100*100层MLPClassifier神经网络AI算法分析图片标签
import matplotlib.pyplot as pltfrom sklearn.neural_network import MLPClassifierfrom sklearn.feature_selection import SelectPercentilefrom sklearn.decomposition import PCAimport numpy as npfrom PIL import Imageimport osbasedir=os.path.abspath('.')X=[]y=[]target=["cat","dog"]for i in os.listdir("img"):f=os.path.join(basedir+"/img",i)image=Image.open(f).convert('F')image=image.resize((100,100))data=np.array(image).astype(dtype=float)data=np.array(data).flatten()X.append(data)y.append(0 if i[0]=='c' else 1)clf=MLPClassifier(hidden_layer_sizes=(100,100))dec=PCA(whiten=True)X=np.array(X)# dec.fit(X)# X=dec.transform(X)# X=fs.fit_transform(X,y)clf.fit(X,y)fig=plt.figure(figsize=(12,3))# fig,ax=plt.subplots(2,4,figsize=(12,6))a=1for i in os.listdir("predict"):f=os.path.join(basedir+"/predict",i)image=Image.open(f).convert('F')image=image.resize((100,100))data=np.array(image).astype(dtype=float)data = np.array(data).flatten()# data=dec.transform(np.array([data]))# data = fs.fit_transform(data)p=clf.predict([data])print("%s:%s" % (f,target[p[0]]))ax=fig.add_subplot(1,4,a)ax.set_xticks([]); ax.set_yticks([])a+=1ax.imshow(image)ax.set_title("%s" % target[p[0]])plt.show()
