CV】keras_resnet 在cifar10数据集上分类

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码农有道 2020-06-01 14:29:17 510 收藏
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文章目录
1.导入库
2.数据准备
2.1 加载训练集
2.2 加载测试集
2.3 对类别做One-Hot编码
2.4 对图片像素的0-255值做归一化，并减去均值
3.搭建神经网络
3.1 定义函数resnet_layer，返回值是经过resnet_layer计算的结果
3.2 定义函数resnet_v1，返回值是模型对象
3.3 定义函数resnet_v2，返回值是模型对象
3.4 实例化模型对象
3.5 多GPU并行训练
3.6 打印模型架构信息
4. 模型训练
4.1 规划学习率（训练到后期时学习率需减小）
4.2 模型训练时的参数设置
4.3 使用图像增强的结果做模型训练
5.模型评估
5.1 加载训练好的模型
5.2 计算训练集的准确率
5.3 计算测试集的准确率
6. 模型测试结果可视化
6.1 随机选100张图可视化
6.2 随机选取100张图片的同时，要求10个类别，每个类别取10张
7.Keras中权重文件的读写
7.1 使用load_model方法加载模型文件
7.2 使用save_weights方法保存权重文件
7.3 使用load_weights方法加载权重文件
1.导入库
1.keras(resnet)：https://github.com/keras-team/keras/blob/master/examples/cifar10_resnet.py
2.用到的cifar10数据集和模型权重链接：https://pan.baidu.com/s/1L4oZAPg_9B_YipPeihmBwQ 提取码：8jcw
5个data_batch文件，每个1万数据

# 加入下面2行，可以使py代码文件中的修改即时生效
%load_ext autoreload
%autoreload 2
# 下行如果python2使用print，也得加上括号
from __future__ import print_function
import keras
# 导入keras库的这4种层：全连接层Dense，2维卷积层Conv2D，批量归一化层BatchNormalization，激活层Activation
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
# 导入keras库的这3种层：平均2维池化层AveratePooling2D, 输入层Input，激活层Activation
from keras.layers import AveragePooling2D, Input, Flatten
# 导入keras库的优化器：Adam优化器
from keras.optimizers import Adam
# 导入keras库的回传函数：模型检查点ModelCheckpoint, LearningRateScheduler
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
# 导入keras库的回传函数：学习率递减ReduceLROnPlateau
from keras.callbacks import ReduceLROnPlateau
# 导入keras库的图片处理函数：图片数据生成器
from keras.preprocessing.image import ImageDataGenerator
# 导入keras库的正则化函数：L2正则化
from keras.regularizers import l2
# 导入keras库的后端：backend中文叫做后端，取别名为K
from keras import backend as K
# 导入keras库的模型函数：Model
from keras.models import Model
# 导入keras库的数据集类：cifar10
from keras.datasets import cifar10
# 导入必需的常用库
import numpy as np
import os
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2.数据准备
2.1 加载训练集
官方代码没有指定路径：

from keras.datasets.cifar import load_batch #load_batch用于加载指定路径
## 加载数据集cifar10里面的训练集
def load_train_dataset(dirPath='../resources/cifar-10-batches-py/'):
train_sample_quantity = 50000
image_width = 32
image_height = 32
channel_quantity = 3
train_X = np.zeros((train_sample_quantity, channel_quantity, image_width, image_height),
dtype='uint8')
train_y = np.zeros((train_sample_quantity, ),
dtype='uint8') #下面循环写入相同大小零矩阵
for i in range(1, 6):
fileName = 'data_batch_%d' %i #看文件名格式
filePath = os.path.join(dirPath, fileName)
startIndex = (i - 1) * 10000
endIndex = i * 10000 #用到了load_batch，训练集定义5万，循环5次，0-1万行，1万-2万行....
train_X[startIndex:endIndex, :, :, :], train_y[startIndex:endIndex] = load_batch(filePath)

print('train_X矩阵转置前：', train_X.shape)
# 从官网上下载的数据集的4个维度为样本个数n、通道数c、宽度w、高度h
# Keras基于Tensorflow，数据的维度顺序要求：样本个数n、宽度w、高度h、通道数c，所以使用np.transpose完成矩阵转置
train_X = train_X.transpose(0, 2, 3, 1)
print('train_X矩阵转置后：', train_X.shape)
return train_X, train_y

