卷积神经网络
实验环境
keras 2.1.5
tensorflow 1.4.0
实验工具
Jupyter Notebook
实验一：手写数字分类
实验目的
将手写数字的灰度图像（28像素×28像素）分类为10个类别（0到9）。
数据集
MNIST数据集：
MNIST数据集，它是机器学习领域中的一个经典数据集。这是一套60000个训练图像，外加10,000个测试图像，由国家标准与技术研究院（NIST在MNIST）在20世纪80年代制定而成。你可以将“解决”MNIST分类问题认为深度学习的“Hello World”。
实验过程
1.基本的卷积神经网络的结构:
它是一系列的二维卷积层和二维最大池化层。
重要的是，一个卷积网络需要输入有形状的张量（图像高、图像宽、图像通道）（不包括批量处理的维度）。在此例子中，将设定卷积网络处理大小为（28,28,1）的输入，这是mnist图像的格式。我们通过将参数input_shape=(28, 28, 1)传输到第一层来实现。
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
２.卷积网络的具体架构:
model.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 3, 3, 64) 36928
=================================================================
Total params: 55,744
Trainable params: 55,744
Non-trainable params: 0
_________________________________________________________________
'''
#每个二维卷积层和二维最大池化层的输出都是一个三维的形状张量(高度、宽度、通道数)。随着网络的深入，宽度和高度都会缩小。通道的数量由传递给二维
#卷积层的第一个参数控制(例如32或64).
3.将我们的最后一个输出张量(形状(3,3,64))输入到一个密集连接的分类网络中:
这些分类器处理向量是一维的，而我们现在的输出是一个三维张量。因此，首先我们要把三维输出压平到一维，然后在顶端增加一些密集层：
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 3, 3, 64) 36928
_________________________________________________________________
flatten_1 (Flatten) (None, 576) 0
_________________________________________________________________
dense_1 (Dense) (None, 64) 36928
_________________________________________________________________
dense_2 (Dense) (None, 10) 650
=================================================================
Total params: 93,322
Trainable params: 93,322
Non-trainable params: 0
_________________________________________________________________
'''
4.训练:
from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
'''
Epoch 1/5
60000/60000 [==============================] - 160s 3ms/step - loss: 0.1797 - acc: 0.9438
Epoch 2/5
60000/60000 [==============================] - 163s 3ms/step - loss: 0.0474 - acc: 0.9857
Epoch 3/5
60000/60000 [==============================] - 8673s 145ms/step - loss: 0.0327 - acc: 0.9896
Epoch 4/5
60000/60000 [==============================] - 39s 647us/step - loss: 0.0248 - acc: 0.9924
Epoch 5/5
60000/60000 [==============================] - 37s 615us/step - loss: 0.0196 - acc: 0.9942
<keras.callbacks.History at 0x1fac5e8cf60>
'''
5.评估:
test_loss, test_acc = model.evaluate(test_images, test_labels)
'''
10000/10000 [==============================] - 3s 255us/step
'''
test_acc
'''
0.99019999999999997
'''
#测试精度达到了惊人的99.1%。
代码整合
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.summary()
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
from keras.datasets import mnist
from keras.utils import to_categorical
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
train_images = train_images.reshape((60000, 28, 28, 1))
train_images = train_images.astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1))
test_images = test_images.astype('float32') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=5, batch_size=64)
test_loss, test_acc = model.evaluate(test_images, test_labels)
test_acc
实验二：使用小数据集的卷积神经网络
实验目的
在包含4000张猫和狗图片（2000只猫，2000只狗）的数据集中，将图像分类为“狗”或“猫”。
数据集
猫与狗数据集：
这个原始数据集包含25,000个狗和猫的图像（每个课程12,500个），并且大小为543MB（压缩）。 下载并解压后，我们将创建一个新的数据集，其中包含三个子集：每个类1000个样本的训练集，每个类500个样本的验证集，以及每个类500个样本的测试集。
实验过程
1.下载数据集：
创建一个新的数据集，其中包含三个子集：每个类1000个样本的训练集，每个类500个样本的验证集，以及每个类500个样本的测试集。
import os, shutil
original_dataset_dir = 'kaggle_original_data/train'
# The directory where we will
# store our smaller dataset
base_dir = 'cats_and_dogs_small'
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
train_cats_dir = os.path.join(train_dir, 'cats')
os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir, 'dogs')
os.mkdir(train_dogs_dir)
validation_cats_dir = os.path.join(validation_dir, 'cats')
os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
os.mkdir(validation_dogs_dir)
test_cats_dir = os.path.join(test_dir, 'cats')
os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir, 'dogs')
os.mkdir(test_dogs_dir)
#
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
# Copy next 500 cat images to validation_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
# Copy next 500 cat images to test_cats_dir
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
# Copy first 1000 dog images to train_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_dogs_dir, fname)
shutil.copyfile(src, dst)
# Copy next 500 dog images to validation_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_dogs_dir, fname)
shutil.copyfile(src, dst)
# Copy next 500 dog images to test_dogs_dir
fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_dogs_dir, fname)
shutil.copyfile(src, dst)
print('total training cat images:', len(os.listdir(train_cats_dir)))
'''
total training cat images: 1000
'''
print('total training dog images:', len(os.listdir(train_dogs_dir)))
'''
total training dog images: 1000
'''
print('total validation cat images:', len(os.listdir(validation_cats_dir)))
'''
total validation cat images: 500
'''
print('total validation dog images:', len(os.listdir(validation_dogs_dir)))
'''
total validation dog images: 500
'''
print('total test cat images:', len(os.listdir(test_cats_dir)))
'''
total test cat images: 500
'''
print('total test dog images:', len(os.listdir(test_dogs_dir)))
'''
total test dog images: 500
'''
２.搭建网络：
我们的卷积网络是一堆交替Conv2D（带有Relu激活）和最大池化2D图层，为处理更大的图像和更复杂的问题，其将有一个Conv2D + MaxPooling2D阶段，最后用单个单元（大小为1的全连接层）和S形激活作为网络输出层。
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 148, 148, 32) 896
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 74, 74, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 72, 72, 64) 18496
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 36, 36, 64) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 34, 34, 128) 73856
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 17, 17, 128) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 15, 15, 128) 147584
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 6272) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 3211776
_________________________________________________________________
dense_2 (Dense) (None, 1) 513
=================================================================
Total params: 3,453,121
Trainable params: 3,453,121
Non-trainable params: 0
