【深度学习走进tensorflow2.0】tensorflow2.0 如何做图像分类模型训练和预测

mac2025-12-04 7

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创建一个数据集文件夹并命名（如 dataset）在数据集文件中创建一个名称为 train 的子文件夹在数据集文件中创建一个名称为 val 的子文件夹在 train 文件夹中，为每个你要训练的对象创建文件夹并命名在 val 文件夹中，为每个你要训练的对象创建文件夹并命名把每个对象的图像放在 train 文件夹下对应名称的子文件夹，这些图像是用于训练模型的图像，为了训练出精准度较高的模型，我建议每个对象收集大约500张以上图像。

目录结构如下：

使用tensorflow2.0 训练残差神经网络resnet-50 。

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator import json import os from tensorflow.keras.applications.resnet50 import ResNet50 batch_size = 32 epochs = 100 IMG_HEIGHT = 224 IMG_WIDTH = 224 num_classes=5 image_input=224 PATH = os.path.join('/home/dongli/tensorflow2.0/corpus/dataset/') train_dir = os.path.join(PATH, 'train') validation_dir = os.path.join(PATH, 'val') train_animal_dir = os.path.join(train_dir, 'animal') train_flower_dir = os.path.join(train_dir, 'flower') train_guitar_dir = os.path.join(train_dir, 'guitar') train_houses_dir = os.path.join(train_dir, 'houses') train_plane_dir = os.path.join(train_dir, 'plane') validation_animal_dir = os.path.join(train_dir, 'animal') validation_flower_dir = os.path.join(train_dir, 'flower') validation_guitar_dir = os.path.join(train_dir, 'guitar') validation_houses_dir = os.path.join(train_dir, 'houses') validation_plane_dir = os.path.join(train_dir, 'plane') num_animal_tr = len(os.listdir(train_animal_dir)) num_flower_tr = len(os.listdir(train_flower_dir)) num_guitar_tr = len(os.listdir(train_guitar_dir)) num_houses_tr = len(os.listdir(train_houses_dir)) num_plane_tr = len(os.listdir(train_plane_dir)) num_animal_val = len(os.listdir(validation_animal_dir)) num_flower_val = len(os.listdir(validation_flower_dir)) num_guitar_val = len(os.listdir(validation_guitar_dir)) num_houses_val = len(os.listdir(validation_houses_dir)) num_plane_val = len(os.listdir(validation_plane_dir)) total_train = num_animal_tr+num_flower_tr+num_guitar_tr+num_houses_tr+num_plane_tr total_val = num_animal_val + num_flower_val+num_guitar_val+num_houses_val+num_plane_val print("Total training images:", total_train) print("Total validation images:", total_val) # 训练集 # 对训练图像应用了重新缩放，45度旋转，宽度偏移，高度偏移，水平翻转和缩放增强。 image_gen_train = ImageDataGenerator( rescale=1./255, width_shift_range=0.1, height_shift_range=0.1 ) train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') # 验证集 image_gen_val = ImageDataGenerator(rescale=1./255) val_data_gen = image_gen_val.flow_from_directory(batch_size=batch_size, directory=validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical') # 创建模型 model=ResNet50(include_top=True, weights=None,classes=num_classes) # 编译模型 model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) # 模型总结 model.summary() # 模型保存格式定义 model_class_dir='./flower_model/' class_indices = train_data_gen.class_indices class_json = {} for eachClass in class_indices: class_json[str(class_indices[eachClass])] = eachClass with open(os.path.join(model_class_dir, "model_class.json"), "w+") as json_file: json.dump(class_json, json_file, indent=4, separators=(",", " : "),ensure_ascii=True) json_file.close() print("JSON Mapping for the model classes saved to ", os.path.join(model_class_dir, "model_class.json")) model_name = 'model_ex-{epoch:03d}_acc-{val_accuracy:03f}.h5' trained_model_dir='./flower_model/' model_path = os.path.join(trained_model_dir, model_name) checkpoint = tf.keras.callbacks.ModelCheckpoint( filepath=model_path, monitor='val_accuracy', verbose=2, save_weights_only=True, save_best_only=True, mode='max', period=1) def lr_schedule(epoch): # Learning Rate Schedule lr =1e-3 total_epochs =epoch check_1 = int(total_epochs * 0.9) check_2 = int(total_epochs * 0.8) check_3 = int(total_epochs * 0.6) check_4 = int(total_epochs * 0.4) if epoch > check_1: lr *= 1e-4 elif epoch > check_2: lr *= 1e-3 elif epoch > check_3: lr *= 1e-2 elif epoch > check_4: lr *= 1e-1 return lr #lr_scheduler =tf.keras.callbacks.LearningRateScheduler(lr_schedule) lr_scheduler = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,patience=5, min_lr=0.001) num_train = len(train_data_gen.filenames) num_test = len(val_data_gen.filenames) print(num_train,num_test) # 模型训练 # 使用fit_generator方法ImageDataGenerator来训练网络。 history = model.fit_generator( train_data_gen, steps_per_epoch=int(num_train / batch_size), epochs=epochs, validation_data=val_data_gen, validation_steps=int(num_test / batch_size), callbacks=[checkpoint,lr_scheduler])

