训练代码

  1 # coding: utf-8
  2 # Learning from Mofan and Mike G
  3 # Recreated by Paprikatree
  4 # Convolution NN Train
  5 
  6 import numpy as np
  7 from keras.datasets import mnist
  8 from keras.utils import np_utils
  9 from keras.models import Sequential
 10 from keras.layers import Convolution2D, Activation, MaxPool2D, Flatten, Dense
 11 from keras.optimizers import Adam
 12 from keras.models import load_model
 13 
 14 
 15 nb_class = 10
 16 nb_epoch = 4
 17 batchsize = 128
 18 
 19 '''
 20 1st,准备参数
 21 X_train: (0,255) --> (0,1) CNN中似乎没有必要？cnn自动转了吗？
 22 设置时间函数测试一下两者对比。
 23 小技巧：X_train /= 255.0 就可不用转换成浮点了？？？
 24 '''
 25 # Preparing your data mnist.  MAC /.keras/datasets  linux home ./keras/datasets
 26 (X_train, Y_train), (X_test, Y_test) = mnist.load_data()
 27 
 28 
 29 # setup data shape
 30 # (-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
 31 X_train = X_train.reshape(-1, 28, 28, 1)  # tensorflow-channel last,while theano-channel first
 32 X_test = X_test.reshape(-1, 28, 28, 1)
 33 
 34 X_train = X_train/255.000
 35 X_test = X_test/255.000
 36 
 37 # One-hot 6 --> [0,0,0,0,0,1,0,0,0]
 38 Y_train = np_utils.to_categorical(Y_train, nb_class)
 39 Y_test = np_utils.to_categorical(Y_test, nb_class)
 40 
 41 '''
 42 2nd,设置模型
 43 '''
 44 
 45 # setup model
 46 model = Sequential()
 47 
 48 # 1st convolution layer # 滤波器要在28x28的图上横着走32次
 49 model.add(Convolution2D(
 50     filters=32,  # 此处把filters写成了filter，找了半天。囧
 51     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
 52     padding='same',  # padding也是一个窗口模式
 53     input_shape=(28, 28, 1)  # 定义输入的数据，必须是元组
 54 ))
 55 model.add(Activation('relu'))
 56 model.add(MaxPool2D(
 57     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。
 58     strides=(2, 2),  # 相当于把图片缩小了。
 59     padding="same",
 60 ))
 61 
 62 # 2nd Conv2D layer
 63 model.add(Convolution2D(
 64     filters=64,
 65     kernel_size=(5, 5),
 66     padding='same',
 67 ))
 68 model.add(Activation('relu'))
 69 model.add(MaxPool2D(
 70     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。
 71     strides=(2, 2),  # 相当于把图片缩小了。
 72     padding="same",
 73 ))  # 讨论，卷积层数和最终结果关系。
 74 
 75 # 1st Fully connected Dense，Dense 全连接层是hello world里面的内容
 76 model.add(Flatten())  # 把卷积层里面的全部转换层一维数组
 77 model.add(Dense(1024))  # Dense is output
 78 model.add(Activation('relu'))
 79 
 80 # 1st Fully connected Dense，Dense 全连接层是hello world里面的内容
 81 # 把卷积层里面的全部转换层一维数组
 82 model.add(Dense(256))  # Dense is output
 83 model.add(Activation('tanh'))
 84 
 85 # 2nd Fully connected Dense
 86 model.add(Dense(10))
 87 model.add(Activation('softmax'))
 88 
 89 '''
 90 3rd 定义参数
 91 '''
 92 # Define Optimizer and setup Param
 93 adam = Adam(lr=0.0001)  # Adam实例化
 94 
 95 # compile model
 96 model.compile(
 97     optimizer=adam,  # optimizer='Adam'也是可以的，且默认lr=0.001，此处已经实例化为adam
 98     loss='categorical_crossentropy',
 99     metrics=['accuracy'],
100 )
101 
102 # Run network
103 model.fit(x=X_train,  # 更多参数可以查看fit函数，alt+鼠标左键单击fit
104           y=Y_train,
105           epochs=nb_epoch,
106           batch_size=batchsize,  # p=parameter, batch_size; v=var, batch size
107           verbose=1,  # 显示模式
108           validation_data=(X_test, Y_test)
109           )
110 model.save('model_name.h5')
111 # evaluation = model.evaluate(X_test, Y_test)  现在用model.fit(validation_data)
112 # print(evaluation)  效果一样

测试代码：

 1 # coding: utf-8
 2 # Learning from Mofan and Mike G
 3 # Recreated by Paprikatree
 4 # Convolution NN Predict
 5 
 6 import numpy as np
 7 from keras.models import load_model  # ??
 8 import matplotlib.pyplot as plt
 9 import matplotlib.image as processimage
10 
11 
12 # load trained model
13 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
14 
15 
16 # 写一个来预测的类
17 class MainPredictImg(object):
18     
19     def __init__(self):
20         pass
21     
22     def pred(self, filename):
23         pred_img = processimage.imread(filename)
24         pred_img = np.array(pred_img)
25         pred_img = pred_img.reshape(-1, 28, 28, 1)
26         prediction = model.predict(pred_img)
27         final_prediction = [result.argmax() for result in prediction][0]
28         a = 0
29         for i in prediction[0]:
30             print(a)
31             print('Percent:{:.30%}'.format(i))
32             a = a+1
33         return final_prediction
34 
35 
36 def main():
37     predict = MainPredictImg()
38     res = predict.pred('4.png')
39     print("your number is:-->", res)
40 
41 
42 if __name__ == '__main__':
43     main()

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