dirPath = '../resources/cifar-10-batches-py/'
train_imageData, train_y = load_train_dataset()
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2.2 加载测试集
# 加载数据集cifar10里面的测试集
def load_test_dataset(dirPath='../resources/cifar-10-batches-py/'):
fileName = 'test_batch'
filePath = os.path.join(dirPath, fileName)
test_X, test_y = load_batch(filePath)
print('test_X矩阵转置前：', test_X.shape)
test_X = test_X.transpose(0, 2, 3, 1)
print('test_X矩阵转置后：', test_X.shape)
return test_X, test_y

dirPath = '../resources/cifar-10-batches-py/'
test_imageData, test_y = load_test_dataset()
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2.3 对类别做One-Hot编码
# 对类别ID做One-Hot编码
from keras.utils import to_categorical

class_quantity = 10
train_Y = to_categorical(train_y, class_quantity)
test_Y = to_categorical(test_y, class_quantity)
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2.4 对图片像素的0-255值做归一化，并减去均值
train_X = train_imageData.astype('float32') / 255
test_X = test_imageData.astype('float32') / 255
pixel_mean = np.mean(train_X, axis=0)
print('pixel_mean.shape:', pixel_mean.shape)
train_X = train_X - pixel_mean
test_X = test_X - pixel_mean
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3.搭建神经网络
3.1 定义函数resnet_layer，返回值是经过resnet_layer计算的结果
def resnet_layer(inputs, #定义了一层resnet_layer
num_filters=16,
kernel_size=3,
strides=1,
activation='relu',
batch_normalization=True,
conv_first=True):

conv = Conv2D(num_filters,
kernel_size=kernel_size,
strides=strides,
padding='same',
kernel_initializer='he_normal',
kernel_regularizer=l2(1e-4))

x = inputs
if conv_first: #为resnet V1
x = conv(x) #conv为一函数，相当于下图weight
if batch_normalization:
x = BatchNormalization()(x)
if activation is not None:
x = Activation(activation)(x)
else: #为resnet V2
if batch_normalization:
x = BatchNormalization()(x) #BatchNormalization()实例化一个函数对象
if activation is not None:
x = Activation(activation)(x)
x = conv(x)
return x
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3.2 定义函数resnet_v1，返回值是模型对象
def resnet_v1(input_shape, depth, num_classes=10):
if (depth - 2) % 6 != 0:
raise ValueError('depth should be 6n+2 (eg 20, 32, 44 in [a])')
# Start model definition.
num_filters = 16
num_res_blocks = int((depth - 2) / 6)

inputs = Input(shape=input_shape)
x = resnet_layer(inputs=inputs)
# Instantiate the stack of residual units
for stack in range(3):
for res_block in range(num_res_blocks):
strides = 1
if stack > 0 and res_block == 0: # first layer but not first stack
strides = 2 # downsample
y = resnet_layer(inputs=x,
num_filters=num_filters,
strides=strides)
y = resnet_layer(inputs=y,
num_filters=num_filters,
activation=None)
if stack > 0 and res_block == 0: # first layer but not first stack
# linear projection residual shortcut connection to match
# changed dims
x = resnet_layer(inputs=x,
num_filters=num_filters,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = keras.layers.add([x, y])
x = Activation('relu')(x)
num_filters *= 2
# Add classifier on top.
# v1 does not use BN after last shortcut connection-ReLU
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(num_classes,
activation='softmax',
kernel_initializer='he_normal')(y)

# Instantiate model.
model = Model(inputs=inputs, outputs=outputs)
return model
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3.3 定义函数resnet_v2，返回值是模型对象
def resnet_v2(input_shape, depth, num_classes=10):
if (depth - 2) % 9 != 0:#深度必须是9n+2，比如20层，56层，110层
raise ValueError('depth should be 9n+2 (eg 56 or 110 in [b])')
# Start model definition.
num_filters_in = 16 # 卷积核数量
num_res_blocks = int((depth - 2) / 9)
inputs = Input(shape=input_shape)

x = resnet_layer(inputs=inputs, # resnet lay 0如下表，第一次调用了一层resnet_layer
num_filters=num_filters_in,
conv_first=True)

