关于python:深度学习实践catsvsdogs数据集

一.下载数据集

数据集来自kaggle 数据集的Dogs vs Cats数据集
百度云盘下载地址
链接：https://pan.baidu.com/s/177uL...
提取码：9j40

二.对数据进行划分

1.创立文件夹如下:

1. train

   1. cats
   2. dogs

2. val

   1. cats
   2. dogs
3. test

2.对数据进行划分,代码如下

def data_cats_processing():    base_train_path = r'E:\mldata\dogvscat\data\train'    base_dest_train_path = r'E:\mldata\dogvscat\train\cats'    base_dest_test_path = r'E:\mldata\dogvscat\test\cats'    base_dest_val_path = r'E:\mldata\dogvscat\val\cats'    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(10000)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_train_path, fname)        shutil.copyfile(src_path, dest_path)    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(10001, 11501)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_val_path, fname)        shutil.copyfile(src_path, dest_path)    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(11501, 12500)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_test_path, fname)        shutil.copyfile(src_path, dest_path)def data_dog_processing():    base_train_path = r'E:\mldata\dogvscat\data\train'    base_dest_train_path = r'E:\mldata\dogvscat\train\dogs'    base_dest_test_path = r'E:\mldata\dogvscat\test\dogs'    base_dest_val_path = r'E:\mldata\dogvscat\val\dogs'    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(10000)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_train_path, fname)        shutil.copyfile(src_path, dest_path)    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(10001, 11501)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_val_path, fname)        shutil.copyfile(src_path, dest_path)    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(11501, 12500)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_test_path, fname)        shutil.copyfile(src_path, dest_path)

三.批量读取数据

代码:

def datagen():    train_datagen = ImageDataGenerator(        rescale=1 / 255.)    test_datagen = ImageDataGenerator(rescale=1 / 255.)    train_dir = r'E:\mldata\dogvscat\train'    val_dir = r'E:\mldata\dogvscat\val'    train_datagen = train_datagen.flow_from_directory(        train_dir,        target_size=(150, 150),        batch_size=20,        class_mode='binary'    )    val_datagen = test_datagen.flow_from_directory(        val_dir,        target_size=(150, 150),        batch_size=20,        class_mode='binary'    )    return train_datagen, val_datagen

四.创立神经网络模型

模型一.根底神经网络

应用后欠拟合,

def base_model1():    # acc 0.8255  val_acc 0.7940 , 欠拟合    # 解决形式    # 1.尝试更好的优化器    # 2.尝试更大网络    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=RMSprop(learning_rate=1e-4), metrics=['acc'])    return model

模型二.应用adam作为优化器

def create_model2():    # 应用 adam    #  acc: 0.8537  - val_acc: 0.7920    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['acc'])    return model

五.开始训练

    # 加载数据    train_datagen, val_datagen = datagen()    # 创立模型    model = create_model2()    # 创立tensorboard应用的log文件夹    log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")    # 创立tensorboard callback 回调    tensorboard_callback = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)    # 开始训练    model.fit_generator(train_datagen,                        steps_per_epoch=100,                        epochs=30,                        validation_data=val_datagen,                        validation_steps=50,                        callbacks=[tensorboard_callback])

