dive into keras, some useful code

DeepLearning Tutorials/dive_into_keras/README.md

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+
+[上一篇文章](http://blog.csdn.net/u012162613/article/details/45397033)总结了Keras的基本使用方法，相信用过的同学都会觉得不可思议，太简洁了。十多天前，我在github上发现这个框架的时候，关注Keras的人还比较少，这两天无论是github还是微薄，都看到越来越多的人关注和使用Keras。所以这篇文章就简单地再介绍一下Keras的使用,方便各位入门。
+
+主要包括以下三个内容：
+
+- 训练CNN并保存训练好的模型。
+- 将CNN用于特征提取，用提取出来的特征训练SVM。
+- 可视化CNN卷积层后的特征图。
+
+仍然以Mnist为例，代码中用的Mnist数据到这里下载 
+[http://pan.baidu.com/s/1qCdS6](http://pan.baidu.com/s/1qCdS6),本文的代码在我的github上:[dive_into _keras](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials)
+
+
+----------
+
+
+###1. 加载数据
+
+数据是图片格式，利用pyhton的PIL模块读取，并转为numpy.array类型。这部分的代码在`data.py`里：
+
+
+----------
+
+
+###2. 训练CNN并保存训练好的CNN模型
+
+将上一步加载进来的数据分为训练数据（X_train，30000个样本）和验证数据（X_val，12000个样本），构建CNN模型并训练。训练过程中，每一个epoch得到的val-accuracy都不一样，我们保存达到最好的val-accuracy时的模型，利用Python的cPickle模块保持。（Keras的开发者最近在添加用hdf5保持模型的功能，我试了一下，没用成功，去github发了issue也没人回，估计还没完善，hdf5压缩率会更高，保存下来的文件会更小。）
+
+这部分的代码在`cnn.py`里，运行:
+
+```
+python cnn.py
+```
+
+在第Epoch 4得到96.45%的validation accuracy,运行完后会得到model.pkl这份文件，保存的就是96.45%对应的模型：
+
+![这里写图片描述](http://img.blog.csdn.net/20150508155724085)
+
+
+----------
+
+
+###3.将CNN用于特征提取，用提取出来的特征训练SVM
+
+上一步得到了一个val-accuracy为96.45%的CNN模型，在一些论文中经常会看到用CNN的全连接层的输出作为特征，然后去训练其他分类器。这里我也试了一下，用全连接层的输出作为样本的特征向量，训练SVM。SVM用的是scikit learn里的算法。
+
+这部分代码在`cnn-svm.py`,运行：
+
+```
+python cnn-svm.py
+```
+
+得到下图的输出，可以看到，cnn-svm的准确率提高到97.89%：
+
+![这里写图片描述](http://img.blog.csdn.net/20150508155806689)
+
+
+----------
+
+
+###4.可视化CNN卷积层后的特征图
+
+将卷积层和全连接层后的特征图、特征向量以图片形式展示出来，用到matplotlib这个库。这部分代码在`get_feature_map.py`里。运行：
+
+```
+python get_feature_map.py
+```
+
+得到全连接层的输出，以及第一个卷积层输出的4个特征图：
+
+![全连接层后的输出](http://img.blog.csdn.net/20150508155842678)
+
+![这里写图片描述](http://img.blog.csdn.net/20150508155724909)
+
+![这里写图片描述](http://img.blog.csdn.net/20150508155810914)
+
+![这里写图片描述](http://img.blog.csdn.net/20150508155833190)
+
+![这里写图片描述](http://img.blog.csdn.net/20150508160043578)
+
+
+----------
+转载请注明出处：http://blog.csdn.net/u012162613/article/details/45581421

