文章目录
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GITHUB地址https://github.com/fz861062923/Keras/new/master
下载数据
读取CIFAR-10数据
y代表label,x代表image
from keras.datasets import cifar10
import numpy as np
(x_train,y_train),\
(x_test,y_test)=cifar10.load_data()
C:\Users\admin\AppData\Local\conda\conda\envs\tensorflow\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 384s 2us/step
查看训练数据
不同的label对应着不同的图像，0:airplane,1:automobile,2:bird,3:cat,4:deer,5:dog,6:frog,7:horse,8:ship,9:truck
shape一下，看看数据的组成结构
x_train.shape#image的结构
(50000, 32, 32, 3)
y_train.shape#label的结构
(50000, 1)
查看第一张image的内容
x_train[0]
array([[[ 59, 62, 63],
[ 43, 46, 45],
[ 50, 48, 43],
...,
[158, 132, 108],
[152, 125, 102],
[148, 124, 103]],
[[ 16, 20, 20],
[ 0, 0, 0],
[ 18, 8, 0],
...,
[123, 88, 55],
[119, 83, 50],
[122, 87, 57]],
[[ 25, 24, 21],
[ 16, 7, 0],
[ 49, 27, 8],
...,
[118, 84, 50],
[120, 84, 50],
[109, 73, 42]],
...,
[[208, 170, 96],
[201, 153, 34],
[198, 161, 26],
...,
[160, 133, 70],
[ 56, 31, 7],
[ 53, 34, 20]],
[[180, 139, 96],
[173, 123, 42],
[186, 144, 30],
...,
[184, 148, 94],
[ 97, 62, 34],
[ 83, 53, 34]],
[[177, 144, 116],
[168, 129, 94],
[179, 142, 87],
...,
[216, 184, 140],
[151, 118, 84],
[123, 92, 72]]], dtype=uint8)
可视化image
建立label与image一一对应的关系字典
dict={0:'airplane',1:'automobile',2:'bird',3:'cat',4:'deer',5:'dog',6:'frog',7:'horse',8:'ship',9:'truck'}
import matplotlib.pyplot as plt
def plot_images_labels_prediction(images,labels,
prediction,idx,num=10):
fig = plt.gcf()
fig.set_size_inches(12, 14)
if num>25: num=25
for i in range(0, num):
ax=plt.subplot(5,5, 1+i)
ax.imshow(images[idx],cmap='binary')
title= str(i)+'.'+dict[labels[i][0]]
if len(prediction)>0:
title+='=>'+dict[prediction[i]]
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()#使用的还是MNIST数据集的可视化函数，只是改变了title的定义，使其看得更明了
查看前面十个图片
plot_images_labels_prediction(x_train,y_train,prediction=[],idx=0)
将images进行预处理
对数据进行标准化，和处理MINIST数据集的思想是一样的
x_train_normalize=x_train/255.0
x_test_normalize=x_test/255.0
查看标准化后的结果
x_train_normalize
array([[[[0.23137255, 0.24313725, 0.24705882],
[0.16862745, 0.18039216, 0.17647059],
[0.19607843, 0.18823529, 0.16862745],
...,
[0.61960784, 0.51764706, 0.42352941],
[0.59607843, 0.49019608, 0.4 ],
[0.58039216, 0.48627451, 0.40392157]],
[[0.0627451 , 0.07843137, 0.07843137],
[0. , 0. , 0. ],
[0.07058824, 0.03137255, 0. ],
...,
[0.48235294, 0.34509804, 0.21568627],
[0.46666667, 0.3254902 , 0.19607843],
[0.47843137, 0.34117647, 0.22352941]],
[[0.09803922, 0.09411765, 0.08235294],
[0.0627451 , 0.02745098, 0. ],
[0.19215686, 0.10588235, 0.03137255],
...,
[0.4627451 , 0.32941176, 0.19607843],
[0.47058824, 0.32941176, 0.19607843],
[0.42745098, 0.28627451, 0.16470588]],
...,
[[0.81568627, 0.66666667, 0.37647059],
[0.78823529, 0.6 , 0.13333333],
[0.77647059, 0.63137255, 0.10196078],
...,
[0.62745098, 0.52156863, 0.2745098 ],