模型保存

模型保存，如果模型保存了模型训练好的权重和图结构信息。采用load_model()导入、若需要只导入权重文件，采用load_weights()方式，需要重新构建一样的模型和编译模型，方能成功。

def create_model(): base_model=ResNet50(include_top=True, weights=None,classes=class_num) model = tf.keras.Model(inputs=base_model.input, outputs=base_model.output) return model # 重新构建模型 model=create_model() # 编译模型 model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) # 导入权重文件 model.load_weights('./flower_model/model_ex-023_acc-0.864130.h5')

加载训练好的模型权重去预测新图片：

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from PIL import Image import numpy as np from io import BytesIO import json import requests CLASS_INDEX = None import keras input_image_size=224 class_num=5 model_jsonPath='./flower_model/model_class.json' def preprocess_input(x): x *= (1./255) return x def decode_predictions(preds, top=5, model_json=""): global CLASS_INDEX if CLASS_INDEX is None: CLASS_INDEX = json.load(open(model_json)) results = [] for pred in preds: top_indices = pred.argsort()[-top:][::-1] for i in top_indices: each_result = [] each_result.append(CLASS_INDEX[str(i)]) each_result.append(pred[i]) results.append(each_result) return results prediction_results = [] prediction_probabilities = [] url='https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1573119512&di=95ad0908ab5e5ce22a674471f0e4d5d1&imgtype=jpg&er=1&src=http%3A%2F%2Fwww.sinaimg.cn%2Fjc%2Fp%2F2007-06-21%2FU2143P27T1D450794F3DT20070621164533.jpg' response=requests.get(url).content image_input=response image_input = Image.open(BytesIO(image_input)) image_input = image_input.convert('RGB') image_input = image_input.resize((input_image_size,input_image_size)) image_input = np.expand_dims(image_input, axis=0) image_to_predict = image_input.copy() image_to_predict = np.asarray(image_to_predict, dtype=np.float64) image_to_predict = preprocess_input(image_to_predict) from tensorflow.keras.applications.resnet50 import ResNet50 from tensorflow import keras def create_model(): base_model=ResNet50(include_top=True, weights=None,classes=class_num) model = tf.keras.Model(inputs=base_model.input, outputs=base_model.output) return model model=create_model() # 编译模型 model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy']) model.load_weights('./flower_model/model_ex-023_acc-0.864130.h5') prediction = model.predict(x=image_to_predict) try: predictiondata = decode_predictions(prediction, top=int(class_num), model_json=model_jsonPath) for result in predictiondata: prediction_results.append(str(result[0])) prediction_probabilities.append(result[1] * 100) except: raise ValueError("An error occured! Try again.") print(prediction_results[0],prediction_probabilities[0])

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【深度学习 走进tensorflow2.0】tensorflow2.0 如何做图像分类模型训练和预测

【深度学习走进tensorflow2.0】tensorflow2.0 如何做图像分类模型训练和预测