本文用 Convolutional Neural Network（CNN）进行MNIST识别，CNN由Yann LeCun 提出，与MLP的区别在于，前面用卷积进行了特征提取，后面全连接层和Multilayer Perceptron（MLP）一样，MLP的demo可以查看这篇博客【Keras-MLP】MNIST，本文的套路都是基于这篇博客的
1 Data preprocessing
from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
np.random.seed(10)
(x_Train, y_Train), (x_Test, y_Test) = mnist.load_data()#载入数据集，没有的话会下载
x_Train4D=x_Train.reshape(x_Train.shape[0],28,28,1).astype('float32')# 60000，28，28，1
x_Test4D=x_Test.reshape(x_Test.shape[0],28,28,1).astype('float32')#10000，28，28，1
# 归一化
x_Train4D_normalize = x_Train4D / 255
x_Test4D_normalize = x_Test4D / 255
# one-hot Encoding
y_TrainOneHot = np_utils.to_categorical(y_Train)
y_TestOneHot = np_utils.to_categorical(y_Test)Output
Using TensorFlow backend.归一化（标准化）的目的，提高模型预测的准确率，并且收敛更快
2 Build Model
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
# 蛋糕架子
model = Sequential()
# 加蛋糕
# 卷积层1，输入28，28 输出 28，28
model.add(Conv2D(filters=16,
kernel_size=(5,5),
padding='same',
input_shape=(28,28,1),
activation='relu'))
# 池化层1，输入图片28，28 输出14，14
model.add(MaxPooling2D(pool_size=(2, 2)))
# 卷积层2，输入图片14，14 输出 14，14
model.add(Conv2D(filters=36,
kernel_size=(5,5),
padding='same',
activation='relu'))
# 池化层2，输入图片14，14 输出7，7
model.add(MaxPooling2D(pool_size=(2, 2)))
# 加入dropout，丢25%
model.add(Dropout(0.25))
# 36*7*7 转化为一维向量，1764个float
model.add(Flatten())
# 全连接，128
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
# output layer
model.add(Dense(10,activation='softmax'))
print(model.summary())Output
_________________________________________________________________
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
_________________________________________________________________
NoneMax-pooling：对图片缩减采样
好处
- 减少所需处理的数据点：减少后续运算所需要的时间
- 让图像位置差异变小
- 参数的数量和计算量下降：缓解过拟合
参数量计算
28*28*1 的图，5*5滤波后变成 28*28*16
1*5*5*16 + 16 = 416（最后的+16是偏置）
14*14*16 的图，5*5滤波后变成 14*14*36
16*5*5*36+36 = 14436
1764 一维向量全连接到128
1764 * 128 + 128 = 225920
128 一维向量全连接到10
128 * 10 + 10 = 1290
3 Training process
model.compile(loss='categorical_crossentropy',
optimizer='adam',metrics=['accuracy'])
train_history=model.fit(x=x_Train4D_normalize,
y=y_TrainOneHot,validation_split=0.2,
epochs=20, batch_size=300,verbose=2)同 MLP 的训练，【Keras-MLP】MNIST， epoch改成了20，batch_size改成了300
Output
Train on 48000 samples, validate on 12000 samples
Epoch 1/20
- 10s - loss: 0.4908 - acc: 0.8472 - val_loss: 0.0956 - val_acc: 0.9728
Epoch 2/20
- 2s - loss: 0.1398 - acc: 0.9592 - val_loss: 0.0626 - val_acc: 0.9809
Epoch 3/20
- 2s - loss: 0.1017 - acc: 0.9694 - val_loss: 0.0513 - val_acc: 0.9840
Epoch 4/20
- 2s - loss: 0.0828 - acc: 0.9761 - val_loss: 0.0457 - val_acc: 0.9861
Epoch 5/20
- 2s - loss: 0.0708 - acc: 0.9774 - val_loss: 0.0394 - val_acc: 0.9878
Epoch 6/20
- 2s - loss: 0.0634 - acc: 0.9811 - val_loss: 0.0396 - val_acc: 0.9886
Epoch 7/20