[0.21960784, 0.12156863, 0.02745098],
[0.20784314, 0.13333333, 0.07843137]],
[[0.70588235, 0.54509804, 0.37647059],
[0.67843137, 0.48235294, 0.16470588],
[0.72941176, 0.56470588, 0.11764706],
...,
[0.72156863, 0.58039216, 0.36862745],
[0.38039216, 0.24313725, 0.13333333],
[0.3254902 , 0.20784314, 0.13333333]],
[[0.69411765, 0.56470588, 0.45490196],
[0.65882353, 0.50588235, 0.36862745],
[0.70196078, 0.55686275, 0.34117647],
...,
[0.84705882, 0.72156863, 0.54901961],
[0.59215686, 0.4627451 , 0.32941176],
[0.48235294, 0.36078431, 0.28235294]]],
[[[0.60392157, 0.69411765, 0.73333333],
[0.49411765, 0.5372549 , 0.53333333],
[0.41176471, 0.40784314, 0.37254902],
...,
[0.35686275, 0.37254902, 0.27843137],
[0.34117647, 0.35294118, 0.27843137],
[0.30980392, 0.31764706, 0.2745098 ]],
[[0.54901961, 0.62745098, 0.6627451 ],
[0.56862745, 0.6 , 0.60392157],
[0.49019608, 0.49019608, 0.4627451 ],
...,
[0.37647059, 0.38823529, 0.30588235],
[0.30196078, 0.31372549, 0.24313725],
[0.27843137, 0.28627451, 0.23921569]],
[[0.54901961, 0.60784314, 0.64313725],
[0.54509804, 0.57254902, 0.58431373],
[0.45098039, 0.45098039, 0.43921569],
...,
[0.30980392, 0.32156863, 0.25098039],
[0.26666667, 0.2745098 , 0.21568627],
[0.2627451 , 0.27058824, 0.21568627]],
...,
[[0.68627451, 0.65490196, 0.65098039],
[0.61176471, 0.60392157, 0.62745098],
[0.60392157, 0.62745098, 0.66666667],
...,
[0.16470588, 0.13333333, 0.14117647],
[0.23921569, 0.20784314, 0.22352941],
[0.36470588, 0.3254902 , 0.35686275]],
[[0.64705882, 0.60392157, 0.50196078],
[0.61176471, 0.59607843, 0.50980392],
[0.62352941, 0.63137255, 0.55686275],
...,
[0.40392157, 0.36470588, 0.37647059],
[0.48235294, 0.44705882, 0.47058824],
[0.51372549, 0.4745098 , 0.51372549]],
[[0.63921569, 0.58039216, 0.47058824],
[0.61960784, 0.58039216, 0.47843137],
[0.63921569, 0.61176471, 0.52156863],
...,
[0.56078431, 0.52156863, 0.54509804],
[0.56078431, 0.5254902 , 0.55686275],
[0.56078431, 0.52156863, 0.56470588]]],
[[[1. , 1. , 1. ],
[0.99215686, 0.99215686, 0.99215686],
[0.99215686, 0.99215686, 0.99215686],
...,
[0.99215686, 0.99215686, 0.99215686],
[0.99215686, 0.99215686, 0.99215686],
[0.99215686, 0.99215686, 0.99215686]],
[[1. , 1. , 1. ],
[1. , 1. , 1. ],
[1. , 1. , 1. ],
...,
[1. , 1. , 1. ],
[1. , 1. , 1. ],
[1. , 1. , 1. ]],
[[1. , 1. , 1. ],
[0.99607843, 0.99607843, 0.99607843],
[0.99607843, 0.99607843, 0.99607843],
...,
[0.99607843, 0.99607843, 0.99607843],
[0.99607843, 0.99607843, 0.99607843],
[0.99607843, 0.99607843, 0.99607843]],
...,
[[0.44313725, 0.47058824, 0.43921569],
[0.43529412, 0.4627451 , 0.43529412],
[0.41176471, 0.43921569, 0.41568627],
...,
[0.28235294, 0.31764706, 0.31372549],
[0.28235294, 0.31372549, 0.30980392],
[0.28235294, 0.31372549, 0.30980392]],
[[0.43529412, 0.4627451 , 0.43137255],
[0.40784314, 0.43529412, 0.40784314],
[0.38823529, 0.41568627, 0.38431373],
...,
[0.26666667, 0.29411765, 0.28627451],