# Instantiate the stack of residual units 实例化剩余单元的堆栈
for stage in range(3):
for res_block in range(num_res_blocks):
activation = 'relu'
batch_normalization = True
strides = 1
if stage == 0:
#如果stage和res_block == 0,不进行activation，batch_normalization，
num_filters_out = num_filters_in * 4
if res_block == 0: # first layer and first stage
activation = None
batch_normalization = False
else:
num_filters_out = num_filters_in * 2
if res_block == 0: # first layer but not first stage
strides = 2 # downsample
# bottleneck residual unit
y = resnet_layer(inputs=x,
num_filters=num_filters_in,
kernel_size=1,
strides=strides,
activation=activation,
batch_normalization=batch_normalization,
conv_first=False)
y = resnet_layer(inputs=y,
num_filters=num_filters_in,
conv_first=False)
y = resnet_layer(inputs=y,
num_filters=num_filters_out,
kernel_size=1,
conv_first=False)
if res_block == 0:
# linear projection residual shortcut connection to match
# changed dims
x = resnet_layer(inputs=x,
num_filters=num_filters_out,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = keras.layers.add([x, y]) #实现shotcut

num_filters_in = num_filters_out
#如上三个resnet_layer调用一个shotcut

# Add classifier on top.
# v2 has BN-ReLU before Pooling
x = BatchNormalization()(x) #如下三行代码对应下表对应后面几行
x = Activation('relu')(x)
x = AveragePooling2D(pool_size=8)(x)
y = Flatten()(x)
outputs = Dense(num_classes,
activation='softmax',
kernel_initializer='he_normal')(y)

# Instantiate model.
model = Model(inputs=inputs, outputs=outputs)
return model
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3.4 实例化模型对象
# Model version
# Orig paper: version = 1 (ResNet v1), Improved ResNet: version = 2 (ResNet v2)
version = 2
# Computed depth from supplied model parameter n
n = 2
if version == 1:
depth = n * 6 + 2
elif version == 2:
depth = n * 9 + 2

# 根据ResNet版本，获取对应的模型对象
if version == 2:
model = resnet_v2(input_shape=input_shape, depth=depth)
else:
model = resnet_v1(input_shape=input_shape, depth=depth)
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3.5 多GPU并行训练
https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/parallel_model.py
打开parallel_model.py文件，在原来文件加入红点一行:

from parallel_model import ParallelModel

gpu_count = 2
model = ParallelModel(model, gpu_count)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.001),
metrics=['accuracy'])
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3.6 打印模型架构信息
# Model name, depth and version
model_type = 'ResNet%dv%d' % (depth, version)
print(model_type)
model.summary()
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如下所示三个resnet_layer为一个shotcut，但如上图是两个layer再加，作者用的三个效果更好

用于数据集cifar10的图像分类网络ResNet20v2架构

处理层_0: 输入层，调用keras.layers中的Input类，通过传递参数shape实例化对象
数据层_0：矩阵形状： N x 32 x 32 x 3

resnet_layer_0 #对应2.3节第一个x = resnet_layer #第一个3表示上层通道数或卷积核fliter数
处理层_1：conv2d_1 卷积核形状： 3 x 3 x 3 x 16 / 1
#输入3大小3*3输出16，步长1，因为输出是16，所以加16个b
#所以3*3*3*16=432（卷积核占的参数量）再加16为第一层conv2d_1参数个数448
数据层_1：矩阵形状： N x 32 x 32 x 16 #None表示batch size
处理层_2：batch_normalization_1
数据层_2: 矩阵形状： N x 32 x 32 x 16
处理层_3：activation_1
数据层_3：矩阵形状： N x 32 x 32 x 16

resnet_layer_1
处理层_4：conv2d_2 卷积核形状： 16 x 1 x 1 x 16 / 1
数据层_4：矩阵形状： N x 32 x 32 x 16

resnet_layer_2
处理层_5：batch_normalization_2
数据层_5：矩阵形状： N x 32 x 32 x 16
处理层_6：activation_2
数据层_6: 矩阵形状： N x 32 x 32 x 16
处理层_7：conv2d_3 卷积核形状: 16 x 3 x 3 x 16 / 1
数据层_7：矩阵形状： N x 32 x 32 x 16

