Keras卷积神经网络识别手写体
卷积神经网络识别手写体
- *进行数据预处理*
- *导入模块*
- *读取MNIST数据*
- *将图像特征值转化为6000,28,28,1的4维矩阵*
- *进行标准化*
- *label进行一位有效编码转换*
- *建立模型*
- *建立卷积层1*
- *建立池化层1*
- *建立卷积层2*
- *建立池化层2，加入Dropout避免Overfitting*
- *建立平坦层*
- *建立隐藏层*
- *进行训练*
- *定义训练方式*
- *开始训练*
- *可视化*
- *评估模型的准确率*
- *进行预测*
- *显示前十项预测结果*
- *建立混淆矩阵*
后期文章陆续登在公众号
使用之前还是先建立了一个单独的虚拟环境，具体方法见上一篇文章，代码地址上传至GITHUB上面了https://github.com/fz861062923/Keras
进行数据预处理
导入模块
from keras.utils import np_utils
import numpy as np
np.random.seed(10)
读取MNIST数据
path =r'.\mnist.npz'
f = np.load(path)
x_train, y_train = f['x_train'], f['y_train']
x_test, y_test = f['x_test'], f['y_test']
f.close()
将图像特征值转化为6000,28,28,1的4维矩阵
x_train4D=x_train.reshape(60000,28,28,1).astype('float32')
x_test4D=x_test.reshape(x_test.shape[0],28,28,1).astype('float32')
进行标准化
x_train4D_normalize=x_train4D / 255
x_test4D_normalize=x_test4D / 255
label进行一位有效编码转换
y_trainonehot=np_utils.to_categorical(y_train)
y_testonehot=np_utils.to_categorical(y_test)
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
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.
model=Sequential()#数字图像大小为28*28
建立模型
建立卷积层1
model.add(Conv2D(filters=16,#建立16个滤镜
kernel_size=(5,5),#每一个Filter大小5*5
padding='same',#Convolution完成后的图像大小不变
input_shape=(28,28,1),#输入的图像形状为28*28,1代表单色灰度，3代表RGB
activation='relu'))#产生16个图像，大小仍然是28*28
建立池化层1
model.add(MaxPooling2D(pool_size=(2,2)))#缩减采样，输出16个14*14的图像
建立卷积层2
model.add(Conv2D(filters=36,#建立16个滤镜
kernel_size=(5,5),#每一个Filter大小5*5
padding='same',#Convolution完成后的图像大小不变
input_shape=(28,28,1),#输入的图像形状为28*28,1代表单色灰度，3代表RGB
activation='relu'))#输出36个14*14的图像
建立池化层2，加入Dropout避免Overfitting
model.add(MaxPooling2D(pool_size=(2,2)))#图像大小变为7*7
model.add(Dropout(0.25))#Dropout设置为25%
建立平坦层
model.add(Flatten())#长度是36*7*7个神经元
建立隐藏层
model.add(Dense(128,activation='relu'))#隐藏层设置128个神经元
model.add(Dropout(0.5))
model.add(Dense(10,activation='softmax'))#
print(model.summary())
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 28, 28, 16) 416
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 14, 36) 14436
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 36) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 7, 7, 36) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1764) 0
_________________________________________________________________
dense_1 (Dense) (None, 128) 225920
_________________________________________________________________
dropout_2 (Dropout) (None, 128) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 1290
=================================================================
Total params: 242,062
Trainable params: 242,062
Non-trainable params: 0
_________________________________________________________________
None
进行训练
定义训练方式
使用compile方法
model.compile(loss='categorical_crossentropy',#设置损失函数
optimizer='adam',#使用adam优化器可以让训练更快收敛，并提高准确率
metrics=['accuracy'])#设置评估模型的方式是准确率
开始训练
train_history=model.fit(x=x_train4D_normalize,
y=y_trainonehot,validation_split=0.2,#将80%作为训练数据，20%作为测试数据
epochs=10,#执行10个训练周期
batch_size=300,#每一批300项数据
verbose=2)#参数为2表示显示训练过程
Train on 48000 samples, validate on 12000 samples
Epoch 1/10
- 140s - loss: 0.4887 - acc: 0.8475 - val_loss: 0.0964 - val_acc: 0.9703
Epoch 2/10
