Tensorflow32 "TensorFlow Combat" note -05 TensorFlow realize convolutional neural Network code

01 Simple Convolution network

# "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.01.py # Simple convolution network from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf mnist = Input_ Data.read_data_sets ("mnist_data/", one_hot=true) Sess = tf. InteractiveSession () def weight_variable (shape): initial = Tf.truncated_normal (shape, stddev=0.1) return TF. Variable (initial) def bias_variable (shape): initial = Tf.constant (0.1, Shape=shape) return TF. Variable (initial) def conv2d (X, W): Return tf.nn.conv2d (x, W, strides = [1, 1, 1, 1], padding = "SAME") def Max_pool _2X2 (x): Return Tf.nn.max_pool (x, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") x = Tf.placeholder ( Tf.float32, [None, 784]) Y_ = Tf.placeholder (Tf.float32, [None,]) X_image = Tf.reshape (x, [-1, 1]) W_conv1 = W Eight_variable ([5, 5, 1,,]) B_CONV1 = Bias_variable ([o]) h_conv1 = Tf.nn.relu (conv2d (X_image, W_CONV1) + B_cONV1) H_pool1 = max_pool_2x2 (h_conv1) w_conv2 = Weight_variable ([5, 5, ()]) B_conv2 = bias_variable ([+]) H_conv2 = t 
F.nn.relu (conv2d (h_pool1, w_conv2) + b_conv2) H_pool2 = max_pool_2x2 (h_conv2) w_fc1 = weight_variable ([7 * 7 * 64, 1024]) B_FC1 = Bias_variable ([1024]) H_pool2_flat = Tf.reshape (H_pool2, [-1, 7*7*64]) H_fc1 = Tf.nn.relu (Tf.matmul (H_pool2_flat , W_FC1) + b_fc1) Keep_prob = Tf.placeholder (tf.float32) H_fc1_drop = Tf.nn.dropout (H_FC1, keep_prob) W_FC2 = Weight_var Iable ([1024]) B_FC2 = Bias_variable ([ten]) Y_conv = Tf.nn.softmax (Tf.matmul (H_fc1_drop, W_FC2) + b_fc2) cross_entropy = Tf.reduce_mean (-tf.reduce_sum (Y_ * Tf.log (Y_CONV), reduction_indices=[1])) Train_step = Tf.train.AdamOptimizer (1e-4 ). Minimize (cross_entropy) correct_prediction = Tf.equal (Tf.argmax (Y_conv, 1), Tf.argmax (Y_, 1)) accuracy = Tf.reduce_ Mean (Tf.cast (correct_prediction, Tf.float32)) Tf.global_variables_initializer (). Run () to I in range (20000): Batch = Mnist.train.next_batch (x) ifI% 1000 = = 0:train_accuracy = Accuracy.eval (feed_dict = {X:batch[0], Y_: batch[1], keep_prob:1.0}) PRI NT ("Step%d, training accuracy%g"% (I, train_accuracy)) Train_step.run (feed_dict = {X:batch[0], Y_: batch[1], keep_p rob:0.5}) Print ("Test accuracy%g"%accuracy.eval (Feed_dict={x:mnist.test.images, Y_: Mnist.test.labels, keep_prob:1.) 0}) "Step 0, training accuracy 0.04 Step 1000, training accuracy 0.96 step, training accuracy 0.92 ... step 16000 , training accuracy 0.98 step 17000, training accuracy 1 step 18000, training accuracy 1 step 19000, training accuracy 1 t EST accuracy 0.9918 ' "

