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目录

1 简介

在本节中，我们将学习如何使用卷积神经网络，并使用更大的数据集，这有助于避免过度拟合的问题！

2 使用更大的数据集进行训练-猫和狗

在之前的实验中，训练了一个马与人类数据的分类器。尽管在训练集上获得了很好的训练结果，但是当我们尝试用真实图像进行分类时，存在许多错误，主要是由于过度拟合–CNN在见过的数据方面表现非常好。

3 导入库

import osimport zipfileimport randomimport tensorflow as tffrom tensorflow.keras.optimizers import RMSpropfrom tensorflow.keras.preprocessing.image import ImageDataGeneratorfrom shutil import copyfile

4 获取数据

local_zip = "./cats-and-dogs.zip"zip_ref = zipfile.ZipFile(local_zip,"r")zip_ref.extractall("cats-and-dogs")zip_ref.close()

print(len(os.listdir("./cats-and-dogs/PetImages/Cat/")))print(len(os.listdir("./cats-and-dogs/PetImages/Dog/")))

1250112501

5 准备数据

def split_data(SOURCE,TRAINING,TESTING,SPLIT_SIZE):    files = []    for filename in os.listdir(SOURCE):        file = SOURCE + filename        if os.path.getsize(file) > 0:            files.append(filename)        else:            print(filename+" is zero length,so ignoring.")        training_length = int(len(files)*SPLIT_SIZE)    testing_length = int(len(files)-training_length)    shuffled_set = random.sample(files,len(files))    training_set = shuffled_set[0:training_length]    testing_set = shuffled_set[:testing_length]        for filename in training_set:        this_file = SOURCE + filename        destination = TRAINING + filename        copyfile(this_file, destination)     for filename in testing_set:        this_file = SOURCE + filename        destination = TESTING + filename        copyfile(this_file, destination)                CAT_SOURCE_DIR = "./cats-and-dogs/PetImages/Cat/"TRAINING_CATS_DIR = "./cats-v-dogs/training/cats/"TESTING_CATS_DIR = "./cats-v-dogs/testing/cats/"DOG_SOURCE_DIR = "./cats-and-dogs/PetImages/Dog/"TRAINING_DOGS_DIR = "./cats-v-dogs/training/dogs/"TESTING_DOGS_DIR = "./cats-v-dogs/testing/dogs/" split_size = .9split_data(CAT_SOURCE_DIR, TRAINING_CATS_DIR, TESTING_CATS_DIR, split_size)split_data(DOG_SOURCE_DIR, TRAINING_DOGS_DIR, TESTING_DOGS_DIR, split_size)

666.jpgis zero length,so ignoring.11702.jpgis zero length,so ignoring.

print(len(os.listdir('./cats-v-dogs/training/cats/')))print(len(os.listdir('./cats-v-dogs/training/dogs/')))print(len(os.listdir('./cats-v-dogs/testing/cats/')))print(len(os.listdir('./cats-v-dogs/testing/dogs/')))

112501125012501250

6 定义模型

model = tf.keras.models.Sequential([    tf.keras.layers.Conv2D(16,(3,3),activation="relu",input_shape=(150,150,3)),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Conv2D(32,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Conv2D(64,(3,3),activation="relu"),    tf.keras.layers.MaxPooling2D(2,2),    tf.keras.layers.Flatten(),    tf.keras.layers.Dense(512,activation="relu"),    tf.keras.layers.Dense(1,activation="sigmoid")])

WARNING:tensorflow:From D:\software\Anaconda\anaconda\lib\site-packages\tensorflow\python\ops\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.Instructions for updating:Colocations handled automatically by placer.

model.compile(optimizer=RMSprop(lr=0.0001),              loss="binary_crossentropy",              metrics=["acc"])

7 训练模型

TRAINING_DIR = "./cats-v-dogs/training/"train_datagen = ImageDataGenerator(rescale=1.0/255.)train_generator = train_datagen.flow_from_directory(TRAINING_DIR,                                                    batch_size=100,                                                    class_mode="binary",                                                    target_size=(150,150))VALIDATION_DIR = "./cats-v-dogs/testing/"validation_datagen = ImageDataGenerator(rescale=1.0/255.)validation_generator = validation_datagen.flow_from_directory(VALIDATION_DIR,                                                              batch_size=100,                                                              class_mode='binary',                                                              target_size=(150, 150))

Found 22498 images belonging to 2 classes.Found 2500 images belonging to 2 classes.

history = model.fit_generator(train_generator,                              epochs=15,                              verbose=1,                              validation_data=validation_generator)

Epoch 1/15 37/225 [===>..........................] - ETA: 5:53 - loss: 0.6875 - acc: 0.5338Epoch 13/1525/25 [==============================] - 16s 643ms/step - loss: 0.2902 - acc: 0.8780225/225 [==============================] - 390s 2s/step - loss: 0.3139 - acc: 0.8636 - val_loss: 0.2902 - val_acc: 0.8780Epoch 14/1525/25 [==============================] - 16s 640ms/step - loss: 0.2689 - acc: 0.8864225/225 [==============================] - 390s 2s/step - loss: 0.2978 - acc: 0.8698 - val_loss: 0.2689 - val_acc: 0.8864Epoch 15/1525/25 [==============================] - 16s 641ms/step - loss: 0.2471 - acc: 0.8952225/225 [==============================] - 390s 2s/step - loss: 0.2840 - acc: 0.8797 - val_loss: 0.2471 - val_acc: 0.8952

8 探索数据集

%matplotlib inlineimport matplotlib.image  as mpimgimport matplotlib.pyplot as plt#-----------------------------------------------------------# Retrieve a list of list results on training and test data# sets for each training epoch#-----------------------------------------------------------acc=history.history['acc']val_acc=history.history['val_acc']loss=history.history['loss']val_loss=history.history['val_loss'] epochs=range(len(acc)) # Get number of epochs #------------------------------------------------# Plot training and validation accuracy per epoch#------------------------------------------------plt.plot(epochs, acc, 'r', "Training Accuracy")plt.plot(epochs, val_acc, 'b', "Validation Accuracy")plt.title('Training and validation accuracy')plt.figure() #------------------------------------------------# Plot training and validation loss per epoch#------------------------------------------------plt.plot(epochs, loss, 'r', "Training Loss")plt.plot(epochs, val_loss, 'b', "Validation Loss")plt.figure()

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