keras02 - hello convolution neural network 搭建第一个卷积神经网络

本项目参考：

https://www.bilibili.com/video/av31500120?t=4657

训练代码

 # coding: utf-8
 # Learning from Mofan and Mike G
 # Recreated by Paprikatree
 # Convolution NN Train

 import numpy as np
 from keras.datasets import mnist
 from keras.utils import np_utils
 from keras.models import Sequential
 from keras.layers import Convolution2D, Activation, MaxPool2D, Flatten, Dense
 from keras.optimizers import Adam
 from keras.models import load_model

 nb_class = 10
 nb_epoch = 4
 batchsize = 128

 '''
 1st,准备参数
 X_train: (0,255) --> (0,1) CNN中似乎没有必要？cnn自动转了吗？
 设置时间函数测试一下两者对比。
 小技巧：X_train /= 255.0 就可不用转换成浮点了？？？
 '''
 # Preparing your data mnist.  MAC /.keras/datasets  linux home ./keras/datasets
 (X_train, Y_train), (X_test, Y_test) = mnist.load_data()

 # setup data shape
 # (-1, 28, 28, 1) -1表示有默认个数据集，28*28是像素，1是1个通道
 X_train = X_train.reshape(-1, 28, 28, 1)  # tensorflow-channel last,while theano-channel first
 X_test = X_test.reshape(-1, 28, 28, 1)

 X_train = X_train/255.000
 X_test = X_test/255.000

 # One-hot 6 --> [0,0,0,0,0,1,0,0,0]
 Y_train = np_utils.to_categorical(Y_train, nb_class)
 Y_test = np_utils.to_categorical(Y_test, nb_class)

 '''
 2nd,设置模型
 '''

 # setup model
 model = Sequential()

 # 1st convolution layer # 滤波器要在28x28的图上横着走32次
 model.add(Convolution2D(
     filters=32,  # 此处把filters写成了filter，找了半天。囧
     kernel_size=[5, 5],  # 滤波器是5x5大小的，可以是list列表，也可以是tuple元祖
     padding='same',  # padding也是一个窗口模式
     input_shape=(28, 28, 1)  # 定义输入的数据，必须是元组
 ))
 model.add(Activation('relu'))
 model.add(MaxPool2D(
     pool_size=(2, 2),  # 按照规则抓取特征,此处为在pool_size的2*2窗口下，strides = 2*2 跳两格再抓取。如 1 2 3 4 5 6...27 28 抓取1 2 ，跳过 3 4 抓取 5 6。
     strides=(2, 2),  # 相当于把图片缩小了。
     padding="same",
 ))

 # 2nd Conv2D layer
 model.add(Convolution2D(
     filters=64,
     kernel_size=(5, 5),
     padding='same',
 ))
 model.add(Activation('relu'))
 model.add(MaxPool2D(
     pool_size=(2, 2),  # 按照规则抓取特征,此处为在pool_size的2*2窗口下，strides = 2*2 跳两格再抓取。如 1 2 3 4 5 6...27 28 抓取1 2 ，跳过 3 4 抓取 5 6。
     strides=(2, 2),  # 相当于把图片缩小了。
     padding="same",
 ))  # 讨论，卷积层数和最终结果关系。

 # 1st Fully connected Dense，Dense 全连接层是hello world里面的内容
 model.add(Flatten())  # 把卷积层里面的全部转换层一维数组
 model.add(Dense(1024))  # Dense is output
 model.add(Activation('relu'))

 # 1st Fully connected Dense，Dense 全连接层是hello world里面的内容
 # 把卷积层里面的全部转换层一维数组
 model.add(Dense(256))  # Dense is output
 model.add(Activation('tanh'))

 # 2nd Fully connected Dense
 model.add(Dense(10))
 model.add(Activation('softmax'))

 '''
 3rd 定义参数
 '''
 # Define Optimizer and setup Param
 adam = Adam(lr=0.0001)  # Adam实例化

 # compile model
 model.compile(
     optimizer=adam,  # optimizer='Adam'也是可以的，且默认lr=0.001，此处已经实例化为adam
     loss='categorical_crossentropy',
     metrics=['accuracy'],
 )

 # Run network
 model.fit(x=X_train,  # 更多参数可以查看fit函数，alt+鼠标左键单击fit
           y=Y_train,
           epochs=nb_epoch,
           batch_size=batchsize,  # p=parameter, batch_size; v=var, batch size
           verbose=1,  # 显示模式
           validation_data=(X_test, Y_test)
           )
 model.save('model_name.h5')
 # evaluation = model.evaluate(X_test, Y_test)  现在用model.fit(validation_data)
 # print(evaluation)  效果一样

测试代码：

 # coding: utf-8
 # Learning from Mofan and Mike G
 # Recreated by Paprikatree
 # Convolution NN Predict

 import numpy as np
 from keras.models import load_model  # ??
 import matplotlib.pyplot as plt
 import matplotlib.image as processimage

 # load trained model
 model = load_model('model_name.h5')  # 已经训练好了的模型，在根目录下，默认为model_name.h5

 # 写一个来预测的类
 class MainPredictImg(object):

     def __init__(self):
         pass

     def pred(self, filename):
         pred_img = processimage.imread(filename)
         pred_img = np.array(pred_img)
         pred_img = pred_img.reshape(-1, 28, 28, 1)
         prediction = model.predict(pred_img)
         final_prediction = [result.argmax() for result in prediction][0]
         a = 0
         for i in prediction[0]:
             print(a)
             print('Percent:{:.30%}'.format(i))
             a = a+1
         return final_prediction

 def main():
     predict = MainPredictImg()
     res = predict.pred('4.png')
     print("your number is:-->", res)

 if __name__ == '__main__':
     main()