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