- 137s - loss: 0.1364 - acc: 0.9584 - val_loss: 0.0632 - val_acc: 0.9805
Epoch 3/10
- 126s - loss: 0.0998 - acc: 0.9704 - val_loss: 0.0514 - val_acc: 0.9833
Epoch 4/10
- 142s - loss: 0.0815 - acc: 0.9747 - val_loss: 0.0479 - val_acc: 0.9854
Epoch 5/10
- 126s - loss: 0.0685 - acc: 0.9787 - val_loss: 0.0413 - val_acc: 0.9878
Epoch 6/10
- 134s - loss: 0.0607 - acc: 0.9812 - val_loss: 0.0371 - val_acc: 0.9896
Epoch 7/10
- 127s - loss: 0.0543 - acc: 0.9834 - val_loss: 0.0357 - val_acc: 0.9898
Epoch 8/10
- 132s - loss: 0.0472 - acc: 0.9856 - val_loss: 0.0341 - val_acc: 0.9899
Epoch 9/10
- 140s - loss: 0.0420 - acc: 0.9869 - val_loss: 0.0320 - val_acc: 0.9911
Epoch 10/10
- 128s - loss: 0.0410 - acc: 0.9874 - val_loss: 0.0310 - val_acc: 0.9911
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()
可视化
show_train_history(train_history,'acc','val_acc')
train_history.history
{'acc': [0.847479166649282,
0.9583541594445706,
0.9703750066459179,
0.9747083440423012,
0.9787083476781845,
0.9811875142157078,
0.9833958476781846,
0.9856041796505451,
0.9869375124573707,
0.9873750120401382],
'loss': [0.488669916568324,
0.13636313006281853,
0.0997816712828353,
0.08148124186554924,
0.06846866491250694,
0.06066784526919946,
0.0542930141906254,
0.04723233079421334,
0.04203242723015137,
0.041007524722954256],
'val_acc': [0.970250004529953,
0.9805000111460686,
0.983333344757557,
0.9854166775941848,
0.9878333434462547,
0.9895833417773247,
0.9898333415389061,
0.9899166747927666,
0.9910833418369294,
0.9910833418369294],
'val_loss': [0.09635946517810226,
0.063223061861936,
0.05139453299343586,
0.04792754085501656,
0.041311824205331506,
0.0371223745518364,
0.03566586235538125,
0.0341158717405051,
0.031965660926653074,
0.030979079770622774]}
show_train_history(train_history,'loss','val_loss')
评估模型的准确率
scores=model.evaluate(x_test4D_normalize,y_testonehot)
str(scores[1]*100)+'%'
10000/10000 [==============================] - 11s 1ms/step
'99.16%'
进行预测
prediction=model.predict_classes(x_test4D_normalize)
prediction[:10]
array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9], dtype=int64)
显示前十项预测结果
def plot_images_labels_prediction(images,labels,
prediction,idx,num=10):#num为要显示的数据项数默认为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)#子图形为5行5列
ax.imshow(images[idx],cmap='binary')
title= "label=" +str(labels[idx])
if len(prediction)>0:
title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()
plot_images_labels_prediction(x_test,y_test,prediction,idx=0)
建立混淆矩阵
import pandas as pd
pd.crosstab(y_test,prediction,
rownames=["label"],colnames=['prediction'])
| prediction | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|---|
| label | ||||||||||
| 0 | 975 | 1 | 0 | 0 | 0 | 0 | 3 | 1 | 0 | 0 |
| 1 | 0 | 1133 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2 | 1 | 0 | 1028 | 0 | 0 | 0 | 0 | 3 | 0 | 0 |
| 3 | 0 | 0 | 1 | 1000 | 0 | 4 | 0 | 3 | 1 | 1 |
| 4 | 0 | 0 | 0 | 0 | 976 | 0 | 0 | 1 | 0 | 5 |
| 5 | 1 | 0 | 0 | 4 | 0 | 885 | 1 | 0 | 0 | 1 |
| 6 | 2 | 2 | 0 | 0 | 1 | 4 | 949 | 0 | 0 | 0 |
| 7 | 1 | 3 | 1 | 0 | 0 | 0 | 0 | 1019 | 1 | 3 |
| 8 | 1 | 0 | 2 | 3 | 0 | 3 | 1 | 3 | 957 | 4 |
| 9 | 1 | 2 | 0 | 1 | 4 | 2 | 0 | 4 | 1 | 994 |