DeepLearning Tutorials/dive_into_keras/cnn-svm.py

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+'''
+Author:wepon
+Code:https://github.com/wepe
+
+File: cnn-svm.py
+'''
+from __future__ import print_function
+import cPickle
+import theano
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from data import load_data
+
+
+def svc(traindata,trainlabel,testdata,testlabel):
+    print("Start training SVM...")
+    svcClf = SVC(C=1.0,kernel="rbf",cache_size=3000)
+    svcClf.fit(traindata,trainlabel)
+
+    pred_testlabel = svcClf.predict(testdata)
+    num = len(pred_testlabel)
+    accuracy = len([1 for i in range(num) if testlabel[i]==pred_testlabel[i]])/float(num)
+    print("cnn-svm Accuracy:",accuracy)
+
+def rf(traindata,trainlabel,testdata,testlabel):
+    print("Start training Random Forest...")
+    rfClf = RandomForestClassifier(n_estimators=400,criterion='gini')
+    rfClf.fit(traindata,trainlabel)
+
+    pred_testlabel = rfClf.predict(testdata)
+    num = len(pred_testlabel)
+    accuracy = len([1 for i in range(num) if testlabel[i]==pred_testlabel[i]])/float(num)
+    print("cnn-rf Accuracy:",accuracy)
+
+if __name__ == "__main__":
+    #load data,split into traindata and testdata
+    data, label = load_data()
+    (traindata,testdata) = (data[0:30000],data[30000:])
+    (trainlabel,testlabel) = (label[0:30000],label[30000:])
+    #use origin_model to predict testdata
+    origin_model = cPickle.load(open("model.pkl","rb"))
+    pred_testlabel = origin_model.predict_classes(testdata,batch_size=1, verbose=1)
+    num = len(testlabel)
+    accuracy = len([1 for i in range(num) if testlabel[i]==pred_testlabel[i]])/float(num)
+    print(" Origin_model Accuracy:",accuracy)
+    #define theano funtion to get output of FC layer
+    get_feature = theano.function([origin_model.layers[0].input],origin_model.layers[11].output(train=False),allow_input_downcast=False)
+    feature = get_feature(data)
+    #train svm using FC-layer feature
+    svc(feature[0:30000],label[0:30000],feature[30000:],label[30000:])

DeepLearning Tutorials/dive_into_keras/cnn.py

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+#coding:utf-8
+
+'''
+Author:wepon
+Code:https://github.com/wepe
+
+File:cnn.py
+    GPU run command:
+        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cnn.py
+    CPU run command:
+        python cnn.py
+'''
+#导入各种用到的模块组件
+from __future__ import absolute_import
+from __future__ import print_function
+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
+from keras.utils import np_utils, generic_utils
+from six.moves import range
+from data import load_data
+import random,cPickle
+
+
+nb_epoch = 5
+batch_size = 100
+nb_class = 10
+#加载数据
+data, label = load_data()
+
+#label为0~9共10个类别，keras要求格式为binary class matrices,转化一下，直接调用keras提供的这个函数
+label = np_utils.to_categorical(label, nb_class)
+
+#14layer, one "add" represent one layer
+def create_model():
+	model = Sequential()
+	model.add(Convolution2D(4, 1, 5, 5, border_mode='valid')) 
+	model.add(Activation('relu'))
+
+	model.add(Convolution2D(8,4, 3, 3, border_mode='valid'))
+	model.add(Activation('relu'))
+	model.add(MaxPooling2D(poolsize=(2, 2)))
+
+	model.add(Convolution2D(16, 8, 3, 3, border_mode='valid')) 
+	model.add(Activation('relu'))
+	model.add(MaxPooling2D(poolsize=(2, 2)))
+
+	model.add(Flatten())
+	model.add(Dense(16*4*4, 128, init='normal'))
+	model.add(Activation('relu'))
+	model.add(Dropout(0.2))
+
+	model.add(Dense(128, nb_class, init='normal'))
+	model.add(Activation('softmax'))
+	return model
+
+
+#############
+#开始训练模型
+##############
+model = create_model()
+sgd = SGD(l2=0.0,lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd)
+
+(X_train,X_val) = (data[0:30000],data[30000:])
+(Y_train,Y_val) = (label[0:30000],label[30000:])
+best_accuracy = 0.0
+for e in range(nb_epoch):
+    #shuffle the data each epoch
+    num = len(Y_train)
+    index = [i for i in range(num)]
+    random.shuffle(index)
+    X_train = X_train[index]
+    Y_train = Y_train[index]
+
+    print('Epoch', e)
+    print("Training...")
+    batch_num = len(Y_train)/batch_size
+    progbar = generic_utils.Progbar(X_train.shape[0])
+    for i in range(batch_num):
+        loss,accuracy = model.train(X_train[i*batch_size:(i+1)*batch_size], Y_train[i*batch_size:(i+1)*batch_size],accuracy=True)
+        progbar.add(batch_size, values=[("train loss", loss),("train accuracy:", accuracy)] )
+
+    #save the model of best val-accuracy
+    print("Validation...")
+    val_loss,val_accuracy = model.evaluate(X_val, Y_val, batch_size=1,show_accuracy=True)
+    if best_accuracy<val_accuracy:
+        best_accuracy = val_accuracy
+	cPickle.dump(model,open("./model.pkl","wb"))