[0.2745098 , 0.29803922, 0.29411765],
[0.30588235, 0.32941176, 0.32156863]],
[[0.41568627, 0.44313725, 0.41176471],
[0.38823529, 0.41568627, 0.38431373],
[0.37254902, 0.4 , 0.36862745],
...,
[0.30588235, 0.33333333, 0.3254902 ],
[0.30980392, 0.33333333, 0.3254902 ],
[0.31372549, 0.3372549 , 0.32941176]]],
...,
[[[0.1372549 , 0.69803922, 0.92156863],
[0.15686275, 0.69019608, 0.9372549 ],
[0.16470588, 0.69019608, 0.94509804],
...,
[0.38823529, 0.69411765, 0.85882353],
[0.30980392, 0.57647059, 0.77254902],
[0.34901961, 0.58039216, 0.74117647]],
[[0.22352941, 0.71372549, 0.91764706],
[0.17254902, 0.72156863, 0.98039216],
[0.19607843, 0.71764706, 0.94117647],
...,
[0.61176471, 0.71372549, 0.78431373],
[0.55294118, 0.69411765, 0.80784314],
[0.45490196, 0.58431373, 0.68627451]],
[[0.38431373, 0.77254902, 0.92941176],
[0.25098039, 0.74117647, 0.98823529],
[0.27058824, 0.75294118, 0.96078431],
...,
[0.7372549 , 0.76470588, 0.80784314],
[0.46666667, 0.52941176, 0.57647059],
[0.23921569, 0.30980392, 0.35294118]],
...,
[[0.28627451, 0.30980392, 0.30196078],
[0.20784314, 0.24705882, 0.26666667],
[0.21176471, 0.26666667, 0.31372549],
...,
[0.06666667, 0.15686275, 0.25098039],
[0.08235294, 0.14117647, 0.2 ],
[0.12941176, 0.18823529, 0.19215686]],
[[0.23921569, 0.26666667, 0.29411765],
[0.21568627, 0.2745098 , 0.3372549 ],
[0.22352941, 0.30980392, 0.40392157],
...,
[0.09411765, 0.18823529, 0.28235294],
[0.06666667, 0.1372549 , 0.20784314],
[0.02745098, 0.09019608, 0.1254902 ]],
[[0.17254902, 0.21960784, 0.28627451],
[0.18039216, 0.25882353, 0.34509804],
[0.19215686, 0.30196078, 0.41176471],
...,
[0.10588235, 0.20392157, 0.30196078],
[0.08235294, 0.16862745, 0.25882353],
[0.04705882, 0.12156863, 0.19607843]]],
[[[0.74117647, 0.82745098, 0.94117647],
[0.72941176, 0.81568627, 0.9254902 ],
[0.7254902 , 0.81176471, 0.92156863],
...,
[0.68627451, 0.76470588, 0.87843137],
[0.6745098 , 0.76078431, 0.87058824],
[0.6627451 , 0.76078431, 0.8627451 ]],
[[0.76078431, 0.82352941, 0.9372549 ],
[0.74901961, 0.81176471, 0.9254902 ],
[0.74509804, 0.80784314, 0.92156863],
...,
[0.67843137, 0.75294118, 0.8627451 ],
[0.67058824, 0.74901961, 0.85490196],
[0.65490196, 0.74509804, 0.84705882]],
[[0.81568627, 0.85882353, 0.95686275],
[0.80392157, 0.84705882, 0.94117647],
[0.8 , 0.84313725, 0.9372549 ],
...,
[0.68627451, 0.74901961, 0.85098039],
[0.6745098 , 0.74509804, 0.84705882],
[0.6627451 , 0.74901961, 0.84313725]],
...,
[[0.81176471, 0.78039216, 0.70980392],
[0.79607843, 0.76470588, 0.68627451],
[0.79607843, 0.76862745, 0.67843137],
...,
[0.52941176, 0.51764706, 0.49803922],
[0.63529412, 0.61960784, 0.58823529],
[0.65882353, 0.63921569, 0.59215686]],
[[0.77647059, 0.74509804, 0.66666667],
[0.74117647, 0.70980392, 0.62352941],
[0.70588235, 0.6745098 , 0.57647059],
...,
[0.69803922, 0.67058824, 0.62745098],
[0.68627451, 0.6627451 , 0.61176471],