resnet_layer_3
处理层_8: batch_normalization_3
数据层_8：矩阵形状: N x 32 x 32 x 16
处理层_9：activation_3
数据层_9：矩阵形状: N x 32 x 32 x 16
处理层_10：conv2d_4 卷积核形状： 16 x 1 x 1 x 64 / 1
数据层_10：矩阵形状: N x 32 x 32 x 64

shortcut_1 #不默认对上层处理，取指定层处理，相加,route_1是自己定义的
处理层_11：route_1 取数据层_3 #数据层_3和下行数据层_11是同一层
数据层_11：矩阵形状： N x 32 x 32 x 16
处理层_12：conv2d_5 卷积核形状： 16 x 1 x 1 x 64 / 1
#（32-1+0）/1 +1=32，公式里第一个32表示上层长32，第二个32是得出这层长
#N x 32 x 32 x 16经过16 x 1 x 1 x 64 / 1处理得出N x 32 x 32 x 64即下行
数据层_12：矩阵形状： N x 32 x 32 x 64
处理层_13：add_1 把数据层_10、数据层_12相加
数据层_13：矩阵形状： N x 32 x 32 x 64

///

resnet_layer_4
处理层_14：batch_normalization_4
数据层_14：矩阵形状: N x 32 x 32 x 64
处理层_15：activation_4
数据层_15：矩阵形状: N x 32 x 32 x 64
处理层_16：conv2d_6 卷积核形状： 64 x 1 x 1 x 16 / 1
数据层_16：矩阵形状: N x 32 x 32 x 16


resnet_layer_5
处理层_17：batch_normalization_5
数据层_17：矩阵形状: N x 32 x 32 x 16
处理层_18：activation_5
数据层_18：矩阵形状: N x 32 x 32 x 16
处理层_19：conv2d_7 卷积核形状： 16 x 3 x 3 x 16 / 1
数据层_19：矩阵形状: N x 32 x 32 x 16


resnet_layer_6
处理层_20：batch_normalization_6
数据层_20：矩阵形状： N x 32 x 32 x 16
处理层_21：activation_6
数据层_21：矩阵形状: N x 32 x 32 x 16
处理层_22：conv2d_8 卷积核形状： 16 x 1 x 1 x 64 / 1
数据层_22：矩阵形状： N x 32 x 32 x 64

shortcut_2
处理层_23：add_2 把数据层_22、数据层_13相加
数据层_23：矩阵形状: N x 32 x 32 x 64

//

resnet_layer_7
处理层_24：batch_normalization_7
数据层_24：矩阵形状: N x 32 x 32 x 64
处理层_25：activation_7
数据层_25：矩阵形状: N x 32 x 32 x 64
处理层_26：conv2d_9 卷积核形状： 64 x 1 x 1 x 64 / 2
数据层_26：矩阵形状： N x 16 x 16 x 64 #通道扩展指最后位64变大，
#下采样指由32*32变为16*16

resnet_layer_8
处理层_25：batch_normalization_8
数据层_25：矩阵形状: N x 16 x 16 x 64
处理层_26：activation_8
数据层_26：矩阵形状: N x 16 x 16 x 64
处理层_27：conv2d_10 卷积核形状： 64 x 3 x 3 x 64 / 1
数据层_27：矩阵形状: N x 16 x 16 x 64

resnet_layer_9
处理层_28：batch_normalization_9
数据层_28：矩阵形状： N x 16 x 16 x 64
处理层_29：activation_9
数据层_29：矩阵形状 N x 16 x 16 x 64
处理层_30：conv2d_11 卷积核形状： 64 x 1 x 1 x 128 / 1
数据层_30：矩阵形状 N x 16 x 16 x 128

shortcut_3
处理层_31：route_2 取数据层_23
数据层_31：矩阵形状: N x 32 x 32 x 64
处理层_32：conv2d_12 卷积核形状： 64 x 1 x 1 x 128 / 2
数据层_32：矩阵形状： N x 16 x 16 x 128
处理层_33：add_3 把数据层_32、数据层_30相加
数据层_33：矩阵形状： N x 16 x 16 x 128

