TensorFlow Combat-alexnet

1 #Import Data2  fromTensorflow.examples.tutorials.mnistImportInput_data3 #reading Data4Mnist=input_data.read_data_sets ("mnist_data/", one_hot=True)5 ImportTensorFlow as TF6 7 #defining the convolution operation function8 defconv2d (name,x,w,b):9     returnTf.nn.relu (Tf.nn.bias_add (tf.nn.conv2d (x,w,strides=[1,1,1,1],padding='same'), b), name=name)Ten  One #define the sample operation function A defMax_pool (name,x,k): -     returnTf.nn.max_pool (x,ksize=[1,k,k,1],strides=[1,k,k,1],padding='same', name=name) -  the #Defining a normalization function - defNorm (name,x,lsize=4): -     returnTF.NN.LRN (x,lsize,bias=1.0,alpha=0.001/9,beta=0.75,name=name) -  + #formally define the alex_net network structure - defalex_net (_x,_weights,_biases,_dropout): +     #to deform the input A_x=tf.reshape (_x,shape=[-1,28,28,1])       at          -     #Build the first convolutional layer -Conv1=conv2d ('CONV1', _x,_weights['WC1'],_biases['BC1']) -Pool1=max_pool ('Pool1', conv1,k=2) -Norm1=norm ('Norm1', pool1,lsize=4) -drop1=tf.nn.dropout (norm1,_dropout) in           -     #Build a second convolutional layer toConv2=conv2d ('Conv2', drop1,_weights['WC2'],_biases['BC2']) +Pool2=max_pool ('pool2', conv2,k=2) -Norm2=norm ('Norm2', pool2,lsize=4) theDrop2=tf.nn.dropout (norm2,_dropout) *         $     #build a third convolutional layerPanax NotoginsengConv3=conv2d ('Conv3', drop2,_weights['wc3'],_biases['BC3']) -Pool3=max_pool ('pool3', conv3,k=2) theNorm3=norm ('NORM3', pool3,lsize=4) +drop3=tf.nn.dropout (norm3,_dropout) A       the     #deform the output and connect the three fully connected layers +Dense1=tf.reshape (drop3,shape=[-1,_weights['WD1'].get_shape (). As_list () [0]]) -Dense1=tf.nn.relu (Tf.matmul (dense1,_weights['WD1']) +_biases['BD1'],name='FC1') $Dense2=tf.nn.relu (Tf.matmul (dense1,_weights['WD2']) +_biases['Bd2'],name='FC2') $Out=tf.matmul (dense2,_weights[' out']+_biases[' out']) -     return out -  the #Set Network Training parameters -learning_rate=0.001Wuyitraining_iters=200000 theBatch_size=64 -Display_step=20 Wu  - #Setting Data Parameters Aboutn_input=784 $n_classes=10 -dropout=0.8 -  - #placeholder input some version written in Tf.types.float32 Ax=Tf.placeholder (Tf.float32,[none,n_input]) +y=Tf.placeholder (tf.float32,[none,n_classes]) thekeep_prob=Tf.placeholder (Tf.float32) -  $ #set the network core size, number of layers, sampling steps, etc. theweights={ the     'WC1': TF. Variable (Tf.random_normal ([3,3,1,64])), the     'WC2': TF. Variable (Tf.random_normal ([3,3,64,128])), the     'wc3': TF. Variable (Tf.random_normal ([3,3,128,256])), -     'WD1': TF. Variable (Tf.random_normal ([4*4*256,1024])), in     'WD2': TF. Variable (Tf.random_normal ([1024,1024])), the     ' out': TF. Variable (Tf.random_normal ([1024,10])) the     } About  thebiases={ the     'BC1': TF. Variable (Tf.random_normal ([64])), the     'BC2': TF. Variable (Tf.random_normal ([128])), +     'BC3': TF. Variable (Tf.random_normal ([256])), -     'BD1': TF. Variable (Tf.random_normal ([1024])), the     'Bd2': TF. Variable (Tf.random_normal ([1024])),Bayi     ' out': TF. Variable (Tf.random_normal ([n_classes])) the } the  - #Build a alex_net network and output the results based on input predictions -Pred=alex_net (X,weights,biases,keep_prob) the  the #build a loss function based on the predicted value pred and the true label y thecost=Tf.reduce_mean (Tf.nn.softmax_cross_entropy_with_logits (pred,y)) the//Set optimization function, minimize loss function -Optimizer=tf.train.adamoptimizer (learning_rate=learning_rate). Minimize (cost) the  the #set up a test network theCorrect_pred=tf.equal (Tf.argmax (pred,1), Tf.argmax (y,1))94accuracy=Tf.reduce_mean (Tf.cast (correct_pred,tf.float32)) the  the #Initialize the network all to the weight of the version written Global_variables_initializer theinit=tf.initialize_all_variables ()98  About With TF. Session () as Sess: -      #Initialize network weights101 Sess.run (init)102Step = 1103 104      #Start training The network until the maximum number of iterations is reached the       whileStep * Batch_size <training_iters:106          #Get Batch Data107Batch_xs, Batch_ys =Mnist.train.next_batch (batch_size)108          109          #Training Network based on batch data theSess.run (Optimizer, feed_dict={x:batch_xs, Y:batch_ys, keep_prob:dropout})111  the          #Print Output113          ifStep% Display_step = =0: the              #Calculation Accuracy theACC = sess.run (accuracy, FEED_DICT={X:BATCH_XS, Y:batch_ys, keep_prob:1.}) the              #Calculate loss Value117Loss = Sess.run (cost, Feed_dict={x:batch_xs, Y:batch_ys, keep_prob:1.})118              Print "Iter"+ STR (step*batch_size) +", Minibatch loss="+"{:. 6f}". Format (loss) +", Training accuracy="+"{:. 5f}". Format (ACC)119Step + = 1 - 121      Print "optimization finished!"122      #Calculate test Accuracy123      Print "Testing Accuracy:", Sess.run (accuracy, feed_dict={x:mnist.test.images[:256], y:mnist.test.labels[:256], keep_prob:1.})

View Code

AlexNet:

Won the 2012-year champion of the ILSVRC Competition classification project (top-5 error rate 16.4%, using additional data to reach 15.3%, 8 layer neural Network 3 conv+2 pooling+3 FC)

The first successful application of Relu dropout and LRN trick in CNN:

Successfully use Relu as the activation function of CNN, and verify its effect in the deeper to the network over the sigmoid, successfully solve the sigmoid in the deep network to the gradient dispersion problem.

Although the Relu activation function was proposed a long time ago, it was not carried forward until the advent of alexnet.

Use during training

TensorFlow Combat-alexnet