DenseNet实战：tensorflow2.X版本，DenseNet121图像分类任务（大数据集）

import numpy as np from tensorflow.keras.optimizers import Adam import cv2 from tensorflow.keras.preprocessing.image import img_to_array from sklearn.model_selection import train_test_split from tensorflow.python.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau from tensorflow.keras.applications import DenseNet121 import os import tensorflow as tf from tensorflow.python.keras.layers import Dense from tensorflow.python.keras.models import Sequential import albumentations norm_size = 224 datapath = 'data/train' EPOCHS = 20 INIT_LR = 1e-3 labelList = [] dicClass = {'cat': 0, 'dog': 1} classnum = 2 batch_size = 4 np.random.seed(42) def loadImageData(): imageList = [] listImage = os.listdir(datapath) for img in listImage: labelName = dicClass[img.split('.')[0]] print(labelName) labelList.append(labelName) dataImgPath = os.path.join(datapath, img) print(dataImgPath) imageList.append(dataImgPath) return imageList print("开始加载数据") imageArr = loadImageData() labelList = np.array(labelList) print("加载数据完成") print(labelList) trainX, valX, trainY, valY = train_test_split(imageArr, labelList, test_size=0.3, random_state=42) train_transform = albumentations.Compose([ albumentations.OneOf([ albumentations.RandomGamma(gamma_limit=(60, 120), p=0.9), albumentations.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.9), albumentations.CLAHE(clip_limit=4.0, tile_grid_size=(4, 4), p=0.9), ]), albumentations.OneOf([ albumentations.Blur(blur_limit=4, p=1), albumentations.MotionBlur(blur_limit=4, p=1), ], p=0.5), albumentations.HorizontalFlip(p=0.5), albumentations.ShiftScaleRotate(shift_limit=0.2, scale_limit=0.2, rotate_limit=20, interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_CONSTANT, p=1), albumentations.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0, p=1.0) ]) def generator(file_pathList,labels,batch_size,train_action=False): L = len(file_pathList) while True: input_labels = [] input_samples = [] for row in range(0, batch_size): temp = np.random.randint(0, L) X = file_pathList[temp] Y = labels[temp] image = cv2.imdecode(np.fromfile(X, dtype=np.uint8), -1) if train_action: image=train_transform(image=image)['image'] image = cv2.resize(image, (norm_size, norm_size), interpolation=cv2.INTER_LANCZOS4) image = img_to_array(image) input_samples.append(image) input_labels.append(Y) batch_x = np.asarray(input_samples)/255.0 batch_y = np.asarray(input_labels) yield (batch_x, batch_y) checkpointer = ModelCheckpoint(filepath='weights_best_Deset_model.hdf5', monitor='val_accuracy', verbose=1, save_best_only=True, mode='max') reduce = ReduceLROnPlateau(monitor='val_accuracy', patience=10, verbose=1, factor=0.5, min_lr=1e-6) #model = ResNet50(weights="imagenet",input_shape=(224,224,3),include_top=False, classes=classnum) #include_top=False 去掉最后的全连接层 model = Sequential() model.add(DenseNet121(include_top=False, pooling='avg', weights='imagenet')) model.add(Dense(classnum, activation='softmax')) # 如果想指定classes，有两个条件：include_top：True， weights：None。否则无法指定classes # model = ResNet50(weights=None,input_shape=(norm_size, norm_size, 3), classes=classnum) optimizer = Adam(learning_rate=INIT_LR) model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy']) history = model.fit(generator(trainX,trainY,batch_size,train_action=True), steps_per_epoch=len(trainX) / batch_size, validation_data=generator(valX,valY,batch_size,train_action=False), epochs=EPOCHS, validation_steps=len(valX) / batch_size, callbacks=[checkpointer, reduce]) model.save('my_model_Desnet.h5') print(history) loss_trend_graph_path = r"WW_loss.jpg" acc_trend_graph_path = r"WW_acc.jpg" import matplotlib.pyplot as plt print("Now,we start drawing the loss and acc trends graph...") # summarize history for accuracy fig = plt.figure(1) plt.plot(history.history["accuracy"]) plt.plot(history.history["val_accuracy"]) plt.title("Model accuracy") plt.ylabel("accuracy") plt.xlabel("epoch") plt.legend(["train", "test"], loc="upper left") plt.savefig(acc_trend_graph_path) plt.close(1) # summarize history for loss fig = plt.figure(2) plt.plot(history.history["loss"]) plt.plot(history.history["val_loss"]) plt.title("Model loss") plt.ylabel("loss") plt.xlabel("epoch") plt.legend(["train", "test"], loc="upper left") plt.savefig(loss_trend_graph_path) plt.close(2) print("We are done, everything seems OK...") # #windows系统设置10关机