[0.68627451, 0.6627451 , 0.60392157]],
[[0.77647059, 0.74117647, 0.67843137],
[0.74117647, 0.70980392, 0.63529412],
[0.69803922, 0.66666667, 0.58431373],
...,
[0.76470588, 0.72156863, 0.6627451 ],
[0.76862745, 0.74117647, 0.67058824],
[0.76470588, 0.74509804, 0.67058824]]],
[[[0.89803922, 0.89803922, 0.9372549 ],
[0.9254902 , 0.92941176, 0.96862745],
[0.91764706, 0.9254902 , 0.96862745],
...,
[0.85098039, 0.85882353, 0.91372549],
[0.86666667, 0.8745098 , 0.91764706],
[0.87058824, 0.8745098 , 0.91372549]],
[[0.87058824, 0.86666667, 0.89803922],
[0.9372549 , 0.9372549 , 0.97647059],
[0.91372549, 0.91764706, 0.96470588],
...,
[0.8745098 , 0.8745098 , 0.9254902 ],
[0.89019608, 0.89411765, 0.93333333],
[0.82352941, 0.82745098, 0.8627451 ]],
[[0.83529412, 0.80784314, 0.82745098],
[0.91764706, 0.90980392, 0.9372549 ],
[0.90588235, 0.91372549, 0.95686275],
...,
[0.8627451 , 0.8627451 , 0.90980392],
[0.8627451 , 0.85882353, 0.90980392],
[0.79215686, 0.79607843, 0.84313725]],
...,
[[0.58823529, 0.56078431, 0.52941176],
[0.54901961, 0.52941176, 0.49803922],
[0.51764706, 0.49803922, 0.47058824],
...,
[0.87843137, 0.87058824, 0.85490196],
[0.90196078, 0.89411765, 0.88235294],
[0.94509804, 0.94509804, 0.93333333]],
[[0.5372549 , 0.51764706, 0.49411765],
[0.50980392, 0.49803922, 0.47058824],
[0.49019608, 0.4745098 , 0.45098039],
...,
[0.70980392, 0.70588235, 0.69803922],
[0.79215686, 0.78823529, 0.77647059],
[0.83137255, 0.82745098, 0.81176471]],
[[0.47843137, 0.46666667, 0.44705882],
[0.4627451 , 0.45490196, 0.43137255],
[0.47058824, 0.45490196, 0.43529412],
...,
[0.70196078, 0.69411765, 0.67843137],
[0.64313725, 0.64313725, 0.63529412],
[0.63921569, 0.63921569, 0.63137255]]]])
对label数据进行预处理
进行一位有效编码和MNIST数据集的处理是一样的
from keras.utils import np_utils
y_train_onehot=np_utils.to_categorical(y_train)
y_test_onehot=np_utils.to_categorical(y_test)
查看转化后的label标签字段
y_train_onehot.shape
y_train_onehot[0]
array([0., 0., 0., 0., 0., 0., 1., 0., 0., 0.], dtype=float32)
建立卷积神经网络模型
from keras.models import Sequential
from keras.layers import Dense,Dropout,Activation,Flatten
from keras.layers import Conv2D,MaxPooling2D,ZeroPadding2D
建立线性堆叠模型
model=Sequential()
建立卷积层1
model.add(Conv2D(filters=32,kernel_size=(3,3),
input_shape=(32,32,3),activation='relu',
padding='same'))
model.add(Dropout(rate=0.25))#设置dropout为25%
建立池化层1
model.add(MaxPooling2D(pool_size=(2,2)))
#将32*32的图像变为16*16，图像的个数仍然为32个
建立卷积层2
model.add(Conv2D(filters=64,kernel_size=(3,3),
activation='relu',padding='same'))
#将图像个数变为64，图像的大小仍然为16*16
model.add(Dropout(rate=0.25))#避免过拟合
建立池化层2
model.add(MaxPooling2D(pool_size=(2,2)))
建立平坦层
model.add(Flatten())
model.add(Dropout(rate=0.25))
建立隐藏层
model.add(Dense(1024,activation='relu'))
model.add(Dropout(rate=0.25))#避免过拟合
建立输出层
model.add(Dense(10,activation='softmax'))
查看模型摘要
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 16, 16, 64) 18496
