TensorFlow练习1: 编程实现一个简单的CNN, Convolutional Neural Networks

Convolutional Neural Networks翻译为卷积神经网络，常用在图像识别和语音分析等领域。CNN详细介绍参看：

• https://en.wikipedia.org/wiki/Convolutional_neural_network
• http://blog.youkuaiyun.com/zouxy09/article/details/8781543
• http://deeplearning.net/tutorial/lenet.html

使用TensorFlow创建CNN

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import tensorflow as tf import numpy as np   # 下载mnist数据集 from tensorflow . examples . tutorials . mnist import input_data mnist = input_data . read_data_sets ( '/tmp/' , one_hot = True )     n_output_layer = 10   # 定义待训练的神经网络 def convolutional_neural_network ( data ) : weights = { 'w_conv1' : tf . Variable ( tf . random_normal ( [ 5 , 5 , 1 , 32 ] ) ) ,                'w_conv2' : tf . Variable ( tf . random_normal ( [ 5 , 5 , 32 , 64 ] ) ) ,                'w_fc' : tf . Variable ( tf . random_normal ( [ 7 * 7 * 64 , 1024 ] ) ) ,                'out' : tf . Variable ( tf . random_normal ( [ 1024 , n_output_layer ] ) ) }   biases = { 'b_conv1' : tf . Variable ( tf . random_normal ( [ 32 ] ) ) ,                'b_conv2' : tf . Variable ( tf . random_normal ( [ 64 ] ) ) ,                'b_fc' : tf . Variable ( tf . random_normal ( [ 1024 ] ) ) ,                'out' : tf . Variable ( tf . random_normal ( [ n_output_layer ] ) ) }   data = tf . reshape ( data , [ - 1 , 28 , 28 , 1 ] )   conv1 = tf . nn . relu ( tf . add ( tf . nn . conv2d ( data , weights [ 'w_conv1' ] , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) , biases [ 'b_conv1' ] ) ) conv1 = tf . nn . max_pool ( conv1 , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' )   conv2 = tf . nn . relu ( tf . add ( tf . nn . conv2d ( conv1 , weights [ 'w_conv2' ] , strides = [ 1 , 1 , 1 , 1 ] , padding = 'SAME' ) , biases [ 'b_conv2' ] ) ) conv2 = tf . nn . max_pool ( conv2 , ksize = [ 1 , 2 , 2 , 1 ] , strides = [ 1 , 2 , 2 , 1 ] , padding = 'SAME' )   fc = tf . reshape ( conv2 , [ - 1 , 7 * 7 * 64 ] ) fc = tf . nn . relu ( tf . add ( tf . matmul ( fc , weights [ 'w_fc' ] ) , biases [ 'b_fc' ] ) )   # dropout剔除一些"神经元" #fc = tf.nn.dropout(fc, 0.8)   output = tf . add ( tf . matmul ( fc , weights [ 'out' ] ) , biases [ 'out' ] ) return output     # 每次使用100条数据进行训练 batch_size = 100   X = tf . placeholder ( 'float' , [ None , 28 * 28 ] ) Y = tf . placeholder ( 'float' ) # 使用数据训练神经网络 def train_neural_network ( X , Y ) : predict = convolutional_neural_network ( X ) cost_func = tf . reduce_mean ( tf . nn . softmax_cross_entropy_with_logits ( predict , Y ) ) optimizer = tf . train . AdamOptimizer ( ) . minimize ( cost_func )    # learning rate 默认 0.001   epochs = 1 with tf . Session ( ) as session : session . run ( tf . initialize_all_variables ( ) ) epoch_loss = 0 for epoch in range ( epochs ) : for i in range ( int ( mnist . train . num_examples / batch_size ) ) : x , y = mnist . train . next_batch ( batch_size ) _ , c = session . run ( [ optimizer , cost_func ] , feed_dict = { X : x , Y : y } ) epoch_loss += c print ( epoch , ' : ' , epoch_loss )   correct = tf . equal ( tf . argmax ( predict , 1 ) , tf . argmax ( Y , 1 ) ) accuracy = tf . reduce_mean ( tf . cast ( correct , 'float' ) ) print ( '准确率: ' , accuracy . eval ( { X : mnist . test . images , Y : mnist . test . labels } ) )   train_neural_network ( X , Y )

执行结果：

1	准确率 :    0.9789

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下面使用tflearn重写上面代码，tflearn是TensorFlow的高级封装，类似Keras。

tflearn提供了更简单、直观的接口。和scikit-learn差不多，代码如下：

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import tflearn from tflearn . layers . conv import conv_2d , max_pool_2d from tflearn . layers . core import input_data , dropout , fully_connected from tflearn . layers . estimator import regression   train_x , train_y , test_x , test_y = tflearn . datasets . mnist . load_data ( one_hot = True )   train_x = train_x . reshape ( - 1 , 28 , 28 , 1 ) test_x = test_x . reshape ( - 1 , 28 , 28 , 1 )   # 定义神经网络模型 conv_net = input_data ( shape = [ None , 28 , 28 , 1 ] , name = 'input' ) conv_net = conv_2d ( conv_net , 32 , 2 , activation = 'relu' ) conv_net = max_pool_2d ( conv _net , 2 ) conv_net = conv_2d ( conv_net , 64 , 2 , activation = 'relu' ) conv_net = max_pool_2d ( conv _net , 2 ) conv_net = fully_connected ( conv_net , 1024 , activation = 'relu' ) conv_net = dropout ( conv_net , 0.8 ) conv_net = fully_connected ( conv_net , 10 , activation = 'softmax' ) conv_net = regression ( conv_net , optimizer = 'adam' , loss = 'categorical_crossentropy' , name = 'output' )   model = tflearn . DNN ( conv_net )   # 训练 model . fit ( { 'input' : train_x } , { 'output' : train_y } , n_epoch = 13 ,            validation_set = ( { 'input' : test_x } , { 'output' : test_y } ) ,            snapshot_step = 300 , show_metric = True , run_id = 'mnist' )   model . save ( 'mnist.model' )    # 保存模型   """ model.load('mnist.model')   # 加载模型 model.predict([test_x[1]])  # 预测 """