Cifar10 Convolution Network

# "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.02.py # Cifar10 Convolution network # CIFAR10 Cifar10_input can get import tensorflow_models\tutorials\image\cifar10 from Cifar10, Cifar10_input Import TensorFlow as TF import numpy as NP import time Max_steps = 3000 batch_size = 128 # Http://www.cs.to Ronto.edu/~kriz/cifar-10-binary.tar.gz # to extract cifar-10-binary.tar.gz to cifar10_data/cifar10-10-batches-bin data_dir = " Cifar10_data/cifar10-10-batches-bin "def Variable_with_weight_loss" (Shape, StdDev, wl): var = tf. Variable (Tf.truncated_normal (shape, StdDev = StdDev)) If WL is not None:weight_loss = tf.multiply (Tf.nn.l2_lo SS (Var), WL, name= "Weight_loss") tf.add_to_collection (' losses ', Weight_loss) return var Cifar10.maybe_downloa D_and_extract () images_train, Labels_train = Cifar10_input.distorted_inputs (Data_dir=data_dir, Batch_size=batch_ Size) images_test, labels_test = cifar10_input.inputs (Eval_data = True, Data_dir=data_dir, batch_size=batch_size) Image_holder = Tf.placeholder (Tf.float32, [Batch_size, 3]) label _holder = Tf.placeholder (Tf.int32, [batch_size]) weight1 = Variable_with_weight_loss (Shape=[5, 5, 3,), StdDev = 5e-2, WL = 0.0) Kernel1 = tf.nn.conv2d (Image_holder, weight1, [1, 1, 1, 1], padding = ' SAME ') BIAS1 = tf. Variable (tf.constant (0.0, shape = [)) Conv1 = Tf.nn.relu (Tf.nn.bias_add (Kernel1, bias1)) Pool1 = Tf.nn.max_pool (conv1 , ksize = [1, 3, 3, 1], strides = [1, 2, 2, 1], padding = ' SAME ') Norm1 = TF.NN.LRN (pool1, 4, bias = 1.0, alpha = 0.001/ 9.0, beta = 0.75) weight2 = variable_with_weight_loss (Shape = [5, 5,, 5e-2, StdDev = wl) 0.0 = Kernel2 Nv2d (Norm1, Weight2, [1, 1, 1, 1], padding = ' SAME ') BIAS2 = tf. Variable (Tf.constant (0.1, shape = [)) Conv2 = Tf.nn.relu (Tf.nn.bias_add (Kernel2, bias2)) Norm2 = Tf.nn.lrn (conv2, 4, Bi As = 1.0, alpha = 0.001/9.0, beta = 0.75) pool2 = Tf.nn.max_pool (norm2, ksize = [1, 3, 3, 1], strides = [1, 2, 2, 1], padding = ' SAME ') reshape = Tf.reshape (pool2, [Batch_size,-1]) Dim = Reshape.get_shape () [1].value WEIGHT3 = Variable_with_weight_loss (Shape = [Dim, 384], StdDev = 0.04, wl = 0.004) BIAS3 = tf. Variable (Tf.constant (0.1, shape = [384])) Local3 = Tf.nn.relu (Tf.matmul (reshape, WEIGHT3) + bias3) Weight4 = Variable_wit H_weight_loss (shape = [384,), StdDev = 0.04, wl = 0.004) Bias4 = tf. Variable (0.1, shape = [Tf.constant]) Local4 = Tf.nn.relu (Tf.matmul (Local3, WEIGHT4) + bias4) Weight5 = Variable_with _weight_loss (shape = [StdDev,], 1/192.0 = wl = 0.0) BIAS5 = tf.
    Variable (tf.constant (0.0, shape = [ten])) Logits = Tf.add (Tf.matmul (Local4, WEIGHT5), BIAS5) def loss (logits, labels): Labels = tf.cast (labels, tf.int64) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits (logits = l ogits, labels = labels, name = ' Cross_entropy_per_example ') Cross_entropy_mean = Tf.reduce_mean (cross_entropy, name = ' Cross_entropy ') tf.add_to_collectiOn (' losses ', Cross_entropy_mean) return Tf.add_n (Tf.get_collection (' losses '), name = ' Total_loss ') loss = loss (logit S, label_holder) Train_op = Tf.train.AdamOptimizer (1e-3). Minimize (loss) Top_k_op = Tf.nn.in_top_k (Logits, Label_holder , 1) sess = tf. InteractiveSession () Tf.global_variables_initializer (). Run () tf.train.start_queue_runners () for step in range (max_ Steps): Start_time = Time.time () image_batch, Label_batch = Sess.run ([Images_train, Labels_train]) _, Loss_val UE = Sess.run ([Train_op, loss], feed_dict = {image_holder:image_batch, label_holder:label_batch}) Duration = Time.ti Me ()-start_time if step% = = 0:examples_per_sec = batch_size/duration Sec_per_batch = Float (du  Ration) format_str= (' step%d, loss =%.2f (%.1f examples/sec;%.3f sec/batch) ") Print (format_str% (step, Loss_value, Examples_per_sec, sec_per_batch)) Num_examples = 10000 import Math num_iter = Int (Math.ceil (num_examples/b atch_size)) True_couNT = 0 Total_sample_count = num_iter * Batch_size step = 0 While step < Num_iter:image_batch, Label_batch = SESS.R Un ([images_test, labels_test]) predictions = Sess.run ([top_k_op], feed_dict = {Image_holder:image_batch, label_holder : Label_batch}) True_count + + np.sum (predictions) Step = 1 predictions = true_count/total_sample_count print (' P Recision @ 1 =%.3f '% predictions) ' step 0, loss = 4.67 (6.0 examples/sec; 21.268 sec/batch) step, loss = 3.65 (773 .7 Examples/sec; 0.165 sec/batch) ... step 2970, loss = 0.95 (877.4 examples/sec; 0.146 sec/batch) step 2980, loss = 1.12 (862.6 examples/s Ec 0.148 Sec/batch) Step 2990, loss = 1.06 (967.1 examples/sec; 0.132 sec/batch) precision @ 1 = 0.705 ""