_________________________________________________________________
dropout_2 (Dropout) (None, 16, 16, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 4096) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 4096) 0
_________________________________________________________________
dense_1 (Dense) (None, 1024) 4195328
_________________________________________________________________
dropout_4 (Dropout) (None, 1024) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 10250
=================================================================
Total params: 4,224,970
Trainable params: 4,224,970
Non-trainable params: 0
_________________________________________________________________
None
进行训练
定义训练方式
model.compile(loss='categorical_crossentropy',#设置损失函数
optimizer='adam',metrics=['accuracy'])#设置优化器和定义评估模型方式
train_history=model.fit(x_train_normalize,y_train_onehot,
validation_split=0.2,
epochs=10,batch_size=128,verbose=2)#训练周期为10，每次训练128项数据
Train on 40000 samples, validate on 10000 samples
Epoch 1/10
- 310s - loss: 1.5489 - acc: 0.4436 - val_loss: 1.3615 - val_acc: 0.5575
Epoch 2/10
- 294s - loss: 1.1747 - acc: 0.5818 - val_loss: 1.1727 - val_acc: 0.6203
Epoch 3/10
- 277s - loss: 1.0407 - acc: 0.6336 - val_loss: 1.0580 - val_acc: 0.6687
Epoch 4/10
- 257s - loss: 0.9329 - acc: 0.6701 - val_loss: 0.9902 - val_acc: 0.6756
Epoch 5/10
- 257s - loss: 0.8434 - acc: 0.7012 - val_loss: 0.9386 - val_acc: 0.6915
Epoch 6/10
- 3895s - loss: 0.7594 - acc: 0.7327 - val_loss: 0.8906 - val_acc: 0.7139
Epoch 7/10
- 338s - loss: 0.6715 - acc: 0.7650 - val_loss: 0.8480 - val_acc: 0.7154
Epoch 8/10
- 335s - loss: 0.6106 - acc: 0.7878 - val_loss: 0.8086 - val_acc: 0.7290
Epoch 9/10
- 361s - loss: 0.5374 - acc: 0.8103 - val_loss: 0.7926 - val_acc: 0.7290
Epoch 10/10
- 394s - loss: 0.4856 - acc: 0.8293 - val_loss: 0.7823 - val_acc: 0.7344
可视化准确率和误差
import matplotlib.pyplot as plt
def show_train_history(train_history,train,validation):#输入参数分别为，train_history,
#训练数据的执行结果，验证数据的执行结果
plt.plot(train_history.history[train])
plt.plot(train_history.history[validation])
plt.title('Train History')
plt.ylabel(train)
plt.xlabel('Epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
show_train_history(train_history,'acc','val_acc')
train_history.history
{'acc': [0.443575,
0.581775,
0.63355,
0.670125,
0.7012,
0.732675,
0.764975,
0.787775,
0.810325,
0.82925],
'loss': [1.5489341194152833,
1.1746543694496154,
1.0407079723358155,
0.9328960960388184,
0.8434274671554566,
0.7594253509521485,
0.671516370010376,
0.6106255568504334,
0.5374301322937012,
0.48556572380065915],
'val_acc': [0.5575,
0.6203,
0.6687,
0.6756,
0.6915,
0.7139,
0.7154,
0.729,
0.729,
0.7344],
'val_loss': [1.3614615459442139,
1.1727069728851318,
1.0579507133483887,
0.9902402221679687,
0.9386354804992676,
0.8906289207458497,
0.8480340557098389,
0.8086261804580689,
0.7925947729110718,
