吴恩达老师的深度学习单层神经网络实现
吴恩达老师的深度学习单层神经网络实现
数据集下载：train_catvnoncat.h5（训练集），test_catvnoncat.h5（测试集）
链接:link. 提取码：qxm6
代码（自己拿别人的改的）第一次写这东西，我也不会插入ipynp的文件。
# To add a new cell, type '# %%'
# To add a new markdown cell, type '# %% [markdown]'
# %%
import numpy as np
import h5py
#加载数据集
train_dataset = h5py.File('C:/Users/Fanta/Downloads/train_catvnoncat.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('C:/Users/Fanta/Downloads/test_catvnoncat.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
print(train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes)
# %%
train_dataset.keys() #相当于一个字典，key 对应value
# %%
train_set_x_orig.shape #209张64*64的RGB图片
# %%
train_set_y_orig.shape
# %%
train_set_y_orig
# %%
classes #两个类别 一个是猫一个不是猫
# %%
#激活函数activition function
def ReLU(z):
mz,nz = z.shape
zero = np.zeros((mz,nz))
return np.maximum(zero,z)
#RuntimeWarning: overflow encountered in exp
def sigmoid(z):
return 1.0/(1+np.exp(-z))
#将4维数组转换为2维
def transformArray(inX):
shape = inX.shape
shape1 = shape[1]*shape[2]*shape[3]
transformedX = np.mat(np.zeros((shape1,shape[0])))
for i,item in enumerate(inX):
transformedItem = item.flatten().reshape(-1,1)
transformedX[:,i] = transformedItem
return transformedX
# %%
train_data_x = transformArray(train_set_x_orig).T
test_data_x = transformArray(test_set_x_orig).T
train_data_y = train_set_y_orig.T
test_data_y = test_set_y_orig.T
# %%
print(train_data_x.shape,test_data_x.shape)
# %%
#初始化权重
# W = np.random.randn(train_data_x.shape[1],1)
# b = np.random.randn(1)
# %%
print(W.shape,b.shape)
# %%
'''这是一个实验'''
k = np.array([0,2,0.3,0.8,0.1])
k = (k>0.5)*1
k
# %%
#损失函数
def lossFunc(A,y_train):
pass
#单层神经网络
class myOneLNN:
def __init__(self,W,b,alpha,af,iterNum): #af 是一个激活函数 iterNum是迭代次数
self.W = W
self.b = b
self.af = af
self.iterNum = iterNum
self.alpha = alpha
def trainFunc(self,X_train,y_train):
m = X_train.shape[0]
X = X_train
Y = y_train
Z = X.dot(self.W) + self.b #前向传播过程
for i in range(iterNum):
Z = X.dot(self.W) + self.b
A = self.af(Z)
dZ = A - Y
dW =X.T.dot(dZ)
db = dZ.sum()/X.shape[0]
self.W -= self.alpha*dW
self.b -= self.alpha*db
return self.W,self.b
def predictFunc(self,X_test):
y_pred = self.af(X_test.dot(self.W) + self.b)
y_pred = 1*(y_pred>0.5)
return y_pred
def testErrors(self,X_test,y_test):
acc = (self.predictFunc(X_test) == y_test).sum()*1.0/len(y_test)
print("acc: {}".format(acc))
# %%
iterNum = 500
W = np.random.randn(train_data_x.shape[1],1)
b = 0
af = sigmoid
alpha = 0.001
oneLNN = myOneLNN(W,b,alpha,af,iterNum)
# %%
W1,b1 = oneLNN.trainFunc(train_data_x,train_data_y)
b1
# %%
oneLNN.predictFunc(test_data_x)
# %%
oneLNN.testErrors(test_data_x,test_data_y)
# %%
[1]https://blog.csdn.net/zhuzuwei/article/details/77950330
作者：another_fantasy