_________________________________________________________________
'''
采用 RMSprop优化器:
因为网络的输出层是一个单simoid单元，所以采用的是二元交叉熵作为我们的损失函数。
from keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
4.数据预处理：
在输入我们的网络之前应该采用预处理浮点张量来对数据格式化。
将其导入我们网络的步骤大致如下：
１.读取图片文件
２.将JPEG内容解码为像素的RBG网格。
３.将这些转换为浮点张量。
４.将像素值（0到255之间）重新缩放到[0，1]间隔（如您所知，神经网络倾向于处理小的输入值）。
from keras.preprocessing.image import ImageDataGenerator
# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=20,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
'''
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
'''
#查看其中一个发生器的输出：
#它会产生150x150个RGB图像（形状（20,150,150,3））和二进制标签（形状（20，））的批量。 20是每个批次中的样品数量（批量大小）。
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
'''
data batch shape: (20, 150, 150, 3)
labels batch shape: (20,)
'''
5.拟合:
使用拟合生成器方法来完成。
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
'''
Epoch 1/30
100/100 [==============================] - 1269s 13s/step - loss: 0.6859 - acc: 0.5475 - val_loss: 0.6616 - val_acc: 0.6420
Epoch 2/30
100/100 [==============================] - 27s 269ms/step - loss: 0.6353 - acc: 0.6390 - val_loss: 0.6266 - val_acc: 0.6590
Epoch 3/30
100/100 [==============================] - 24s 237ms/step - loss: 0.5914 - acc: 0.6910 - val_loss: 0.6410 - val_acc: 0.6390
Epoch 4/30
100/100 [==============================] - 24s 240ms/step - loss: 0.5601 - acc: 0.7165 - val_loss: 0.6574 - val_acc: 0.6340
Epoch 5/30
100/100 [==============================] - 25s 248ms/step - loss: 0.5373 - acc: 0.7265 - val_loss: 0.6056 - val_acc: 0.6720
Epoch 6/30
100/100 [==============================] - 20s 195ms/step - loss: 0.5116 - acc: 0.7505 - val_loss: 0.5675 - val_acc: 0.6990
Epoch 7/30
100/100 [==============================] - 20s 196ms/step - loss: 0.4860 - acc: 0.7560 - val_loss: 0.5564 - val_acc: 0.6980
Epoch 8/30
100/100 [==============================] - 20s 202ms/step - loss: 0.4555 - acc: 0.7840 - val_loss: 0.5694 - val_acc: 0.7020
Epoch 9/30
100/100 [==============================] - 23s 233ms/step - loss: 0.4339 - acc: 0.7990 - val_loss: 0.5547 - val_acc: 0.7100
Epoch 10/30
100/100 [==============================] - 21s 208ms/step - loss: 0.4034 - acc: 0.8185 - val_loss: 0.5484 - val_acc: 0.7280
Epoch 11/30
100/100 [==============================] - 22s 217ms/step - loss: 0.3810 - acc: 0.8295 - val_loss: 0.5737 - val_acc: 0.7220
Epoch 12/30
100/100 [==============================] - 22s 218ms/step - loss: 0.3609 - acc: 0.8455 - val_loss: 0.6161 - val_acc: 0.7010
Epoch 13/30
100/100 [==============================] - 21s 208ms/step - loss: 0.3415 - acc: 0.8470 - val_loss: 0.5498 - val_acc: 0.7260
Epoch 14/30
100/100 [==============================] - 20s 205ms/step - loss: 0.3190 - acc: 0.8640 - val_loss: 0.5676 - val_acc: 0.7250
Epoch 15/30
100/100 [==============================] - 20s 204ms/step - loss: 0.2891 - acc: 0.8865 - val_loss: 0.5854 - val_acc: 0.7420
Epoch 16/30
100/100 [==============================] - 21s 205ms/step - loss: 0.2694 - acc: 0.8890 - val_loss: 0.5864 - val_acc: 0.7290
Epoch 17/30
100/100 [==============================] - 20s 203ms/step - loss: 0.2471 - acc: 0.9015 - val_loss: 0.6189 - val_acc: 0.7210
Epoch 18/30
100/100 [==============================] - 25s 248ms/step - loss: 0.2318 - acc: 0.9105 - val_loss: 0.6030 - val_acc: 0.7300
Epoch 19/30
100/100 [==============================] - 23s 227ms/step - loss: 0.2075 - acc: 0.9220 - val_loss: 0.8091 - val_acc: 0.6860
Epoch 20/30
100/100 [==============================] - 24s 236ms/step - loss: 0.1872 - acc: 0.9350 - val_loss: 0.6385 - val_acc: 0.7480
Epoch 21/30
100/100 [==============================] - 22s 220ms/step - loss: 0.1730 - acc: 0.9435 - val_loss: 0.6707 - val_acc: 0.7330
Epoch 22/30
100/100 [==============================] - 27s 267ms/step - loss: 0.1569 - acc: 0.9465 - val_loss: 0.6582 - val_acc: 0.7440
Epoch 23/30
100/100 [==============================] - 25s 246ms/step - loss: 0.1438 - acc: 0.9485 - val_loss: 0.6917 - val_acc: 0.7460
Epoch 24/30
100/100 [==============================] - 28s 284ms/step - loss: 0.1239 - acc: 0.9585 - val_loss: 0.7309 - val_acc: 0.7400
Epoch 25/30
100/100 [==============================] - 24s 245ms/step - loss: 0.1099 - acc: 0.9625 - val_loss: 0.7476 - val_acc: 0.7290
Epoch 26/30
100/100 [==============================] - 25s 248ms/step - loss: 0.0936 - acc: 0.9700 - val_loss: 0.7722 - val_acc: 0.7390
Epoch 27/30
100/100 [==============================] - 21s 210ms/step - loss: 0.0811 - acc: 0.9740 - val_loss: 1.1478 - val_acc: 0.6940
Epoch 28/30
100/100 [==============================] - 21s 209ms/step - loss: 0.0717 - acc: 0.9780 - val_loss: 0.9114 - val_acc: 0.7130
Epoch 29/30
100/100 [==============================] - 21s 214ms/step - loss: 0.0634 - acc: 0.9800 - val_loss: 0.8197 - val_acc: 0.7400
Epoch 30/30
100/100 [==============================] - 22s 218ms/step - loss: 0.0561 - acc: 0.9840 - val_loss: 0.8574 - val_acc: 0.7360
'''
#保存模型
model.save('cats_and_dogs_small_1.h5')
6.在训练集和验证集上画出训练过程中的损失函数和准确度：
发生过度拟合。
7.数据增强：
过拟合是由于没有太多的样本可供学习而导致的，这使我们无法训练能够推广到新数据的模型。 给定无限的数据，我们的模型将涉及手头数据分布的每个可能方面：我们永远不会过度使用。 数据增强采用从现有训练样本中生成更多训练数据的方法，通过一些随机变换来“增强”样本，从而产生可信的图像。 目标是在训练时，我们的模型永远不会重复看到完全相同的图片。 这有助于模型涉及数据的更多方面并更好地推广。
#配置大量的随机转换
datagen = ImageDataGenerator(
rotation_range=40, #随机旋转图片的范围
width_shift_range=0.2, #在其中垂直或水平随机变换图片的范围
height_shift_range=0.2,
shear_range=0.2, #随机应用剪切转换
zoom_range=0.2, #随机缩放内部照片
horizontal_flip=True, #水平随机翻转一半图像
fill_mode='nearest') #用于填充新创建像素
#查看增强图像
from keras.preprocessing import image
fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]
img_path = fnames[3]
img = image.load_img(img_path, target_size=(150, 150))
x = image.img_to_array(img)
x = x.reshape((1,) + x.shape)
i = 0
for batch in datagen.flow(x, batch_size=1):
plt.figure(i)
imgplot = plt.imshow(image.array_to_img(batch[0]))
i += 1
if i % 4 == 0:
break
plt.show()
投入依然严重相互关联，因此，这可能不足以完全摆脱过拟合。 为了进一步解决过拟合问题，我们还会在密集连接的分类器之前为模型添加一个Dropout层：
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
８.训练网络：
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,)
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=32,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary')
