以吴恩达DL第一课第二周为基础的猫识别算法
本文记录了在学习BP的过程中,借由吴恩达Deep Learning第一课第二周为模板写的猫识别算法。(其实相当于是课后作业hh)
1.加载库,用matplot作图,里面的lr_utils是吴恩达打包好的一个用来加载数据的包,如果是直接运行的话可能会报错,最后面给出一个别的大佬写的代码也可以用。
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 %matplotlib inline
2.加载训练集和测试集
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
3.记录样本数m和图像的长宽(长宽相等,只用了一个num_px),打印一下看看长什么样
m_train=train_set_x_orig.shape[0] m_test=test_set_x_orig.shape[0] num_px=train_set_x_orig.shape[1] print ("Number of training examples: m_train = " + str(m_train)) print ("Number of testing examples: m_test = " + str(m_test)) print ("Height/Width of each image: num_px = " + str(num_px)) print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)") print ("train_set_x shape: " + str(train_set_x_orig.shape)) print ("train_set_y shape: " + str(train_set_y.shape)) print ("test_set_x shape: " + str(test_set_x_orig.shape)) print ("test_set_y shape: " + str(test_set_y.shape))
4.把获得的数据转换成一个二维数组的形式,打印看看转换之后的样子
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: " + str(train_set_x_flatten.shape)) print ("train_set_y shape: " + str(train_set_y.shape)) print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape)) print ("test_set_y shape: " + str(test_set_y.shape)) print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))
5.彩色图像像素值实际上是一个由三个数字组成的向量,范围从0到255。这里给他标准化一下,不是图像需要从每个示例中减去整个数组的平均值,然后将每个样本除以整个数组的标准偏差,图像的话直接除以255就完事儿了
train_set_x = train_set_x_flatten/255. test_set_x = test_set_x_flatten/255.
6.使用np.exp写激活函数sigmoid
def sigmoid(z): s=1/(1+np.exp(-z)) return s
7.由于是简单的单层神经网络,直接初始化W,B为0就可以了,assert负责检查一下w、b的格式(形状),看看和预期的一不一致,这里最好注意一下,不然深层的网络查错会很麻烦
def initialize_with_zeros(dim): ### w -- initialized vector of shape (dim, 1) b -- initialized scalar (corresponds to the bias) ### w=np.zeros(shape=(dim,1)) b=0 assert(w.shape == (dim, 1)) assert(isinstance(b, float) or isinstance(b, int)) return w, b
8.前向传播的模块 np.squeeze()负责删掉维度为1的,防止后面cost出现一些奇奇怪怪的形状
def propagate(w, b, X, Y): m = X.shape[1] A=sigmoid(np.dot(w.T,X)+b) cost = -1/m * np.sum(Y*np.log(A)+(1-Y)*np.log(1-A)) dw = 1/m *np.dot(X ,(A-Y).T) db=1/m*np.sum(A-Y) assert(dw.shape == w.shape) assert(db.dtype == float) cost = np.squeeze(cost) assert(cost.shape == ()) grads = {"dw": dw,"db": db} return grads, cost
9.优化optimize的模块,每迭代100次记录一下他的代价,并保存到costs里面,方便后面作图
def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False): costs = [] for i in range(num_iterations): grads, cost = 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 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
10.对图片进行预测的模块
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) for i in range(A.shape[1]): Y_prediction[0,i] = 1 if A[0,i] > 0.5 else 0 ### if A[0,i]>0.5: Y_prediction[0,i]=1 else: Y_prediction[0,i]=0 ### assert(Y_prediction.shape == (1, m)) return Y_prediction
11.模型整理(其实就是把之前做的模块整合到一起),打印一下此时的cost
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]) parameters, grads, costs = optimize(w,b,X_train,Y_train,num_iterations,learning_rate,print_cost) w = parameters["w"] b = parameters["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
12.跑模型~
d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations = 2000, learning_rate = 0.005, print_cost = True)
13.收获的时候到了,看看你的。这个index可以随便换,只要是测试集里面的图片标签都可以(大概是),验收一下成果
index = 1 plt.imshow(test_set_x[:,index].reshape((num_px, num_px, 3))) print ("y = " + str(test_set_y[0,index]) + ", you predicted that it is a \"" + classes[int(d["Y_prediction_test"][0,index])].decode("utf-8") + "\" picture.")
14.做个图,看看loss(也就是cost)
costs = np.squeeze(d['costs']) plt.plot(costs) plt.ylabel('cost') plt.xlabel('iterations (per hundreds)') plt.title("Learning rate =" + str(d["learning_rate"])) plt.show()
完结撒花!对了还有数据集我不知道怎么弄到博客园上,先试试,不行的话我再开一个专门上传这个数据集
15.补一个大佬给的加载文件的方法。
import numpy as np import h5py def load_dataset(): train_dataset = h5py.File('datasets/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('datasets/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])) return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes #来自一位csdn大佬,有机会的话希望能给他点一点小红心