DeepLearning Tutorials/dive_into_keras/data.py

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+#coding:utf-8
+"""
+Author:wepon
+Code:https://github.com/wepe
+
+File: data.py
+
+download data here: http://pan.baidu.com/s/1qCdS6
+
+"""
+
+
+import os
+from PIL import Image
+import numpy as np
+
+#读取文件夹mnist下的42000张图片，图片为灰度图，所以为1通道，如果是将彩色图作为输入,则将1替换为3,图像大小28*28
+def load_data():
+	data = np.empty((42000,1,28,28),dtype="float32")
+	label = np.empty((42000,),dtype="uint8")
+	imgs = os.listdir("./mnist")
+	num = len(imgs)
+	for i in range(num):
+		img = Image.open("./mnist/"+imgs[i])
+		arr = np.asarray(img,dtype="float32")
+		data[i,:,:,:] = arr
+		label[i] = int(imgs[i].split('.')[0])
+	#归一化和零均值化
+	scale = np.max(data)
+	data /= scale
+	mean = np.std(data)
+	data -= mean
+	return data,label
+
+
+
+
+
+
+
+

DeepLearning Tutorials/dive_into_keras/get_feature_map.py

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+"""
+Author:wepon
+Code:https://github.com/wepe
+
+File: get_feature_map.py
+	1.  visualize feature map of Convolution Layer, Fully Connected layer
+	2.  rewrite the code so you can treat CNN as feature extractor, see file: cnn-svm.py
+"""
+from __future__ import print_function
+import cPickle,theano
+from data import load_data
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+
+#load the saved model
+model = cPickle.load(open("model.pkl","rb"))
+
+#define theano funtion to get output of  FC layer
+get_feature = theano.function([model.layers[0].input],model.layers[11].output(train=False),allow_input_downcast=False) 
+
+#define theano funtion to get output of  first Conv layer 
+get_featuremap = theano.function([model.layers[0].input],model.layers[2].output(train=False),allow_input_downcast=False) 
+
+
+data, label = load_data()
+
+# visualize feature  of  Fully Connected layer
+#data[0:10] contains 10 images
+feature = get_feature(data[0:10])  #visualize these images's FC-layer feature
+plt.imshow(feature,cmap = cm.Greys_r)
+plt.show()
+
+#visualize feature map of Convolution Layer
+num_fmap = 4	#number of feature map
+for i in range(num_fmap):
+	featuremap = get_featuremap(data[0:10])
+	plt.imshow(featuremap[0][i],cmap = cm.Greys_r) #visualize the first image's 4 feature map
+	plt.show()

DeepLearning Tutorials/keras_usage/cnn.py

@@ -2,9 +2,9 @@
 
 '''
     GPU run command:
-        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cifar10_cnn.py
+        THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python cnn.py
     CPU run command:
-        python cifar10_cnn.py
+        python cnn.py
 '''
 #导入各种用到的模块组件
 from __future__ import absolute_import

DeepLearning Tutorials/readme.md

@@ -7,5 +7,12 @@ DeepLearning Tutorials
 - Softmax_sgd(or logistic_sgd)
 
 
-每个文件夹下有两份代码，一份是Deeplearning网页上的教程的代码，另外一份带有 _commentate 后缀的是我所注释的，因为加入中文注释，显得比较杂乱，仅作帮助理解。真正使用代码时，请直接用原始的代码。
+以上每个文件夹下有两份代码，一份是Deeplearning网页上的教程的代码，另外一份带有 _commentate 后缀的是我所注释的，因为加入中文注释，显得比较杂乱，仅作帮助理解。真正使用代码时，请直接用原始的代码。
+
+建议Theano用户转用Keras，不会让你失望，下面两份介绍Keras的基本使用。
+
+- keras_usage
+- dive _into _keras
+
+
 

README.md

@@ -17,6 +17,8 @@ CSDN：[wepon的专栏](http://blog.csdn.net/u012162613)
 - **DeepLearning Tutorials**
 
       这个文件夹下包含一些深度学习算法的实现代码，以及具体的应用实例，包含：
+
+	  [dive_into _keras]() Keras使用进阶。介绍了怎么保存训练好的CNN模型，怎么将CNN用作特征提取，怎么可视化卷积图。[文章链接](http://blog.csdn.net/u012162613/article/details/45581421)
       
       [keras_usage](https://github.com/wepe/MachineLearning/tree/master/DeepLearning%20Tutorials/keras_usage) 介绍了一个简单易用的深度学习框架keras，用经典的Mnist分类问题对该框架的使用进行说明，训练一个CNN，总共不超过30行代码。[文章链接](http://blog.csdn.net/u012162613/article/details/45397033)