【深度学习】CNN入门——代码

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一、读取数据

使用Python、Keras

# 头文件
from keras.datasets import mnist
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Convolution2D, Dense, Flatten, Activation, MaxPooling2D
from keras.utils import np_utils
from keras.optimizers import Adam
import numpy as np
from keras.models import load_model
import numpy as np
from PIL import Image

# 读取数据并查看，数据集下载地址https://d.ailemon.me/mnist.npz
# 也可以使用 (x_train, y_train), (x_test, y_test) = mnist.load_data()会自动在线下载
(x_train, y_train), (x_test, y_test) = mnist.load_data("C:/Users/Li Yanbin/Desktop/StudyNN/mnist.npz")
plt.subplot(121)
plt.imshow(x_train[0], cmap=plt.get_cmap('gray'))
plt.subplot(122)
plt.imshow(x_train[1], cmap=plt.get_cmap('gray'))
# show the plot
plt.show()

二、数据预处理

# 数据预处理
x_train = x_train.reshape(60000, 28, 28, 1)
x_test = x_test.reshape(10000, 28, 28, 1)
# 标签onehot编码
y_test = np_utils.to_categorical(y_test, 10)
y_train = np_utils.to_categorical(y_train, 10)

三、建立模型并训练

# design model
model = Sequential()
model.add(Convolution2D(25, (5, 5), input_shape=(28, 28, 1)))
model.add(MaxPooling2D(2, 2))
model.add(Activation('relu'))
model.add(Convolution2D(50, (5, 5)))
model.add(MaxPooling2D(2, 2))
model.add(Activation('relu'))
model.add(Flatten())

model.add(Dense(50))
model.add(Activation('relu'))
model.add(Dense(10))
model.add(Activation('softmax'))
adam = Adam(lr=0.001)

# compile model
model.compile(optimizer=adam,loss='categorical_crossentropy',metrics=['accuracy'])

# training model
model.fit(x_train, y_train, batch_size=100, epochs=5)

# test model
print(model.evaluate(x_test, y_test, batch_size=100))

四、保存模型

注意保存模型和读取模型，用读取的模型训练数据

# save model
model.save('./model.h5')

五、查看效果

# 读取图片
def ImageToMatrix(filename):
    im = Image.open(filename)
    #change to greyimage
    im=im.convert("L")
    data = im.getdata()
    data = np.matrix(data,dtype='int')
    return data

# 读取模型
model = load_model('./model.h5')

# 读取数据并处理
data=ImageToMatrix('./5.png')
data = np.array(data)
data = data.reshape(1, 28, 28, 1)

# 预测并查看结果
result=model.predict_classes(data,batch_size=1,verbose=0)
print(result)

测试图片：

测试结果：