0.7822921306610108]}
show_train_history(train_history,'loss','val_loss')
评估模型准确率
score=model.evaluate(x_test_normalize,y_test_onehot)
score[1]
10000/10000 [==============================] - 31s 3ms/step
0.7284
进行预测
prediction=model.predict_classes(x_test_normalize)
查看预测结果
prediction[:10]
array([3, 8, 8, 0, 6, 6, 1, 2, 3, 1], dtype=int64)
查看前十项预测结果
plot_images_labels_prediction(x_test,y_test,prediction,0,10)
查看预测概率
probability=model.predict(x_test_normalize)
probability.shape
(10000, 10)
def show_probability(y,prediction,x,#y为真实值，prediction为预测结果，x为预测的图像
probability,i):#probability为预测概率，i表示开始显示数据的index
print('label:',dict[y[i][0]],
'prediction:',dict[prediction[i]])
plt.figure(figsize=(2,2))
plt.imshow(np.reshape(x_test[i],(32,32,3)))
plt.show()
for j in range(10):
print(dict[j]+'\t\tprobability:%f'%(probability[i][j]))
show_probability(y_test,prediction,x_test,probability,0)#第0项数据的预测结果
label: cat prediction: cat
airplane probability:0.000951
automobile probability:0.001414
bird probability:0.015849
cat probability:0.662395
deer probability:0.006565
dog probability:0.283635
frog probability:0.017295
horse probability:0.003373
ship probability:0.006642
truck probability:0.001881
show_probability(y_test,prediction,x_test,probability,3)#查看第三项数据的预测概率
label: airplane prediction: airplane
airplane probability:0.780524
automobile probability:0.010881
bird probability:0.056743
cat probability:0.000491
deer probability:0.003982
dog probability:0.000053
frog probability:0.000369
horse probability:0.000228
ship probability:0.145283
truck probability:0.001446
显示混淆矩阵
建立混淆矩阵
prediction
array([3, 8, 8, ..., 5, 1, 7], dtype=int64)
prediction.shape
(10000,)
y_test
array([[3],
[8],
[8],
...,
[5],
[1],
[7]])
y_test.reshape(-1)#将y_test变为一维数组
array([3, 8, 8, ..., 5, 1, 7])
import pandas as pd
print(dict)
pd.crosstab(y_test.reshape(-1),prediction,
rownames=['label'],colnames=['prediction'])
{0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog', 6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck'}
| prediction | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|
| label | ||||||||||
| 0 | 788 | 17 | 52 | 6 | 9 | 6 | 22 | 11 | 55 | 34 |
| 1 | 6 | 818 | 14 | 6 | 3 | 4 | 17 | 4 | 26 | 102 |
| 2 | 58 | 3 | 627 | 24 | 111 | 54 | 78 | 24 | 13 | 8 |
| 3 | 24 | 9 | 83 | 416 | 94 | 182 | 136 | 26 | 12 | 18 |
| 4 | 18 | 2 | 65 | 22 | 719 | 25 | 80 | 57 | 10 | 2 |
| 5 | 16 | 2 | 45 | 118 | 68 | 622 | 64 | 46 | 7 | 12 |
| 6 | 5 | 5 | 38 | 21 | 25 | 15 | 879 | 4 | 5 | 3 |
| 7 | 15 | 1 | 44 | 21 | 65 | 46 | 18 | 773 | 4 | 13 |
| 8 | 51 | 32 | 24 | 7 | 10 | 5 | 8 | 2 | 831 | 30 |
| 9 | 32 | 62 | 18 | 6 | 2 | 8 | 14 | 12 | 35 | 811 |
结论
- 蛙类最不容易混淆，猫类最容易混淆
- 狗很容易被认为是猫，应为真实值为5预测为3有118次
- 由上图同理可以看出猫也十分容易被预测成狗
- 2，3，4，5，6预测为1的数量都十分少
最后反思，本实验的准确率不高，因为模型比较简单，初衷是为了迅速看到实验的OUTPUT,加上我的处理器很垃圾，运行时间开销很大，条件允许的话，解决方法：可以增加卷积层，在Flatten层中建立多个隐藏层，并提高神经元的个数