- 2s - loss: 0.0558 - acc: 0.9829 - val_loss: 0.0452 - val_acc: 0.9865
Epoch 8/20
- 2s - loss: 0.0509 - acc: 0.9842 - val_loss: 0.0338 - val_acc: 0.9901
Epoch 9/20
- 2s - loss: 0.0443 - acc: 0.9867 - val_loss: 0.0336 - val_acc: 0.9900
Epoch 10/20
- 2s - loss: 0.0419 - acc: 0.9872 - val_loss: 0.0349 - val_acc: 0.9898
Epoch 11/20
- 2s - loss: 0.0414 - acc: 0.9877 - val_loss: 0.0328 - val_acc: 0.9908
Epoch 12/20
- 2s - loss: 0.0374 - acc: 0.9877 - val_loss: 0.0285 - val_acc: 0.9912
Epoch 13/20
- 2s - loss: 0.0332 - acc: 0.9902 - val_loss: 0.0288 - val_acc: 0.9915
Epoch 14/20
- 2s - loss: 0.0306 - acc: 0.9906 - val_loss: 0.0273 - val_acc: 0.9920
Epoch 15/20
- 2s - loss: 0.0302 - acc: 0.9899 - val_loss: 0.0277 - val_acc: 0.9924
Epoch 16/20
- 2s - loss: 0.0284 - acc: 0.9905 - val_loss: 0.0274 - val_acc: 0.9915
Epoch 17/20
- 2s - loss: 0.0262 - acc: 0.9919 - val_loss: 0.0293 - val_acc: 0.9920
Epoch 18/20
- 2s - loss: 0.0254 - acc: 0.9915 - val_loss: 0.0295 - val_acc: 0.9923
Epoch 19/20
- 2s - loss: 0.0255 - acc: 0.9917 - val_loss: 0.0269 - val_acc: 0.9933
Epoch 20/20
- 2s - loss: 0.0224 - acc: 0.9927 - val_loss: 0.0260 - val_acc: 0.99334 可视化训练过程
import matplotlib.pyplot as plt
def show_train_history(train_acc,test_acc):
plt.plot(train_history.history[train_acc])
plt.plot(train_history.history[test_acc])
plt.title('Train History')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.savefig('1.png')
plt.show()调用准确率
show_train_history('acc','val_acc')Output
调用损失
show_train_history('loss','val_loss')Output
可以看到，训练集和验证集的结果很接近，过拟合现象大大减缓，相对于MLP
5 评估模型的precision
scores = model.evaluate(x_Test4D_normalize , y_TestOneHot)
scores[1]Output
10000/10000 [==============================] - 1s 116us/step
0.99356 Predict
预测前10项的结果
prediction=model.predict_classes(x_Test4D_normalize)
prediction[:10]Output
array([7, 2, 1, 0, 4, 1, 4, 9, 5, 9])7 可视化预测结果
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')
ax.set_title("label=" +str(labels[idx])+
",predict="+str(prediction[idx])
,fontsize=10)
ax.set_xticks([]);ax.set_yticks([])
idx+=1
plt.show()调用
plot_images_labels_prediction(x_Test,y_Test,prediction,idx=0)Output
8 Confusion Matrix
import pandas as pd
a = pd.crosstab(y_Test,prediction,rownames=['label'],colnames=['predict'])
print(a)Output
predict 0 1 2 3 4 5 6 7 8 9
label
0 976 0 0 0 0 0 1 0 2 1
1 0 1132 1 0 0 0 0 1 1 0
2 1 0 1029 0 0 0 0 2 0 0
3 0 0 0 1002 0 5 0 0 2 1
4 0 0 0 0 977 0 1 0 2 2
5 0 0 0 3 0 888 1 0 0 0
6 4 2 0 0 2 1 949 0 0 0
7 0 2 4 0 0 0 0 1017 1 4
8 1 0 1 2 1 1 0 1 964 3
9 0 0 0 0 4 2 0 2 0 1001不打印的话会画出来漂亮一些的表格，哈哈
显示一下，把5错分为3的情况
df = pd.DataFrame({'label':y_Test, 'predict':prediction})
b = df[(df.label==5)&(df.predict==3)]
print(b)Output
label predict
1393 5 3
2369 5 3
2597 5 3第一列是图片id，从0开始编号
声明
声明：代码源于《TensorFlow+Keras深度学习人工智能实践应用》 林大贵版，引用、转载请注明出处，谢谢，如果对书本感兴趣，买一本看看吧！！！