keras01 - hello world ~ 搭建第一个神经网络

 import numpy as np
 from keras.datasets import mnist
 from keras.models import Sequential, Model
 from keras.layers.core import Dense, Activation, Dropout
 from keras.utils import np_utils

 import matplotlib.pyplot as plt
 import matplotlib.image as processimage

 # Load mnist RAW dataset
 # 训练集28*28的图片X_train = (60000, 28, 28) 训练集标签Y_train = (60000,1)
 # 测试集图片X_test  = (10000, 28, 28) 测试集标签Y_test  = (10000,1)
 (X_train, Y_train), (X_test, Y_test) = mnist.load_data()
 print(X_train.shape, Y_train.shape)
 print(X_test.shape, Y_test.shape)

 '''
 第一步，准备数据
 '''
 # Prepare 准备数据
 # Reshape 60k个图片，每个28*28的图片，降维成一个784的一维数组
 X_train = X_train.reshape(60000, 784)  # 28*28 = 784
 X_test = X_test.reshape(10000, 784)
 # set type into float32 设置成浮点型，因为使用的是GPU，GPU可以加速运算浮点型
 # CPU使用int型计算会更快
 X_train = X_train.astype('float32')  # astype SET AS TYPE INTO
 X_test = X_test.astype('float32')
 # 归一化颜色
 X_train = X_train/255  # 除以255个颜色，X_train(0, 255)-->(0, 1) 更有利于浮点运算
 X_test = X_test/255

 '''
 第二步，给神经网络设置基本参数
 '''
 # Prepare basic setups
 batch_sizes = 4096  # 一次给神经网络注入多少数据，别超过6万，和GPU内存有关
 nb_class = 10  # 设置多少个分类
 nb_epochs = 10  # 60k数据训练20次，一般小数据10次就够了

 '''
 第三步，设置标签
 '''
 # Class vectors label(7) into [0,0,0,0,0,0,0,1,0,1]  把7设置成向量
 Y_test = np_utils.to_categorical(Y_test, nb_class)  # Label
 Y_train = np_utils.to_categorical(Y_train, nb_class)

 '''
 第四步，设置网络结构
 '''
 model = Sequential()  # 顺序搭建层
 # 1st layer
 model.add(Dense(512, input_shape=(784,)))  # Dense是输出给下一层, input_dim = 784 [X*784]
 model.add(Activation('relu'))  # tanh
 model.add(Dropout(0.2))  # overfitting

 # 2nd layer
 model.add(Dense(256))  # 256是因为上一层已经输出512了，所以不用标注输入
 model.add(Activation('relu'))
 model.add(Dropout(0.2))

 # 3rd layer
 model.add(Dense(10))
 model.add(Activation('softmax'))  # 根据10层输出，softmax做分类

 '''
 第五步，编译compile
 '''
 model.compile(
     loss='categorical_crossentropy',
     optimizer='rmsprop',
     metrics=['accuracy']
 )

 # 启动网络训练 Fire up
 Trainning = model.fit(
     X_train, Y_train,
     batch_size=batch_sizes,
     epochs=nb_epochs,
     validation_data=(X_test, Y_test)
 )
 # 以上就可运行

 '''
 最后，检查工作
 '''
 # Trainning.history  # 检查训练历史
 # Trainning.params  # 检查训练参数

 # 拉取test里的图
 testrun = X_test[9999].reshape(1, 784)

 testlabel = Y_test[9999]
 print('label:-->', testlabel)
 print(testrun.shape)
 plt.imshow(testrun.reshape([28, 28]))

 # 判断输出结果
 pred = model.predict(testrun)
 print(testrun)
 print('label of test same Y_test[9999]-->>', testlabel)
 print('预测结果-->>', pred)
 print([final.argmax() for final in pred])  # 找到pred数组中的最大值

 # 用自己的画的图28*28预测一下 (不太准，可以用卷积)
 # 可以用PS创建28*28像素的图，且是灰度，没有色彩
 target_img = processimage.imread('/.../picture.jpg')
 print(' before reshape:->>', target_img.shape)
 plt.imshow(target_img)
 target_img = target_img.reshape(1, 784)  # reshape
 print(' after reshape:->>', target_img.shape)

 target_img = np.array(target_img)  # img --> numpy array
 target_img = target_img.astype('float32')  # int --> float32
 target_img /= 255  # (0,255) --> (0,1)

 print(target_img)

 mypred = model.predict(target_img)
 print(mypred)
 print(myfinal.argmax() for myfinal in mypred)

参考：https://www.bilibili.com/video/av29806227