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=10,
validation_data=validation_generator,
validation_steps=50)
'''
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/10
100/100 [==============================] - 65s 646ms/step - loss: 0.6938 - acc: 0.5212 - val_loss: 0.6851 - val_acc: 0.4956
Epoch 2/10
100/100 [==============================] - 57s 574ms/step - loss: 0.6838 - acc: 0.5513 - val_loss: 0.6942 - val_acc: 0.5089
Epoch 3/10
100/100 [==============================] - 57s 573ms/step - loss: 0.6700 - acc: 0.5828 - val_loss: 0.6439 - val_acc: 0.6136
Epoch 4/10
100/100 [==============================] - 57s 571ms/step - loss: 0.6478 - acc: 0.6275 - val_loss: 0.6255 - val_acc: 0.6225
Epoch 5/10
100/100 [==============================] - 59s 586ms/step - loss: 0.6324 - acc: 0.6428 - val_loss: 0.5974 - val_acc: 0.6834
Epoch 6/10
100/100 [==============================] - 60s 604ms/step - loss: 0.6126 - acc: 0.6678 - val_loss: 0.5896 - val_acc: 0.6593
Epoch 7/10
100/100 [==============================] - 66s 662ms/step - loss: 0.6132 - acc: 0.6597 - val_loss: 0.5620 - val_acc: 0.7164
Epoch 8/10
100/100 [==============================] - 57s 572ms/step - loss: 0.6044 - acc: 0.6672 - val_loss: 0.5758 - val_acc: 0.6872
Epoch 9/10
100/100 [==============================] - 76s 764ms/step - loss: 0.5782 - acc: 0.7094 - val_loss: 0.7336 - val_acc: 0.6148
Epoch 10/10
100/100 [==============================] - 87s 866ms/step - loss: 0.5931 - acc: 0.6788 - val_loss: 0.5397 - val_acc: 0.7253
'''
model.save('cats_and_dogs_small_2-10.h5')
9.绘制结果：
由于数据增强和丢弃策略，我们不再过度拟合
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
代码整合
import keras
keras.__version__
import os, shutil
original_dataset_dir = 'kaggle_original_data/train'
base_dir = 'cats_and_dogs_small'
os.mkdir(base_dir)
train_dir = os.path.join(base_dir, 'train')
os.mkdir(train_dir)
validation_dir = os.path.join(base_dir, 'validation')
os.mkdir(validation_dir)
test_dir = os.path.join(base_dir, 'test')
os.mkdir(test_dir)
train_cats_dir = os.path.join(train_dir, 'cats')
os.mkdir(train_cats_dir)
train_dogs_dir = os.path.join(train_dir, 'dogs')
os.mkdir(train_dogs_dir)
validation_cats_dir = os.path.join(validation_dir, 'cats')
os.mkdir(validation_cats_dir)
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
os.mkdir(validation_dogs_dir)
test_cats_dir = os.path.join(test_dir, 'cats')
os.mkdir(test_cats_dir)
test_dogs_dir = os.path.join(test_dir, 'dogs')
os.mkdir(test_dogs_dir)
fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_cats_dir, fname)
shutil.copyfile(src, dst)
fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_cats_dir, fname)
shutil.copyfile(src, dst)
fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_cats_dir, fname)
shutil.copyfile(src, dst)
fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(train_dogs_dir, fname)
shutil.copyfile(src, dst)
fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(validation_dogs_dir, fname)
shutil.copyfile(src, dst)
fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]
for fname in fnames:
src = os.path.join(original_dataset_dir, fname)
dst = os.path.join(test_dogs_dir, fname)
shutil.copyfile(src, dst)
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
from keras import optimizers
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1./255)
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
for data_batch, labels_batch in train_generator:
print('data batch shape:', data_batch.shape)
print('labels batch shape:', labels_batch.shape)
break
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50)
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
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_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=32,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=32,
class_mode='binary')
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=10,
validation_data=validation_generator,
validation_steps=50)
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
实验三：使用预训练的convnet
实验目的
使用VGG16架构将ImageNet数据集分类。
数据集
ImageNet数据集：
ImageNet有140万标记图像和1000个不同类别，包含许多动物类别，包括不同种类的猫和狗，因此可以期望在我们的猫与狗分类问题上表现很好。
实验过程
两种方式可以有效利用预训练网络：特征提取和微调。
特征提取
使用先前训练的网络从新样本中所提取的感兴趣的特征。然后通过一个新的分类器从头开始训练。
1.用于图像分类的convnets包括两部分：它们从一系列池化层和卷积层开始，最后以密集连接的分类器结束。第一部分被称为模型的“卷积base”。对于小网络来说，“特征提取”将简单地包含预训练网络的卷积base，通过它运行新的数据，并在输出之上训练一个新的分类器。
1.实例化VGG16模型：
from keras.applications import VGG16
conv_base = VGG16(weights='imagenet', #权值初始值
include_top=False, #在网络顶部是否包含密集连接的分类器
input_shape=(150, 150, 3)) #输入网络的图像张量的形状
#VGG16卷积base架构的详细信息
conv_base.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 150, 150, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 150, 150, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 150, 150, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 75, 75, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 75, 75, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 75, 75, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 37, 37, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 37, 37, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 18, 18, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 9, 9, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
'''
2.第一种方法：
在我们的数据集上运行卷积base，将其输出以Numpy数组的格式保存到磁盘上，然后将这些数据用作独立的密集连接分类器的输入。
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
base_dir = '/Users/Administrator/Desktop/deeplearning/02.卷积神经网络/cats_and_dogs_small'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
test_dir = os.path.join(base_dir, 'test')
datagen = ImageDataGenerator(rescale=1./255)
batch_size = 20
def extract_features(directory, sample_count):
features = np.zeros(shape=(sample_count, 4, 4, 512))
labels = np.zeros(shape=(sample_count))
generator = datagen.flow_from_directory(
directory,
target_size=(150, 150),
batch_size=batch_size,
class_mode='binary')
i = 0
for inputs_batch, labels_batch in generator:
features_batch = conv_base.predict(inputs_batch)
features[i * batch_size : (i + 1) * batch_size] = features_batch
labels[i * batch_size : (i + 1) * batch_size] = labels_batch
i += 1
if i * batch_size >= sample_count:
# Note that since generators yield data indefinitely in a loop,
# we must `break` after every image has been seen once.
break
return features, labels
train_features, train_labels = extract_features(train_dir, 2000)
validation_features, validation_labels = extract_features(validation_dir, 1000)
test_features, test_labels = extract_features(test_dir, 1000)
'''
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
'''
#扁平化
train_features = np.reshape(train_features, (2000, 4 * 4 * 512))
validation_features = np.reshape(validation_features, (1000, 4 * 4 * 512))
test_features = np.reshape(test_features, (1000, 4 * 4 * 512))
训练：
使用dropout正则化技术，定义密集连接的分类器，在刚才记录的数据和标签上进行训练。
from keras import models
from keras import layers
from keras import optimizers
model = models.Sequential()
model.add(layers.Dense(256, activation='relu', input_dim=4 * 4 * 512))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer=optimizers.RMSprop(lr=2e-5),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(train_features, train_labels,
epochs=30,
batch_size=20,
validation_data=(validation_features, validation_labels))
'''
Train on 2000 samples, validate on 1000 samples
Epoch 1/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.6134 - acc: 0.6590 - val_loss: 0.4378 - val_acc: 0.8340
Epoch 2/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.4305 - acc: 0.7965 - val_loss: 0.3568 - val_acc: 0.8680
Epoch 3/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.3378 - acc: 0.8660 - val_loss: 0.3159 - val_acc: 0.8790
Epoch 4/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.3136 - acc: 0.8705 - val_loss: 0.2912 - val_acc: 0.8840
Epoch 5/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.2815 - acc: 0.8845 - val_loss: 0.2826 - val_acc: 0.8870
Epoch 6/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.2578 - acc: 0.8975 - val_loss: 0.2670 - val_acc: 0.8970
Epoch 7/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.2358 - acc: 0.9075 - val_loss: 0.2592 - val_acc: 0.8980
Epoch 8/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.2298 - acc: 0.9045 - val_loss: 0.2553 - val_acc: 0.8980
Epoch 9/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.2144 - acc: 0.9130 - val_loss: 0.2630 - val_acc: 0.8900
Epoch 10/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1975 - acc: 0.9255 - val_loss: 0.2461 - val_acc: 0.8980
Epoch 11/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1932 - acc: 0.9305 - val_loss: 0.2439 - val_acc: 0.8960
Epoch 12/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1832 - acc: 0.9325 - val_loss: 0.2504 - val_acc: 0.8970
Epoch 13/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1775 - acc: 0.9370 - val_loss: 0.2398 - val_acc: 0.9010
Epoch 14/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1709 - acc: 0.9355 - val_loss: 0.2380 - val_acc: 0.8990
Epoch 15/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1644 - acc: 0.9455 - val_loss: 0.2416 - val_acc: 0.9010
Epoch 16/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1526 - acc: 0.9485 - val_loss: 0.2359 - val_acc: 0.9020
Epoch 17/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1442 - acc: 0.9515 - val_loss: 0.2360 - val_acc: 0.9010
Epoch 18/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1320 - acc: 0.9535 - val_loss: 0.2465 - val_acc: 0.8970
Epoch 19/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1327 - acc: 0.9555 - val_loss: 0.2363 - val_acc: 0.8990
Epoch 20/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1263 - acc: 0.9535 - val_loss: 0.2484 - val_acc: 0.9000
Epoch 21/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1210 - acc: 0.9570 - val_loss: 0.2352 - val_acc: 0.8940
Epoch 22/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1147 - acc: 0.9620 - val_loss: 0.2351 - val_acc: 0.9020
Epoch 23/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1108 - acc: 0.9630 - val_loss: 0.2416 - val_acc: 0.8990
Epoch 24/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1070 - acc: 0.9680 - val_loss: 0.2411 - val_acc: 0.8980
Epoch 25/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1045 - acc: 0.9695 - val_loss: 0.2379 - val_acc: 0.9000
Epoch 26/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.1015 - acc: 0.9650 - val_loss: 0.2394 - val_acc: 0.8970
Epoch 27/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.0937 - acc: 0.9700 - val_loss: 0.2402 - val_acc: 0.8970
Epoch 28/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.0902 - acc: 0.9740 - val_loss: 0.2597 - val_acc: 0.8970
Epoch 29/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.0867 - acc: 0.9750 - val_loss: 0.2445 - val_acc: 0.8950
Epoch 30/30
2000/2000 [==============================] - 4s 2ms/step - loss: 0.0860 - acc: 0.9705 - val_loss: 0.2404 - val_acc: 0.8980
'''
查看训练期间的损失和准确率曲线:
由于该技术不适合数据扩增的情况，模型几乎从一开始就过拟合了。
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
3.第二种方法：
通过在网络顶部增加全连接层来扩展我们拥有的conv_base模型，并在输入数据上端到端地进行运行。
from keras import models
from keras import layers
model = models.Sequential()
model.add(conv_base)
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
vgg16 (Model) (None, 4, 4, 512) 14714688
_________________________________________________________________
flatten_2 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_5 (Dense) (None, 256) 2097408
_________________________________________________________________
dense_6 (Dense) (None, 1) 257
=================================================================
Total params: 16,812,353
