基于Keras的MNIST手写识别

Keras是基于Theano和Tensorflow的深度学习框架，参考文档。

Keras不但提供了经常使用的Layers、Normalization、Regularation、Activation等算法，甚至还包括了几个经常使用的数据库例如cifar-10和mnist等等。

下面的代码算是Keras的Helloworld入门例程，利用MLP实现的MNIST手写数字识别：

from keras.models import Sequential  
from keras.layers.core import Dense, Dropout, Activation  
from keras.optimizers import SGD  
from keras.datasets import mnist  
import numpy

model = Sequential()  
model.add(Dense(784, 500, init='glorot_uniform')) # 输入层，28*28=784  
model.add(Activation('tanh')) # 激活函数是tanh  
model.add(Dropout(0.5)) # 采用50%的dropout

model.add(Dense(500, 500, init='glorot_uniform')) # 隐层节点500个  
model.add(Activation('tanh'))  
model.add(Dropout(0.5))

model.add(Dense(500, 10, init='glorot_uniform')) # 输出结果是10个类别，因此维度是10  
model.add(Activation('softmax')) # 最后一层用softmax

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) # 设定学习率（lr）等参数  
model.compile(loss='categorical_crossentropy', optimizer=sgd, class_mode='categorical') # 使用交叉熵做为loss函数

(X_train, y_train), (X_test, y_test) = mnist.load_data() # 使用Keras自带的mnist工具读取数据（第一次须要联网）

X_train = X_train.reshape(X_train.shape[0], X_train.shape[1] * X_train.shape[2]) # 因为mist的输入数据维度是(num, 28, 28)，这里须要把后面的维度直接拼起来变成784维  
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1] * X_test.shape[2])  
Y_train = (numpy.arange(10) == y_train[:, None]).astype(int) # 参考上一篇文章，这里须要把index转换成一个one hot的矩阵  
Y_test = (numpy.arange(10) == y_test[:, None]).astype(int)

# 开始训练，这里参数比较多。batch_size就是batch_size，nb_epoch就是最多迭代的次数， shuffle就是是否把数据随机打乱以后再进行训练
# verbose是屏显模式，官方这么说的：verbose: 0 for no logging to stdout, 1 for progress bar logging, 2 for one log line per epoch.
# 就是说0是不屏显，1是显示一个进度条，2是每一个epoch都显示一行数据
# show_accuracy就是显示每次迭代后的正确率
# validation_split就是拿出百分之多少用来作交叉验证
model.fit(X_train, Y_train, batch_size=200, nb_epoch=100, shuffle=True, verbose=1, show_accuracy=True, validation_split=0.3)  
print 'test set'  
model.evaluate(X_test, Y_test, batch_size=200, show_accuracy=True, verbose=1)

输出结果：

ssh://shibotian@***.***.***.***:22/usr/bin/python -u /usr/local/shared_dir/local/ipython_shibotian/shibotian/code/kreas_test1/run.py  
Using gpu device 0: Tesla K40m  
Train on 42000 samples, validate on 18000 samples  
Epoch 0  
0/42000 [==============================] - 1s - loss: 0.9894 - acc.: 0.7386 - val. loss: 0.4795 - val. acc.: 0.8807  
Epoch 1  
0/42000 [==============================] - 1s - loss: 0.5635 - acc.: 0.8360 - val. loss: 0.4084 - val. acc.: 0.8889

省略。。。。。

Epoch 98  
0/42000 [==============================] - 1s - loss: 0.2838 - acc.: 0.9116 - val. loss: 0.1872 - val. acc.: 0.9418  
Epoch 99  
0/42000 [==============================] - 1s - loss: 0.2740 - acc.: 0.9163 - val. loss: 0.1842 - val. acc.: 0.9434  
test set  
0/10000 [==============================] - 0s - loss: 0.1712 - acc.: 0.9480     

Process finished with exit code 0