keras程序—最简单CNN实现

如果你已经配置好GPU版本的tensorflow那么在ubuntu14.04主机上执行：$ pip install keras 就可以安装keras环境

tensorflow版本的CNN请参照：http://blog.csdn.net/u012969412/article/details/70837231

#-*- coding:utf-8 -*-
import numpy as np
np.random.seed(123)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.pooling import MaxPooling2D
from keras.layers import Embedding, LSTM
from keras.utils import np_utils
from keras.datasets import mnist
# 载入mnist数据集(第一次执行需要下载数据)
def loda_mnist():
	(X_train, y_train), (X_test, y_test) = mnist.load_data()
	# 5. Preprocess input data
	X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
	X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
	X_train = X_train.astype('float32')
	X_test = X_test.astype('float32')
	X_train /= 255
	X_test /= 255
	Y_train = np_utils.to_categorical(y_train, 10)
	Y_test  = np_utils.to_categorical(y_test, 10)
	print X_train.shape,Y_train.shape
	print X_test.shape,Y_test.shape
 	return X_train,Y_train,X_test,Y_test

if __name__ == '__main__':
	X_train,Y_train,X_test,Y_test = loda_mnist();
	model = Sequential()
	# 2个卷积层
	model.add( Conv2D(32, (5, 5), activation='relu', input_shape=(28,28,1)) ) # 第一层卷积
	model.add( MaxPooling2D(pool_size=(2,2)) )                                # 第一层池化 
	model.add( Conv2D(64, (5, 5), activation='relu', input_shape=(14,14,1)) ) # 第二层卷积
	model.add( MaxPooling2D(pool_size=(2,2)) )                                # 第二层池化
	model.add( Dropout(0.25) ) # 添加节点keep_prob
	# 2个全连接层
	model.add( Flatten() ) # 将多维数据压成1维，方便全连接层操作
	model.add( Dense(1024, activation='relu') ) # 添加全连接层
	model.add( Dropout(0.5) )
	model.add( Dense(10, activation='softmax') )
	# 编译模型
	model.compile( loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] )
	# 训练模型
	model.fit( X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1 )
	# 评估模型
	score = model.evaluate( X_test, Y_test, verbose=0 )
	print score