Trainable params: 16,812,353
Non-trainable params: 0
_________________________________________________________________
'''
冻结卷积base:
设置它的trainable属性。
print('This is the number of trainable weights '
'before freezing the conv base:', len(model.trainable_weights))
'''
This is the number of trainable weights before freezing the conv base: 30
'''
conv_base.trainable = False
print('This is the number of trainable weights '
'after freezing the conv base:', len(model.trainable_weights))
'''
This is the number of trainable weights after freezing the conv base: 4
'''
训练:
from keras.preprocessing.image import ImageDataGenerator
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,
fill_mode='nearest')
# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
# This is the target directory
train_dir,
# All images will be resized to 150x150
target_size=(150, 150),
batch_size=20,
# Since we use binary_crossentropy loss, we need binary labels
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=2e-5),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50,
verbose=2)
'''
Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
Epoch 1/30
- 526s - loss: 0.5844 - acc: 0.6955 - val_loss: 0.4446 - val_acc: 0.8160
Epoch 2/30
- 520s - loss: 0.4693 - acc: 0.7885 - val_loss: 0.3661 - val_acc: 0.8700
Epoch 3/30
- 521s - loss: 0.4280 - acc: 0.8055 - val_loss: 0.3229 - val_acc: 0.8710
Epoch 4/30
- 521s - loss: 0.4052 - acc: 0.8200 - val_loss: 0.2976 - val_acc: 0.8860
Epoch 5/30
- 520s - loss: 0.3775 - acc: 0.8380 - val_loss: 0.2880 - val_acc: 0.8850
Epoch 6/30
- 521s - loss: 0.3605 - acc: 0.8430 - val_loss: 0.2758 - val_acc: 0.8930
Epoch 7/30
- 521s - loss: 0.3538 - acc: 0.8440 - val_loss: 0.2689 - val_acc: 0.8970
Epoch 8/30
- 521s - loss: 0.3415 - acc: 0.8540 - val_loss: 0.2656 - val_acc: 0.8970
Epoch 9/30
- 521s - loss: 0.3339 - acc: 0.8575 - val_loss: 0.2678 - val_acc: 0.8980
Epoch 10/30
- 521s - loss: 0.3229 - acc: 0.8595 - val_loss: 0.2750 - val_acc: 0.8810
Epoch 11/30
- 521s - loss: 0.3221 - acc: 0.8560 - val_loss: 0.2536 - val_acc: 0.8920
Epoch 12/30
- 521s - loss: 0.3250 - acc: 0.8600 - val_loss: 0.2635 - val_acc: 0.8870
Epoch 13/30
- 521s - loss: 0.3263 - acc: 0.8570 - val_loss: 0.2522 - val_acc: 0.8930
Epoch 14/30
- 521s - loss: 0.3053 - acc: 0.8685 - val_loss: 0.2505 - val_acc: 0.8890
Epoch 15/30
- 521s - loss: 0.3203 - acc: 0.8665 - val_loss: 0.2459 - val_acc: 0.9020
Epoch 16/30
- 521s - loss: 0.3107 - acc: 0.8635 - val_loss: 0.2601 - val_acc: 0.8890
Epoch 17/30
- 521s - loss: 0.2983 - acc: 0.8760 - val_loss: 0.2534 - val_acc: 0.8920
Epoch 18/30
- 520s - loss: 0.2995 - acc: 0.8780 - val_loss: 0.2457 - val_acc: 0.8970
Epoch 19/30
- 520s - loss: 0.2951 - acc: 0.8760 - val_loss: 0.2445 - val_acc: 0.8990
Epoch 20/30
- 521s - loss: 0.2912 - acc: 0.8715 - val_loss: 0.2602 - val_acc: 0.8920
Epoch 21/30
- 522s - loss: 0.2915 - acc: 0.8760 - val_loss: 0.2375 - val_acc: 0.9070
Epoch 22/30
- 521s - loss: 0.3073 - acc: 0.8610 - val_loss: 0.2387 - val_acc: 0.9060
Epoch 23/30
- 521s - loss: 0.2885 - acc: 0.8805 - val_loss: 0.2384 - val_acc: 0.9030
Epoch 24/30
- 522s - loss: 0.2868 - acc: 0.8770 - val_loss: 0.2403 - val_acc: 0.9030
Epoch 25/30
- 522s - loss: 0.2883 - acc: 0.8705 - val_loss: 0.2397 - val_acc: 0.9000
Epoch 26/30
- 521s - loss: 0.2985 - acc: 0.8700 - val_loss: 0.2415 - val_acc: 0.8980
Epoch 27/30
- 521s - loss: 0.2778 - acc: 0.8775 - val_loss: 0.2552 - val_acc: 0.8960
Epoch 28/30
- 521s - loss: 0.2842 - acc: 0.8775 - val_loss: 0.2361 - val_acc: 0.9010
Epoch 29/30
- 522s - loss: 0.2737 - acc: 0.8865 - val_loss: 0.2385 - val_acc: 0.8990
Epoch 30/30
- 522s - loss: 0.2767 - acc: 0.8745 - val_loss: 0.2423 - val_acc: 0.9000
'''
model.save('cats_and_dogs_small_3.h5')
绘制结果:
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
微调
解冻基础网络中的一些层：
进行特征提取时完成了将所自定义网络添加到已训练过的基础网络之上，冻结基础网络，训练添加的部分。
解冻conv_base，然后冻结其中的个别层。
conv_base.summary()
'''
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 150, 150, 3) 0
_________________________________________________________________
block1_conv1 (Conv2D) (None, 150, 150, 64) 1792
_________________________________________________________________
block1_conv2 (Conv2D) (None, 150, 150, 64) 36928
_________________________________________________________________
block1_pool (MaxPooling2D) (None, 75, 75, 64) 0
_________________________________________________________________
block2_conv1 (Conv2D) (None, 75, 75, 128) 73856
_________________________________________________________________
block2_conv2 (Conv2D) (None, 75, 75, 128) 147584
_________________________________________________________________
block2_pool (MaxPooling2D) (None, 37, 37, 128) 0
_________________________________________________________________
block3_conv1 (Conv2D) (None, 37, 37, 256) 295168
_________________________________________________________________
block3_conv2 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_conv3 (Conv2D) (None, 37, 37, 256) 590080
_________________________________________________________________
block3_pool (MaxPooling2D) (None, 18, 18, 256) 0
_________________________________________________________________
block4_conv1 (Conv2D) (None, 18, 18, 512) 1180160
_________________________________________________________________
block4_conv2 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_conv3 (Conv2D) (None, 18, 18, 512) 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) (None, 9, 9, 512) 0
