前言
对CIFAR-10 数据集的分类是机器学习中一个公开的基准测试问题，其任务是对一组大小为32x32的RGB图像进行分类，这些图像涵盖了10个类别：
飞机， 汽车， 鸟， 猫， 鹿， 狗， 青蛙， 马， 船以及卡车。
首先来看下cifar10数据集：
这里面一共有五个训练文件，一个测试文件。网上的教程大多都是需要以下五个文件，在这里自己实现了单文件的训练代码。代码需要提前下载好cifar10数据，CIFAR-10 python version版本的哦~
源代码
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 24 09:43:25 2018
@author: new
"""
import numpy as np
import os
import sys
import keras.backend as K
from six.moves import cPickle
import cv2
import numpy as np
import tensorflow as tf
import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD, Adadelta, Adagrad
from keras.utils import np_utils, generic_utils
from keras.utils import plot_model
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
#【0】设置超参
batch_size = 32
num_classes = 10
epochs = 5
data_augmentation = True
def load_batch(fpath, label_key='labels'):
f = open(fpath, 'rb')
if sys.version_info < (3,):
d = cPickle.load(f)
else:
d = cPickle.load(f, encoding='bytes')
# decode utf8
d_decoded = {}
for k, v in d.items():
d_decoded[k.decode('utf8')] = v
d = d_decoded
f.close()
data = d['data']
labels = d[label_key]
data = data.reshape(data.shape[0], 3, 32, 32)
return data, labels
def load_data():
dirname = 'C:/Users/new/Desktop/cifar-10-batches-py'
num_train_samples = 50000
x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')
y_train = np.empty((num_train_samples,), dtype='uint8')
for i in range(1, 6):
fpath = os.path.join(dirname, 'data_batch_' + str(i))
(x_train[(i - 1) * 10000: i * 10000, :, :, :],
y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)
fpath = os.path.join(dirname, 'test_batch')
x_test, y_test = load_batch(fpath)
y_train = np.reshape(y_train, (len(y_train), 1))
y_test = np.reshape(y_test, (len(y_test), 1))
if K.image_data_format() == 'channels_last':
x_train = x_train.transpose(0, 2, 3, 1)
x_test = x_test.transpose(0, 2, 3, 1)
return (x_train, y_train), (x_test, y_test)
(x_train, y_train), (x_test, y_test)=load_data()
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print('x_test shape:', x_test.shape)
print('y_test shape:', y_test.shape)
plt.figure(1)
plt.imshow(x_train[0]) # 显示第一张训练图片
plt.figure(2)
plt.imshow(x_test[0]) # 显示第一张测试图片
# 【3】将标签转化成 one-hot 编码
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# 【4】构建深度CNN序贯模型
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
print(model.summary()) # 打印模型概况
# 【5】编译模型
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)#初始化一个 RMSprop 优化器
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
# 【6】数据预处理/增强+模型训练
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
)
else:
print('Using real-time data augmentation.')
# ImageDataGenerator:图片生成器，用以生成一个batch的图像数据，训练时该函数会无限生成数据
# 直到达到规定的epoch次数。图片生成(CPU)和训练（GPU）并行执行。
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=0, # 随机旋转的角度范围
width_shift_range=0.1, # 随机水平偏移的幅度范围
height_shift_range=0.1,
horizontal_flip=True, # 随机水平翻转
vertical_flip=False)
datagen.fit(x_train) # 计算样本的统计信息，进行数据预处理（如去中心化，标准化）
model.fit_generator(datagen.flow(x_train, y_train, # datagen.flow()不断生成一个batch的数据用于模型训练
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4)
# 【7】保存模型以及权重
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# 【8】测试集评估模型
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
实验结果
Using TensorFlow backend.
x_train shape: (50000, 32, 32, 3)
y_train shape: (50000, 1)
x_test shape: (10000, 32, 32, 3)
y_test shape: (10000, 1)
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
activation_1 (Activation) (None, 32, 32, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 30, 30, 32) 9248
_________________________________________________________________
activation_2 (Activation) (None, 30, 30, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 15, 15, 64) 18496
_________________________________________________________________
activation_3 (Activation) (None, 15, 15, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 13, 13, 64) 36928
_________________________________________________________________
activation_4 (Activation) (None, 13, 13, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 2304) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 1180160
_________________________________________________________________
activation_5 (Activation) (None, 512) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 5130
_________________________________________________________________
activation_6 (Activation) (None, 10) 0
=================================================================
Total params: 1,250,858
Trainable params: 1,250,858
Non-trainable params: 0
_________________________________________________________________
None
Using real-time data augmentation.
Epoch 1/5
1563/1563 [==============================] - 102s 65ms/step - loss: 1.8342 - acc: 0.3229 - val_loss: 1.5518 - val_acc: 0.4325
Epoch 2/5
1563/1563 [==============================] - 120s 77ms/step - loss: 1.5533 - acc: 0.4310 - val_loss: 1.4069 - val_acc: 0.4883
Epoch 3/5
1563/1563 [==============================] - 108s 69ms/step - loss: 1.4322 - acc: 0.4846 - val_loss: 1.2653 - val_acc: 0.5508
Epoch 4/5
1563/1563 [==============================] - 107s 68ms/step - loss: 1.3429 - acc: 0.5180 - val_loss: 1.1613 - val_acc: 0.5869
Epoch 5/5
1563/1563 [==============================] - 107s 69ms/step - loss: 1.2704 - acc: 0.5454 - val_loss: 1.1002 - val_acc: 0.6138
Saved trained model at C:\Users\new\Desktop\chapter_2\saved_models\keras_cifar10_trained_model.h5
10000/10000 [==============================] - 6s 611us/step
Test loss: 1.1002309656143188
Test accuracy: 0.6138由于迭代的次数比较少，所以测试集上的准确率不是太高，可以多迭代几次试下哦~~~
加载模型进行预测
model = load_model('C:/Users/new/Desktop/chapter_2/saved_models/keras_cifar10_trained_model.h5')
print('test after load: ', model.predict(x_test[0:2])) 测试后的结果：
test after load: [[0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00
0.00000000e+00 2.44679976e-34 0.00000000e+00 0.00000000e+00
1.05497485e-29 0.00000000e+00]
[0.00000000e+00 1.46545753e-08 0.00000000e+00 0.00000000e+00
0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
1.00000000e+00 0.00000000e+00]]这里是one-hot向量，最大的那个就是预测出的类别~~~