（1）后向传播

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)+b2
    A2 = sigmoid(Z2)
    
    assert(A2.shape==(1,X.shape[1]))
    
    cache = {
        "Z1":Z1,
        "A1":A1,
        "Z2":Z2,
        "A2":A2
    }
    
    return A2,cache

#测试forward_propagation函数
#np.mean:求取均值
X_assess,parameters = forward_propagation_test_case()
A2,cache = forward_propagation(X_assess,parameters)

print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))

-0.0004997557777419902 -0.000496963353231779 0.00043818745095914653 0.500109546852431

(2) 练习：实现compute_cost（）以计算损失J的值。

def compute_cost(A2, Y, parameters):
    
    m = Y.shape[1]
    
    logprobs = Y*np.log(A2) + (1-Y)* np.log(1-A2)
    cost = -1/m * np.sum(logprobs)
    
    cost = np.squeeze(cost)
    
    assert(isinstance(cost, float))
    
    return cost

#测试compute_cost函数
A2, Y, parameters = compute_cost_test_case()
cost = compute_cost(A2, Y, parameters)
print("cost="+str(cost))

cost=0.6929198937761265

(3)前向传播

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)
    db2 = 1/m * np.sum(dZ2,axis = 1,keepdims = True)
    
    dZ1 = np.dot(W2.T,dZ2) * (1-np.power(A1,2))
    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

# 测试函数 backward_propagation
parameters, cache, X_assess, Y_assess = backward_propagation_test_case()
grads = backward_propagation(parameters, cache, X_assess, Y_assess)
print ("dW1 = "+ str(grads["dW1"]))
print ("db1 = "+ str(grads["db1"]))
print ("dW2 = "+ str(grads["dW2"]))
print ("db2 = "+ str(grads["db2"]))

dW1 = [[ 0.01018708 -0.00708701]
[ 0.00873447 -0.0060768 ]
[-0.00530847 0.00369379]
[-0.02206365 0.01535126]]
db1 = [[-0.00069728]
[-0.00060606]
[ 0.000364 ]
[ 0.00151207]]
dW2 = [[ 0.00363613 0.03153604 0.01162914 -0.01318316]]
db2 = [[0.06589489]]

(4)梯度下降

def update_parameters(parameters, grads, learning_rate = 1.2):
    
    W1 = parameters["W1"]
    W2 = parameters["W2"]
    b1 = parameters["b1"]
    b2 = parameters["b2"]
    
    dW1 = grads["dW1"]
    dW2 = grads["dW2"]
    db1 = grads["db1"]
    db2 = grads["db2"]
    
    W1 = W1 - learning_rate*dW1
    W2 = W2 - learning_rate*dW2
    b1 = b1 - learning_rate*db1
    b2 = b2 - learning_rate*db2
    
    parameters = {
        "W1":W1,
        "b1":b1,
        "W2":W2,
        "b2":b2
    }
    
    return parameters

# 测试函数 update_parameters
parameters, grads = update_parameters_test_case()
parameters = update_parameters(parameters, grads)

print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))

W1 = [[-0.00643025 0.01936718]
[-0.02410458 0.03978052]
[-0.01653973 -0.02096177]
[ 0.01046864 -0.05990141]]
b1 = [[-1.02420756e-06]
[ 1.27373948e-05]
[ 8.32996807e-07]
[-3.20136836e-06]]
W2 = [[-0.01041081 -0.04463285 0.01758031 0.04747113]]
b2 = [[0.00010457]]

4、在nn_model（）中集成1、2和3部分中的函数

def nn_model(X,Y,n_h, num_iterations = 10000, print_cost=False):
    
    # 1:layer_sizes(X, Y)
    np.random.seed(3)
    n_x = layer_sizes(X, Y)[0]
    n_y = layer_sizes(X, Y)[2]
    
    # 2:initialize_parameters(n_x,n_h,n_y)
    parameters = initialize_parameters(n_x,n_h,n_y)
    W1 = parameters["W1"]
    b1 = parameters["b1"]
    W2 = parameters["W2"]
    b2 = parameters["b2"]
    
    # 3:forward_propagation(X,parameters)
    for i in range(0,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)
        
        if print_cost and i % 1000 == 0:
            print("Cost after iteration %i: %f" %(i, cost))
    
    return parameters

#测试函数 nn_model
X_assess,Y_assess = nn_model_test_case()
parameters = nn_model(X_assess,Y_assess,4, num_iterations = 10000, print_cost=False)
print("W1 = " + str(parameters["W1"]))
print("b1 = " + str(parameters["b1"]))
print("W2 = " + str(parameters["W2"]))
print("b2 = " + str(parameters["b2"]))

W1 = [[-0.37522457 -0.66412896]
[ 0.01244063 0.01658567]
[-0.17662459 -0.2802151 ]
[-0.0120937 -0.01635778]]
b1 = [[-1.14066663]
[-1.69949907]
[-1.36650217]
[-1.81190248]]
W2 = [[ 3.60822483e-17 -2.77555756e-17 2.22044605e-17 6.10622664e-17]]
b2 = [[-0.26467576]]

5、预测

使用你的模型通过构建predict()函数进行预测。
使用正向传播来预测结果。

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

#测试函数predict
parameters,X_assess = predict_test_case()
predictions = predict(parameters , X_assess)
print("predictions mean = " + str(np.mean(predictions)))

predictions mean = 0.6666666666666666

# 集成测试数据
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))

Cost after iteration 0: 0.693048
Cost after iteration 1000: 0.288083
Cost after iteration 2000: 0.254385
Cost after iteration 3000: 0.233864
Cost after iteration 4000: 0.226792
Cost after iteration 5000: 0.222644
Cost after iteration 6000: 0.219731
Cost after iteration 7000: 0.217504
Cost after iteration 8000: 0.219504
Cost after iteration 9000: 0.218571

Text(0.5,1,'Decision Boundary for hidden layer size 4')

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

Accuracy: 90%

#拓展1：调整隐藏层大小
plt.figure(figsize=(16, 32))
hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]
for i, n_h in enumerate(hidden_layer_sizes):
    plt.subplot(5, 2, i+1)
    plt.title('Hidden Layer of size %d' % n_h)
    parameters = nn_model(X, Y, n_h, num_iterations = 5000)
    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y)
    predictions = predict(parameters, X)
    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)
    print ("Accuracy for {} hidden units: {} %".format(n_h, accuracy))

Accuracy for 1 hidden units: 67.5 %
Accuracy for 2 hidden units: 67.25 %
Accuracy for 3 hidden units: 90.75 %
Accuracy for 4 hidden units: 90.5 %
Accuracy for 5 hidden units: 91.25 %
Accuracy for 10 hidden units: 90.25 %
Accuracy for 20 hidden units: 90.0 %

# 拓展2：新的数据集
noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()

datasets = {"noisy_circles": noisy_circles,
            "noisy_moons": noisy_moons,
            "blobs": blobs,
            "gaussian_quantiles": gaussian_quantiles}

### START CODE HERE ### (choose your dataset)
dataset = "gaussian_quantiles"
### END CODE HERE ###

X, Y = datasets[dataset]
X, Y = X.T, Y.reshape(1, Y.shape[0])

# make blobs binary
if dataset == "blobs":
    Y = Y%2

# Visualize the data
plt.scatter(X[0, :], X[1, :], c=Y.reshape(X[0,:].shape), s=40, cmap=plt.cm.Spectral);