_________________________________________________________________
block5_conv1 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv2 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_conv3 (Conv2D) (None, 9, 9, 512) 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) (None, 4, 4, 512) 0
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________
'''
从第一层到block4_pool层冻结，而层block5_conv1，block5_conv2并block5_conv3接受训练的更新。
conv_base.trainable = True
set_trainable = False
for layer in conv_base.layers:
if layer.name == 'block5_conv1':
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
'''
<keras.engine.input_layer.InputLayer object at 0x0000018C750AFA20>
<keras.layers.convolutional.Conv2D object at 0x0000018C750E40F0>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FE9CAC8>
<keras.layers.pooling.MaxPooling2D object at 0x0000018C7FF07FD0>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FEDB5F8>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FF350F0>
<keras.layers.pooling.MaxPooling2D object at 0x0000018C7FF5E470>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FF4C2E8>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FF86E80>
<keras.layers.convolutional.Conv2D object at 0x0000018C7FFB1438>
<keras.layers.pooling.MaxPooling2D object at 0x0000018C7FFC8A90>
<keras.layers.convolutional.Conv2D object at 0x0000018C06008630>
<keras.layers.convolutional.Conv2D object at 0x0000018C060219B0>
<keras.layers.convolutional.Conv2D object at 0x0000018C060490B8>
<keras.layers.pooling.MaxPooling2D object at 0x0000018C0605EC50>
<keras.layers.convolutional.Conv2D object at 0x0000018C060763C8>
<keras.layers.convolutional.Conv2D object at 0x0000018C0609F048>
<keras.layers.convolutional.Conv2D object at 0x0000018C060C5BE0>
<keras.layers.pooling.MaxPooling2D object at 0x0000018C060F2278>
'''
开始微调网络:
为制对我们正在微调的3层网络特征输出的更新幅度，使用学习速率很低的RMSprop优化函数。
from keras.models import load_model
model = load_model('cats_and_dogs_small_3.h5')
from keras.preprocessing.image import ImageDataGenerator
model.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-5),
metrics=['acc'])
history = model.fit_generator(
train_generator,
steps_per_epoch=100,
epochs=100,
validation_data=validation_generator,
validation_steps=50)
'''
Epoch 1/100
100/100 [==============================] - 71s 708ms/step - loss: 0.2964 - acc: 0.8740 - val_loss: 0.2679 - val_acc: 0.8940
Epoch 2/100
100/100 [==============================] - 69s 695ms/step - loss: 0.2588 - acc: 0.8950 - val_loss: 0.2117 - val_acc: 0.9130
Epoch 3/100
100/100 [==============================] - 70s 696ms/step - loss: 0.2290 - acc: 0.9045 - val_loss: 0.2136 - val_acc: 0.9130
Epoch 4/100
100/100 [==============================] - 69s 690ms/step - loss: 0.2251 - acc: 0.9090 - val_loss: 0.2173 - val_acc: 0.9130
Epoch 5/100
100/100 [==============================] - 69s 689ms/step - loss: 0.2254 - acc: 0.9075 - val_loss: 0.2012 - val_acc: 0.9180
Epoch 6/100
100/100 [==============================] - 69s 694ms/step - loss: 0.1914 - acc: 0.9230 - val_loss: 0.1999 - val_acc: 0.9270
Epoch 7/100
100/100 [==============================] - 69s 694ms/step - loss: 0.1788 - acc: 0.9320 - val_loss: 0.2214 - val_acc: 0.9180
Epoch 8/100
100/100 [==============================] - 69s 689ms/step - loss: 0.1718 - acc: 0.9275 - val_loss: 0.2047 - val_acc: 0.9210
Epoch 9/100
100/100 [==============================] - 69s 688ms/step - loss: 0.1522 - acc: 0.9340 - val_loss: 0.2339 - val_acc: 0.9120
Epoch 10/100
100/100 [==============================] - 69s 692ms/step - loss: 0.1413 - acc: 0.9440 - val_loss: 0.1962 - val_acc: 0.9260
Epoch 11/100
100/100 [==============================] - 69s 688ms/step - loss: 0.1683 - acc: 0.9330 - val_loss: 0.2370 - val_acc: 0.9080
Epoch 12/100
100/100 [==============================] - 69s 694ms/step - loss: 0.1428 - acc: 0.9415 - val_loss: 0.1867 - val_acc: 0.9290
Epoch 13/100
100/100 [==============================] - 69s 687ms/step - loss: 0.1289 - acc: 0.9540 - val_loss: 0.1870 - val_acc: 0.9310
Epoch 14/100
100/100 [==============================] - 69s 689ms/step - loss: 0.1386 - acc: 0.9440 - val_loss: 0.1763 - val_acc: 0.9290
Epoch 15/100
100/100 [==============================] - 69s 695ms/step - loss: 0.1281 - acc: 0.9475 - val_loss: 0.1794 - val_acc: 0.9300
Epoch 16/100
100/100 [==============================] - 69s 694ms/step - loss: 0.1126 - acc: 0.9545 - val_loss: 0.1843 - val_acc: 0.9340
Epoch 17/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0951 - acc: 0.9595 - val_loss: 0.1679 - val_acc: 0.9310
Epoch 18/100
100/100 [==============================] - 69s 688ms/step - loss: 0.1109 - acc: 0.9565 - val_loss: 0.1779 - val_acc: 0.9340
Epoch 19/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0944 - acc: 0.9670 - val_loss: 0.1766 - val_acc: 0.9320
Epoch 20/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0942 - acc: 0.9670 - val_loss: 0.2022 - val_acc: 0.9250
Epoch 21/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0921 - acc: 0.9635 - val_loss: 0.2021 - val_acc: 0.9260
Epoch 22/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0825 - acc: 0.9670 - val_loss: 0.1876 - val_acc: 0.9320
Epoch 23/100
100/100 [==============================] - 69s 689ms/step - loss: 0.1010 - acc: 0.9630 - val_loss: 0.1837 - val_acc: 0.9360
Epoch 24/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0847 - acc: 0.9670 - val_loss: 0.1988 - val_acc: 0.9350
Epoch 25/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0731 - acc: 0.9705 - val_loss: 0.2018 - val_acc: 0.9350