resnet_layer_10
处理层_34：batch_normalization_10
数据层_34：矩阵形状: N x 16 x 16 x 128
处理层_35：activation_10
数据层_35：矩阵形状: N x 16 x 16 x 128
处理层_36：conv2d_13 卷积核形状： 128 x 1 x 1 x 64 / 1
数据层_36：矩阵形状: N x 16 x 16 x 64

resnet_layer_11
处理层_37：batch_normalization_11
数据层_37：矩阵形状: N x 16 x 16 x 64
处理层_38：activation_11
数据层_38：矩阵形状: N x 16 x 16 x 64
处理层_39：conv2d_14 卷积核形状: 64 x 3 x 3 x 64 / 1
数据层_39：矩阵形状: N x 16 x 16 x 64

resnet_layer_12
处理层_40：batch_normalization_12
数据层_40：矩阵形状: N x 16 x 16 x 64
处理层_41：activation_12
数据层_41：矩阵形状: N x 16 x 16 x 64
处理层_42：conv2d_15 卷积核形状: 64 x 1 x 1 x 128 / 1
数据层_42：矩阵形状: N x 16 x 16 x 128

shortcut_4
处理层_43：add_4 把数据层_42、数据层_33相加
数据层_43：矩阵形状： N x 16 x 16 x 128

resnet_layer_13
处理层_44：batch_normalization_13
数据层_44：矩阵形状: N x 16 x 16 x 128
处理层_45：activation_13
数据层_45：矩阵形状: N x 16 x 16 x 128
处理层_46：conv2d_16 卷积核形状: 128 x 1 x 1 x 128 / 2
数据层_46：矩阵形状: N x 8 x 8 x 128

resnet_layer_14
处理层_47：batch_normalization_14
数据层_47：矩阵形状: N x 8 x 8 x 128
处理层_48：activation_14
数据层_48：矩阵形状: N x 8 x 8 x 128
处理层_49：conv2d_17 卷积核形状: 128 x 3 x 3 x 128 / 1
数据层_49：矩阵形状: N x 8 x 8 x 128

resnet_layer_15
处理层_50：batch_normalization_15
数据层_50：矩阵形状: N x 8 x 8 x 128
处理层_51：activation_15
数据层_51：矩阵形状: N x 8 x 8 x 128
处理层_52：conv2d_18 卷积核形状: 128 x 1 x 1 x 256 / 1
数据层_52：矩阵形状: N x 8 x 8 x 256

shortcut_5
处理层_53：route_3 取数据层_43
数据层_53：矩阵形状: N x 16 x 16 x 128
处理层_54：conv2d_19 卷积核形状： 128 x 1 x 1 x 256 / 2
数据层_54：矩阵形状: N x 8 x 8 x 256
处理层_55：add_5 把数据层_54、数据层_52相加
数据层_55：矩阵形状： N x 8 x 8 x 256

resnet_layer_16
处理层_56：batch_normalization_16
数据层_56：矩阵形状: N x 8 x 8 x 256
处理层_57：activation_16
数据层_57：矩阵形状: N x 8 x 8 x 256
处理层_58：conv2d_20 卷积核形状: 256 x 1 x 1 x 128 / 1
数据层_58：矩阵形状: N x 8 x 8 x 128

resnet_layer_17
处理层_59：batch_normalization_17
数据层_59：矩阵形状: N x 8 x 8 x 128
处理层_60：activation_17
数据层_60：矩阵形状: N x 8 x 8 x 128
处理层_61：conv2d_21 卷积核形状: 128 x 3 x 3 x 128 / 1
数据层_61：矩阵形状: N x 8 x 8 x 128

resnet_layer_18
处理层_62：batch_normalization_18
数据层_62：矩阵形状: N x 8 x 8 x 128
处理层_63：activation_18
数据层_63：矩阵形状: N x 8 x 8 x 128
处理层_64：conv2d_22 卷积核形状: 128 x 1 x 1 x 256 / 1
数据层_64：矩阵形状: N x 8 x 8 x 256

shortcut_6 #18层每3次一共做了6次shortcut
处理层_62：add_6 把数据层_64、数据层_55相加
数据层_62：矩阵形状： N x 8 x 8 x 256

