1. 准备工作
在此 Codelab 中，您将学习使用大型数据集训练 CNN，这有助于避免过拟合问题。
前提条件
如果您之前未使用过 TensorFlow 构建卷积，可能需要完成构建卷积并执行池化 Codelab（我们在其中介绍了卷积和池化），以及构建卷积神经网络 (CNN) 以增强计算机视觉 Codelab（我们在其中探讨了如何使计算机更加高效地识别图像）。
学习内容
- 如何避免过拟合
您将构建的内容
- CNN，训练后用于识别猫或狗的图像（基于经典的 Kaggle 挑战）。
所需条件
您可以找到在 Colab 中运行其余 Codelab 的代码。
您还需要安装 TensorFlow 以及您在上一个 Codelab 中安装的库。
2. 使用猫和狗的大型数据集进行训练
在此 Codelab 中，您将使用一个真实且非常庞大的数据集，并了解它对避免过拟合的影响。
首先，使用必要的库设置您所需的开发环境。
import os
import zipfile
import random
import tensorflow as tf
from tensorflow.keras.optimizers import RMSprop
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from shutil import copyfile
3. 获取数据
Kaggle 挑战的完整数据集由 Microsoft 提供。您可以在此处找到该数据。如果以下代码块中的网址不起作用，请参阅注释中的说明。
# If the URL doesn't work, visit https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765
# And right click on the 'Download Manually' link to get a new URL to the dataset
# Note: This is a very large dataset and will take time to download
!wget --no-check-certificate "https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip" -O "/tmp/cats-and-dogs.zip"
local_zip = '/tmp/cats-and-dogs.zip'
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('/tmp')
zip_ref.close()
print(len(os.listdir('/tmp/PetImages/Cat/')))
print(len(os.listdir('/tmp/PetImages/Dog/')))
# Expected Output:
# 12501
# 12501
4. 准备数据
下载数据后，将其解压缩到训练和测试目录中。以下代码可以实现此目的：
try:
os.mkdir('/tmp/cats-v-dogs')
os.mkdir('/tmp/cats-v-dogs/training')
os.mkdir('/tmp/cats-v-dogs/testing')
os.mkdir('/tmp/cats-v-dogs/training/cats')
os.mkdir('/tmp/cats-v-dogs/training/dogs')
os.mkdir('/tmp/cats-v-dogs/testing/cats')
os.mkdir('/tmp/cats-v-dogs/testing/dogs')
except OSError:
pass
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 = "/tmp/PetImages/Cat/"
TRAINING_CATS_DIR = "/tmp/cats-v-dogs/training/cats/"
TESTING_CATS_DIR = "/tmp/cats-v-dogs/testing/cats/"
DOG_SOURCE_DIR = "/tmp/PetImages/Dog/"
TRAINING_DOGS_DIR = "/tmp/cats-v-dogs/training/dogs/"
TESTING_DOGS_DIR = "/tmp/cats-v-dogs/testing/dogs/"
split_size = .9
split_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)
# Expected output
# 666.jpg is zero length, so ignoring
# 11702.jpg is zero length, so ignoring
您可以使用以下代码检查您的数据是否已正确解压：
print(len(os.listdir('/tmp/cats-v-dogs/training/cats/')))
print(len(os.listdir('/tmp/cats-v-dogs/training/dogs/')))
print(len(os.listdir('/tmp/cats-v-dogs/testing/cats/')))
print(len(os.listdir('/tmp/cats-v-dogs/testing/dogs/')))
# Expected output:
# 11250
# 11250
# 1250
# 1250
5. 定义模型
接下来，将模型定义为一系列具有最大池化的卷积层。
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')
])
model.compile(optimizer=RMSprop(lr=0.001), loss='binary_crossentropy', metrics=['accuracy'])
6. 训练模型
现在模型已经定义，您可以使用 ImageDataGenerator 训练模型。
TRAINING_DIR = "/tmp/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 = "/tmp/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))
# Expected Output:
# Found 22498 images belonging to 2 classes.
# Found 2500 images belonging to 2 classes.
为了训练模型，您现在可以调用 model.fit_generator，并将其传递给您创建的生成器。
# Note that this may take some time.
history = model.fit_generator(train_generator,
epochs=15,
verbose=1,
validation_data=validation_generator)
7. 探索结果
您可以使用以下代码探索训练和验证的准确率并绘制图表。请使用此代码了解何时达到训练效率上限，并查看是否过拟合。
%matplotlib inline
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
#-----------------------------------------------------------
# Retrieve a list of list results on training and test data
# sets for each training epoch
#-----------------------------------------------------------
acc=history.history['accuracy']
val_acc=history.history['val_accuracy']
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()
8. 测试模型
如果要使用模型旋转图像，可以使用以下代码。请上传图像，了解模型如何对图像进行分类！
# Here's a codeblock just for fun. You should be able to upload an image here
# and have it classified without crashing
import numpy as np
from google.colab import files
from keras.preprocessing import image
uploaded = files.upload()
for fn in uploaded.keys():
# predicting images
path = '/content/' + fn
img = image.load_img(path, target_size=(150, 150))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict(images, batch_size=10)
print(classes[0])
if classes[0]>0.5:
print(fn + " is a dog")
else:
print(fn + " is a cat")
9. 恭喜
现在，您已了解了机器学习的基础知识，包括从基本原理到创建卷积神经网络！
了解详情
如需了解机器学习和 TensorFlow 可以如何帮助您创建计算机视觉模型，请访问 TensorFlow.org。