基于Theano的深度学习(Deep Learning)框架Keras学习随笔-02-Example

      http://blog.csdn.net/niuwei22007/article/details/49053771原地址可以查看更多文章
      下面这些例子是keras前期版本的，现在已经升级到了keras0.3.0，以下代码需要进行修改才可以。如今的代码更简洁，使用更方便，不需要自己计算每一层的输入shape，除了第一层。

下面来看几个例子，来了解一下Keras的便捷之处。不需要具体去研究代码的意思，只需要看一下这个实现过程。用编程的装饰模式把各个组件模块化，然后可以自己随意的拼装。首先介绍一个基于Keras做的手写MNIST识别的代码，剩下的就看一下实现过程即可。

No.0用Keras实现MNIST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))
 
# 输出结果是10个类别，所以维度是10
model.add(Dense(500, 10, init='glorot_uniform'))
model.add(Activation('softmax')) # 最后一层用softmax
 
# 设定学习率（lr）等参数 
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9,nesterov=True)  
# 使用交叉熵作为loss函数，就是熟知的log损失函数
model.compile(loss='categorical_crossentropy',
optimizer=sgd, class_mode='categorical')
# 使用Keras自带的mnist工具读取数据（第一次需要联网）
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 由于输入数据维度是(num, 28, 28)，这里需要把后面的维度直接拼起来变成784维
X_train = X_train.reshape(X_train.shape[0],
X_train.shape[1]* X_train.shape[2])
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1]* X_test.shape[2]) 
# 这里需要把index转换成一个one hot的矩阵
Y_train = (numpy.arange(10) == y_train[:,None]).astype(int) 
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之后再进行训练
# verbose是屏显模式，官方这么说的：verbose: 0 forno 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做交叉验证
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)

No.1用Keras实现MLP(1)

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
 
model = Sequential()
# Dense(input, output, init=’wegiths initial method’)
model.add(Dense(20, 64, init='uniform'))
model.add(Activation('tanh')) # 激活函数
model.add(Dropout(0.5))     #采用50%的dropout
 
model.add(Dense(64, 64, init='uniform'))
model.add(Activation('tanh'))
model.add(Dropout(0.5))
 
model.add(Dense(64, 2, init='uniform'))
model.add(Activation('softmax'))
 
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)#设定学习速度，衰减量等
model.compile(loss='mean_squared_error', optimizer=sgd) #损失函数为均方误差
 
# ………此处是加载训练数据的的代码。
 
# 开始训练。nb_epoch是迭代次数，batcn_size是数据块大小。
model.fit(X_train, y_train, nb_epoch=20, batch_size=16)
score = model.evaluate(X_test, y_test, batch_size=16)

No.2用Keras实现MLP(2)：比(1)更简洁

model = Sequential()
model.add(Dense(20, 64, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, 64, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(64, 2, init='uniform', activation='softmax'))
 
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)

No.3 VGG-like卷积网络。

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation,Flatten
from keras.layers.convolutional import Convolution2D,MaxPooling2D
from keras.optimizers import SGD
 
model = Sequential()
model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dense(64*8*8, 256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
 
model.add(Dense(256, 10))
model.add(Activation('softmax'))
 
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd)
 
model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)

No.4长短期记忆网络用于序列分类。

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation,Flatten
from keras.layers.convolutional import Convolution2D,MaxPooling2D
from keras.optimizers import SGD
 
model = Sequential()
model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dense(64*8*8, 256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
 
model.add(Dense(256, 10))
model.add(Activation('softmax'))
 
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',optimizer=sgd)
 
model.fit(X_train, Y_train, batch_size=32, nb_epoch=1)

No.5图像字幕识别。

max_caption_len = 16
 
model = Sequential()
model.add(Convolution2D(32, 3, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(32, 32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
 
model.add(Convolution2D(64, 32, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
 
model.add(Convolution2D(128, 64, 3, 3, border_mode='full'))
model.add(Activation('relu'))
model.add(Convolution2D(128, 128, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(poolsize=(2, 2)))
 
model.add(Flatten())
model.add(Dense(128*4*4, 256))
model.add(Activation('relu'))
model.add(Dropout(0.5))
 
model.add(RepeatVector(max_caption_len))
# the GRU below returns sequences of max_caption_lenvectors of size 256 (our word embedding size)
model.add(GRU(256, 256, return_sequences=True))
 
model.compile(loss='mean_squared_error', optimizer='rmsprop')
 
# "images" is a numpy array of shape(nb_samples, nb_channels=3, width, height)
# "captions" is a numpy array of shape(nb_samples, max_caption_len=16, embedding_dim=256)
# captions are supposed already embedded (dense vectors).
model.fit(images, captions, batch_size=16, nb_epoch=100)

参考资料：

1. 官方教程
2. http://ju.outofmemory.cn/entry/188683