/

classifier#如果是yoloV3就是yolo层了
处理层_63：batch_normalization_19
数据层_63：矩阵形状： N x 8 x 8 x 256
处理层_64：activation_19
数据层_64：矩阵形状： N x 8 x 8 x 256
处理层_65：average_pooling2d_1
数据层_65：矩阵形状： N x 1 x 1 x 256
处理层_66：flatten_1
数据层_66：矩阵形状： N x 256
处理层_67：dense_1
数据层_67：矩阵形状： N x 10
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所以ResNet20_V2：这个20是conv共19层（layer0）+dense1层

4. 模型训练
4.1 规划学习率（训练到后期时学习率需减小）
def lr_schedule(epoch):
lr = 1e-3
if epoch > 180:
lr *= 0.5e-3
elif epoch > 160:
lr *= 1e-3
elif epoch > 120:
lr *= 1e-2
elif epoch > 80:
lr *= 1e-1
print('Learning rate: ', lr)
return lr
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4.2 模型训练时的参数设置
# Training parameters
batch_size = 64 # orig paper trained all networks with batch_size=128
epochs = 200

# Prepare model model saving directory.
save_dir = os.path.abspath('../resources/saved_models')
model_name = 'cifar10_%s_model.{epoch:03d}.h5' % model_type
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
filepath = os.path.join(save_dir, model_name)

# Prepare callbacks for model saving and for learning rate adjustment.
checkpoint = ModelCheckpoint(filepath=filepath,
monitor='val_acc',
verbose=0,
save_best_only=True)
lr_scheduler = LearningRateScheduler(lr_schedule)
lr_reducer = ReduceLROnPlateau(factor=np.sqrt(0.1),
cooldown=0,
patience=5,
min_lr=0.5e-6)
callbacks = [checkpoint, lr_reducer, lr_scheduler]
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4.3 使用图像增强的结果做模型训练
data_augmentation = True
if data_augmentation:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
# set input mean to 0 over the dataset
featurewise_center=False,
# set each sample mean to 0
samplewise_center=False,
# divide inputs by std of dataset
featurewise_std_normalization=False,
# divide each input by its std
samplewise_std_normalization=False,
# apply ZCA whitening
zca_whitening=False,
# epsilon for ZCA whitening
zca_epsilon=1e-06,
# randomly rotate images in the range (deg 0 to 180)
rotation_range=0,
# randomly shift images horizontally
width_shift_range=0.1,
# randomly shift images vertically
height_shift_range=0.1,
# set range for random shear
shear_range=0.,
# set range for random zoom
zoom_range=0.,
# set range for random channel shifts
channel_shift_range=0.,
# set mode for filling points outside the input boundaries
fill_mode='nearest',
# value used for fill_mode = "constant"
cval=0.,
# randomly flip images
horizontal_flip=True,
# randomly flip images
vertical_flip=False,
# set rescaling factor (applied before any other transformation)
rescale=None,
# set function that will be applied on each input
preprocessing_function=None,
# image data format, either "channels_first" or "channels_last"
data_format=None,
# fraction of images reserved for validation (strictly between 0 and 1)
validation_split=0.0)

datagen.fit(x_train)

# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
validation_data=(x_test, y_test),
epochs=epochs,
verbose=1,
workers=4,
callbacks=callbacks)
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附：python的生成器与keras.preprocessing.image文件的ImageDataGenerator类的关系：
问号可以找在哪个路径：

如下图打开image.py发现是继承image:

找image:

5.模型评估
5.1 加载训练好的模型
from keras.models import load_model
from keras.optimizers import Adam

model_filePath = '../resources/saved_models/cifar10_ResNet56v2_model.162.h5'
model = load_model(model_filePath)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.001),
metrics=['accuracy'])
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5.2 计算训练集的准确率
scores = model.evaluate(train_X, train_Y, verbose=1, batch_size=1000)
print('Test loss:%.6f' %scores[0])
print('Test accuracy:%.6f' %scores[1])
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5.3 计算测试集的准确率
scores = model.evaluate(test_X, test_Y, verbose=1, batch_size=1000)
print('Test loss:%.6f' %scores[0])
print('Test accuracy:%.6f' %scores[1])
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6. 模型测试结果可视化
6.1 随机选100张图可视化
import math
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
import random

def draw_image(position, image, title, isTrue):
plt.subplot(*position)
plt.imshow(image)
plt.axis('off')
if not isTrue:
plt.title(title, color='red')
else:
plt.title(title)

