# 我在训练自己的人脸分类模型的时候发现图片的维度不能太高，经过很多次测试过后觉得一般人脸图片分为28*28大小训练的效果比较好。
建议在使用其训练自己的物体识别模型的时候，尽量把图片压缩到28*28
# coding:utf-8 import time import matplotlib.pyplot as plt from autokeras import ImageClassifier from keras.engine.saving import load_model from keras.utils import plot_model from scipy.misc import imresize import numpy as np import pandas as pd import random import os from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 导入图片的函数 def read_img(path): nameList = os.listdir(path) n = len(nameList) # indexImg,columnImg = plt.imread(path+'/'+nameList[0]).shape x_train = np.zeros([n,28,28,1]);y_train=[] for i in range(n): x_train[i,:,:,0] = imresize(plt.imread(path+'/'+nameList[i]),[28,28]) y_train.append(np.int(nameList[i].split('.')[1])) return x_train,y_train x_train,y_train = read_img('./dataset') y_train = pd.DataFrame(y_train) n = len(y_train[y_train.iloc[:,0]==2]) x_train = np.array(x_train) x_wzp = np.random.choice(y_train[y_train.iloc[:,0]==1].index.tolist(),n,replace=False) x_train_w = x_train[x_wzp,:].copy() x_train_l = x_train[y_train[y_train.iloc[:,0]==2].index.tolist()].copy() x_train = np.concatenate([x_train_w,x_train_l],axis=0) print(x_train.shape) y_train = y_train.iloc[-208:,:].copy() # 对两组数据进行洗牌 index = random.sample(range(len(y_train)),len(y_train)) index = np.array(index) y_train = y_train.iloc[index,:] # y_train.plot() # plt.show() x_train = x_train[index,:,:,:] # x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2) # print(x_train.shape,y_train.shape,x_test.shape,y_test.shape) # y_test = y_test.values.reshape(-1) y_train = y_train.values.reshape(-1) # 数据测试 print(y_train) for i in range(5): n = i*20 img = x_train[n,:,:,:].reshape((28,28)) print(y_train[n]) plt.figure() plt.imshow(img,cmap='gray') plt.xticks([]) plt.yticks([]) plt.show() if __name__=='__main__': start = time.time() # 模型构建 model = ImageClassifier(verbose=True) # 搜索网络模型 model.fit(x_train,y_train,time_limit=1*60) # 验证最优模型 model.final_fit(x_train,y_train,x_train,y_train,retrain=True) # 给出评估结果 score = model.evaluate(x_train,y_train) # 识别结果 y_predict = model.predict(x_train) # y_pred = np.argmax(y_predict,axis=1) # 精确度 accuracy = accuracy_score(y_train,y_predict) # 打印出score与accuracy print('score:',score,' accuracy:',accuracy) print(y_predict,y_train) model_dir = r'./trainer/auto_learn_Model.h5' model_img = r'./trainer/imgModel_ST.png' # 保存可视化模型 # model.load_searcher().load_best_model().produce_keras_model().save(model_dir) # 加载模型 # automodel = load_model(model_dir) # 输出模型 structure 图 # plot_model(automodel, to_file=model_img) end = time.time() print('time:',end-start)