残缺代码

import os, shutilfrom tensorflow.keras.preprocessing.image import ImageDataGeneratorfrom tensorflow.keras.models import Sequentialfrom tensorflow.keras.layers import Dense, MaxPooling2D, Conv2D, Flatten, Dropoutfrom tensorflow.keras.optimizers import RMSprop, Adam, SGDimport datetimefrom tensorflow.keras import callbacksfrom tensorflow.keras.applications import VGG16from tensorflow.keras.models import Model# 划分数据集 共25000# 训练集 20000# 验证集 3000# 测试集 2000def data_cats_processing():    base_train_path = r'E:\mldata\dogvscat\data\train'    base_dest_train_path = r'E:\mldata\dogvscat\train\cats'    base_dest_test_path = r'E:\mldata\dogvscat\test\cats'    base_dest_val_path = r'E:\mldata\dogvscat\val\cats'    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(10000)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_train_path, fname)        shutil.copyfile(src_path, dest_path)    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(10001, 11501)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_val_path, fname)        shutil.copyfile(src_path, dest_path)    cat_fnames = ['cat.{}.jpg'.format(i) for i in range(11501, 12500)]    for fname in cat_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_test_path, fname)        shutil.copyfile(src_path, dest_path)def data_dog_processing():    base_train_path = r'E:\mldata\dogvscat\data\train'    base_dest_train_path = r'E:\mldata\dogvscat\train\dogs'    base_dest_test_path = r'E:\mldata\dogvscat\test\dogs'    base_dest_val_path = r'E:\mldata\dogvscat\val\dogs'    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(10000)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_train_path, fname)        shutil.copyfile(src_path, dest_path)    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(10001, 11501)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_val_path, fname)        shutil.copyfile(src_path, dest_path)    dog_fnames = ['dog.{}.jpg'.format(i) for i in range(11501, 12500)]    for fname in dog_fnames:        src_path = os.path.join(base_train_path, fname)        dest_path = os.path.join(base_dest_test_path, fname)        shutil.copyfile(src_path, dest_path)def datagen():    train_datagen = ImageDataGenerator(        rescale=1. / 255,        rotation_range=40,        width_shift_range=0.2,        height_shift_range=0.2,        shear_range=0.2,        zoom_range=0.2,        horizontal_flip=True    )    test_datagen = ImageDataGenerator(rescale=1 / 255.)    train_dir = r'E:\mldata\dogvscat\train'    val_dir = r'E:\mldata\dogvscat\val'    train_datagen = train_datagen.flow_from_directory(        train_dir,        target_size=(150, 150),        batch_size=20,        class_mode='binary'    )    val_datagen = test_datagen.flow_from_directory(        val_dir,        target_size=(150, 150),        batch_size=20,        class_mode='binary'    )    return train_datagen, val_datagendef base_model1():    # acc 0.8255  val_acc 0.7940 , 欠拟合    # 解决形式    # 1.尝试更好的优化器    # 2.尝试更大网络    # 3.更多数据,应用数据加强    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=RMSprop(learning_rate=1e-4), metrics=['acc'])    return modeldef create_model2():    # 应用 adam    #  acc: 0.8537  - val_acc: 0.7920    # 解决形式    # 1.尝试更好的优化器    # 2.尝试更大网络    # 3.更多数据,应用数据加强    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['acc'])    return modeldef create_model3():    # 应用 adam,数据增强器    # acc: 0.7487 - val_acc: 0.7760    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['acc'])    return modeldef create_model4():    # 应用 adam,增大网络    # acc: 0.7366 - val_loss: 0.5200 - val_acc: 0.7350    # 不应用数据加强    #  acc: 0.8382 - val_loss: 0.4190 - val_acc: 0.8110    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(256, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(1024, activation='relu'))    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['acc'])    return modeldef create_model5():    # 应用 vgg网络    #  acc: 0.8466 - val_loss: 0.3708 - val_acc: 0.8320    model = Sequential()    model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(64, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Conv2D(128, (3, 3), activation='relu'))    model.add(MaxPooling2D((2, 2)))    model.add(Flatten())    model.add(Dense(512, activation='relu'))    model.add(Dense(1, activation='sigmoid'))    model.compile(loss='binary_crossentropy', optimizer=Adam(learning_rate=1e-4), metrics=['acc'])    return modeldef vgg_model():    # 应用 vgg网络    #  acc: 0.8466 - val_loss: 0.3708 - val_acc: 0.8320    top_model = Sequential()    top_model.add(Flatten())    top_model.add(Dense(256, activation='relu'))    top_model.add(Dropout(0.5))    top_model.add(Dense(1, activation='sigmoid'))    base_model = VGG16(weights='imagenet',                       include_top=False,                       input_shape=(150, 150, 3))    model = Model(inputs=base_model.input, outputs=top_model(base_model.output))    for layer in model.layers[:-1]:        layer.trainable = False    model.compile(loss='categorical_crossentropy',                  optimizer=SGD(lr=1e-4, momentum=0.9),                  metrics=['accuracy'])    model.summary()    return modelif __name__ == '__main__':    # train_data_processing()    # 加载数据    train_datagen, val_datagen = datagen()    # 创立模型    model = create_model2()    # 创立tensorboard应用的log文件夹    log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")    # 创立tensorboard callback 回调    tensorboard_callback = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)    # 开始训练    model.fit_generator(train_datagen,                        steps_per_epoch=100,                        epochs=30,                        validation_data=val_datagen,                        validation_steps=50,                        callbacks=[tensorboard_callback])