Epoch 26/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0827 - acc: 0.9695 - val_loss: 0.1748 - val_acc: 0.9400
Epoch 27/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0722 - acc: 0.9755 - val_loss: 0.2203 - val_acc: 0.9330
Epoch 28/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0680 - acc: 0.9735 - val_loss: 0.2063 - val_acc: 0.9280
Epoch 29/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0654 - acc: 0.9710 - val_loss: 0.1923 - val_acc: 0.9360
Epoch 30/100
100/100 [==============================] - 69s 694ms/step - loss: 0.0733 - acc: 0.9710 - val_loss: 0.1965 - val_acc: 0.9320
Epoch 31/100
100/100 [==============================] - 69s 692ms/step - loss: 0.0708 - acc: 0.9760 - val_loss: 0.2125 - val_acc: 0.9290
Epoch 32/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0669 - acc: 0.9745 - val_loss: 0.2035 - val_acc: 0.9350
Epoch 33/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0679 - acc: 0.9735 - val_loss: 0.2199 - val_acc: 0.9270
Epoch 34/100
100/100 [==============================] - 69s 692ms/step - loss: 0.0628 - acc: 0.9785 - val_loss: 0.1928 - val_acc: 0.9310
Epoch 35/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0664 - acc: 0.9770 - val_loss: 0.2173 - val_acc: 0.9290
Epoch 36/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0515 - acc: 0.9820 - val_loss: 0.2261 - val_acc: 0.9340
Epoch 37/100
100/100 [==============================] - 69s 692ms/step - loss: 0.0555 - acc: 0.9785 - val_loss: 0.3316 - val_acc: 0.9160
Epoch 38/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0558 - acc: 0.9790 - val_loss: 0.2359 - val_acc: 0.9340
Epoch 39/100
100/100 [==============================] - 69s 692ms/step - loss: 0.0400 - acc: 0.9875 - val_loss: 0.2345 - val_acc: 0.9350
Epoch 40/100
100/100 [==============================] - 69s 695ms/step - loss: 0.0504 - acc: 0.9830 - val_loss: 0.2222 - val_acc: 0.9370
Epoch 41/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0437 - acc: 0.9835 - val_loss: 0.2325 - val_acc: 0.9390
Epoch 42/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0455 - acc: 0.9830 - val_loss: 0.2271 - val_acc: 0.9410
Epoch 43/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0557 - acc: 0.9805 - val_loss: 0.2746 - val_acc: 0.9300
Epoch 44/100
100/100 [==============================] - 69s 694ms/step - loss: 0.0450 - acc: 0.9840 - val_loss: 0.3066 - val_acc: 0.9230
Epoch 45/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0536 - acc: 0.9810 - val_loss: 0.2126 - val_acc: 0.9330
Epoch 46/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0477 - acc: 0.9810 - val_loss: 0.1954 - val_acc: 0.9400
Epoch 47/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0360 - acc: 0.9885 - val_loss: 0.2033 - val_acc: 0.9410
Epoch 48/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0405 - acc: 0.9820 - val_loss: 0.2188 - val_acc: 0.9330
Epoch 49/100
100/100 [==============================] - 68s 685ms/step - loss: 0.0318 - acc: 0.9880 - val_loss: 0.2992 - val_acc: 0.9240
Epoch 50/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0308 - acc: 0.9900 - val_loss: 0.2760 - val_acc: 0.9280
Epoch 51/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0429 - acc: 0.9855 - val_loss: 0.2365 - val_acc: 0.9360
Epoch 52/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0435 - acc: 0.9845 - val_loss: 0.2321 - val_acc: 0.9400
Epoch 53/100
100/100 [==============================] - 69s 686ms/step - loss: 0.0306 - acc: 0.9895 - val_loss: 0.2982 - val_acc: 0.9260
Epoch 54/100
100/100 [==============================] - 69s 686ms/step - loss: 0.0406 - acc: 0.9850 - val_loss: 0.3509 - val_acc: 0.9260
Epoch 55/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0374 - acc: 0.9890 - val_loss: 0.2440 - val_acc: 0.9350
Epoch 56/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0313 - acc: 0.9880 - val_loss: 0.2671 - val_acc: 0.9300
Epoch 57/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0264 - acc: 0.9865 - val_loss: 0.2277 - val_acc: 0.9340
Epoch 58/100
100/100 [==============================] - 69s 685ms/step - loss: 0.0286 - acc: 0.9890 - val_loss: 0.3027 - val_acc: 0.9310
Epoch 59/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0332 - acc: 0.9865 - val_loss: 0.2370 - val_acc: 0.9330
Epoch 60/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0251 - acc: 0.9900 - val_loss: 0.2270 - val_acc: 0.9400
Epoch 61/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0345 - acc: 0.9880 - val_loss: 0.3246 - val_acc: 0.9190
Epoch 62/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0360 - acc: 0.9905 - val_loss: 0.3061 - val_acc: 0.9290
Epoch 63/100
100/100 [==============================] - 69s 685ms/step - loss: 0.0405 - acc: 0.9855 - val_loss: 0.2458 - val_acc: 0.9390
Epoch 64/100
100/100 [==============================] - 69s 686ms/step - loss: 0.0241 - acc: 0.9905 - val_loss: 0.2371 - val_acc: 0.9350
Epoch 65/100
100/100 [==============================] - 68s 685ms/step - loss: 0.0223 - acc: 0.9920 - val_loss: 0.2663 - val_acc: 0.9420
Epoch 66/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0325 - acc: 0.9900 - val_loss: 0.2829 - val_acc: 0.9340
Epoch 67/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0319 - acc: 0.9855 - val_loss: 0.2462 - val_acc: 0.9410
Epoch 68/100
100/100 [==============================] - 68s 684ms/step - loss: 0.0314 - acc: 0.9900 - val_loss: 0.2452 - val_acc: 0.9400