def batch_draw_images(model, batch_size, test_imageData, test_X, test_y, id2name_dict):
index_list = list(range(len(test_imageData)))
selected_index_list = random.sample(index_list, batch_size)
true_imageData = test_imageData[selected_index_list]
true_X = test_X[selected_index_list]
true_y = np.array(test_y)[selected_index_list]
predict_Y = model.predict(true_X)
predict_y = np.argmax(predict_Y, axis=1)
row_number = math.ceil(batch_size ** 0.5)
column_number = row_number
plt.figure(figsize=(row_number+8, column_number+8))
for i in range(row_number):
for j in range(column_number):
index = i * column_number + j
if index < batch_size:
position = (row_number, column_number, index+1)
image = true_imageData[index]
actual_classId = true_y[index]
predict_classId = predict_y[index]
isTrue = actual_classId==predict_classId
actual_className = id2name_dict[actual_classId]
predict_className = id2name_dict[predict_classId]
title = 'actual:%s\npredict:%s' %(actual_className, predict_className)
draw_image(position, image, title, isTrue)

batch_size = 100 #展示100张图
className_list = ['飞机', '汽车', '鸟', '猫', '鹿', '狗', '青蛙', '马', '船', '卡车']
id2name_dict = {a:b for a, b in enumerate(className_list)}
batch_draw_images(model, batch_size, test_imageData, test_X, test_y, id2name_dict)
plt.show()
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6.2 随机选取100张图片的同时，要求10个类别，每个类别取10张
def get_selectedIndexList(test_y, batch_size):
assert batch_size % 10 == 0, 'batch_size must be times by 10, or you change function get_selectedIndexList'
column_number = int(batch_size / 10)
classId_ndarray = np.unique(test_y)
selected_index_list = []
for i, classId in enumerate(classId_ndarray):
index_ndarray = np.where(test_y==classId)[0]
selected_index_ndarray = np.random.choice(index_ndarray, column_number)
selected_index_list.extend(selected_index_ndarray.tolist())
return selected_index_list

def batch_draw_images_2(model, selected_index_list, test_imageData, test_X, test_y, id2name_dict):
true_imageData = test_imageData[selected_index_list]
true_X = test_X[selected_index_list]
true_y = np.array(test_y)[selected_index_list]
predict_Y = model.predict(true_X)
predict_y = np.argmax(predict_Y, axis=1)
row_number = math.ceil(batch_size ** 0.5)
column_number = row_number
plt.figure(figsize=(row_number+8, column_number+8))
for i in range(row_number):
for j in range(column_number):
index = i * column_number + j
if index < batch_size:
position = (row_number, column_number, index+1)
image = true_imageData[index]
actual_classId = true_y[index]
predict_classId = predict_y[index]
isTrue = actual_classId==predict_classId
actual_className = id2name_dict[actual_classId]
predict_className = id2name_dict[predict_classId]
title = 'actual:%s\npredict:%s' %(actual_className, predict_className)
draw_image(position, image, title, isTrue)

batch_size = 100
className_list = ['飞机', '汽车', '鸟', '猫', '鹿', '狗', '青蛙', '马', '船', '卡车']
id2name_dict = {a:b for a, b in enumerate(className_list)}
selected_index_list = get_selectedIndexList(test_y, batch_size)
batch_draw_images_2(model, selected_index_list, test_imageData, test_X, test_y, id2name_dict)
plt.show()
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7.Keras中权重文件的读写
7.1 使用load_model方法加载模型文件
from keras.models import load_model
from keras.optimizers import Adam

model_filePath = '../resources/saved_models/cifar10_ResNet56v2_model.162.h5'
model = load_model(model_filePath)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.001),
metrics=['accuracy'])
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7.2 使用save_weights方法保存权重文件
weights_h5FilePath = '../resources/saved_models/resnet56v2_weights.h5'
model.save_weights(weights_h5FilePath)
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7.3 使用load_weights方法加载权重文件
input_shape = (32, 32, 3)
depth = 56
model = resnet_v2(input_shape, depth)
weights_h5FilePath = '../resources/saved_models/resnet56v2_weights.h5'
model.load_weights(weights_h5FilePath)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=0.001),
metrics=['accuracy'])