Epoch 69/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0217 - acc: 0.9920 - val_loss: 0.2276 - val_acc: 0.9400
Epoch 70/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0316 - acc: 0.9895 - val_loss: 0.3000 - val_acc: 0.9320
Epoch 71/100
100/100 [==============================] - 69s 685ms/step - loss: 0.0235 - acc: 0.9900 - val_loss: 0.2419 - val_acc: 0.9380
Epoch 72/100
100/100 [==============================] - 68s 684ms/step - loss: 0.0254 - acc: 0.9890 - val_loss: 0.2744 - val_acc: 0.9270
Epoch 73/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0213 - acc: 0.9935 - val_loss: 0.2577 - val_acc: 0.9360
Epoch 74/100
100/100 [==============================] - 69s 687ms/step - loss: 0.0234 - acc: 0.9900 - val_loss: 0.5941 - val_acc: 0.8910
Epoch 75/100
100/100 [==============================] - 69s 686ms/step - loss: 0.0229 - acc: 0.9910 - val_loss: 0.2376 - val_acc: 0.9410
Epoch 76/100
100/100 [==============================] - 68s 684ms/step - loss: 0.0269 - acc: 0.9895 - val_loss: 0.2192 - val_acc: 0.9400
Epoch 77/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0306 - acc: 0.9895 - val_loss: 0.4235 - val_acc: 0.9180
Epoch 78/100
100/100 [==============================] - 69s 688ms/step - loss: 0.0297 - acc: 0.9870 - val_loss: 0.4058 - val_acc: 0.9150
Epoch 79/100
100/100 [==============================] - 68s 685ms/step - loss: 0.0185 - acc: 0.9920 - val_loss: 0.2406 - val_acc: 0.9350
Epoch 80/100
100/100 [==============================] - 68s 682ms/step - loss: 0.0231 - acc: 0.9925 - val_loss: 0.2911 - val_acc: 0.9350
Epoch 81/100
100/100 [==============================] - 68s 685ms/step - loss: 0.0210 - acc: 0.9925 - val_loss: 0.2490 - val_acc: 0.9390
Epoch 82/100
100/100 [==============================] - 68s 683ms/step - loss: 0.0285 - acc: 0.9900 - val_loss: 0.2651 - val_acc: 0.9400
Epoch 83/100
100/100 [==============================] - 69s 686ms/step - loss: 0.0238 - acc: 0.9915 - val_loss: 0.2630 - val_acc: 0.9420
Epoch 84/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0139 - acc: 0.9945 - val_loss: 0.4327 - val_acc: 0.9170
Epoch 85/100
100/100 [==============================] - 70s 696ms/step - loss: 0.0318 - acc: 0.9885 - val_loss: 0.3970 - val_acc: 0.9170
Epoch 86/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0212 - acc: 0.9920 - val_loss: 0.2888 - val_acc: 0.9360
Epoch 87/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0271 - acc: 0.9900 - val_loss: 0.3411 - val_acc: 0.9280
Epoch 88/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0168 - acc: 0.9955 - val_loss: 0.2189 - val_acc: 0.9480
Epoch 89/100
100/100 [==============================] - 70s 697ms/step - loss: 0.0233 - acc: 0.9920 - val_loss: 0.2459 - val_acc: 0.9410
Epoch 90/100
100/100 [==============================] - 69s 690ms/step - loss: 0.0224 - acc: 0.9915 - val_loss: 0.2352 - val_acc: 0.9400
Epoch 91/100
100/100 [==============================] - 70s 699ms/step - loss: 0.0189 - acc: 0.9925 - val_loss: 0.2632 - val_acc: 0.9380
Epoch 92/100
100/100 [==============================] - 69s 694ms/step - loss: 0.0163 - acc: 0.9940 - val_loss: 0.3046 - val_acc: 0.9360
Epoch 93/100
100/100 [==============================] - 69s 695ms/step - loss: 0.0099 - acc: 0.9980 - val_loss: 0.3264 - val_acc: 0.9350
Epoch 94/100
100/100 [==============================] - 69s 694ms/step - loss: 0.0174 - acc: 0.9950 - val_loss: 0.2712 - val_acc: 0.9370
Epoch 95/100
100/100 [==============================] - 69s 691ms/step - loss: 0.0132 - acc: 0.9965 - val_loss: 0.3202 - val_acc: 0.9330
Epoch 96/100
100/100 [==============================] - 69s 695ms/step - loss: 0.0126 - acc: 0.9945 - val_loss: 0.2944 - val_acc: 0.9330
Epoch 97/100
100/100 [==============================] - 70s 695ms/step - loss: 0.0236 - acc: 0.9940 - val_loss: 0.3391 - val_acc: 0.9340
Epoch 98/100
100/100 [==============================] - 69s 689ms/step - loss: 0.0241 - acc: 0.9910 - val_loss: 0.2671 - val_acc: 0.9360
Epoch 99/100
100/100 [==============================] - 69s 695ms/step - loss: 0.0231 - acc: 0.9915 - val_loss: 0.2538 - val_acc: 0.9400
Epoch 100/100
100/100 [==============================] - 69s 693ms/step - loss: 0.0219 - acc: 0.9935 - val_loss: 0.2560 - val_acc: 0.9370
'''
model.save('cats_and_dogs_small_4.h5')
绘制结果:
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(acc))
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
这些曲线看起来非常嘈杂，为了使它们更具可读性，我们可以通过计算指数移动平均值来替换每个损失和准确度来使它们平滑。
def smooth_curve(points, factor=0.8):
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
plt.plot(epochs,
smooth_curve(acc), 'bo', label='Smoothed training acc')
plt.plot(epochs,
smooth_curve(val_acc), 'b', label='Smoothed validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs,
smooth_curve(loss), 'bo', label='Smoothed training loss')
plt.plot(epochs,
smooth_curve(val_loss), 'b', label='Smoothed validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
评估模型：
test_generator = test_datagen.flow_from_directory(
test_dir,
target_size=(150, 150),
batch_size=20,
class_mode='binary')
test_loss, test_acc = model.evaluate_generator(test_generator, steps=50)
print('test acc:', test_acc)
'''
Found 1000 images belonging to 2 classes.
test acc: 0.9730000052452088
'''
小结
1.convnet是用于计算机视觉任务的最佳机器学习模型。即使在一个非常小的数据集上，也能够从头开始进行训练，并获得可观的结果。
2.在小数据集上，过拟合将是主要问题。数据扩增是一种处理图像数据时对抗过拟合问题的强有力方法。
3.通过特征提取，很容易在一个新数据集上使用已有的convnet。这也是处理小图像数据集的一个非常有价值的技术。
4.作为对特征提取的补充，还可以使用微调，将一部分通过现有模型学习到的特征适用在新问题上，该方法可以进一步提升性能。
