【20170630】TensorFlow与python学习(3)-Mnist进阶
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【00:链接】
mnist入门:http://blog.csdn.net/xumeng7231488/article/details/74011569
mnist进阶教程:http://www.tensorfly.cn/tfdoc/tutorials/mnist_pros.html
【01:代码】
import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=True)import tensorflow as tf#===============================================================config = tf.ConfigProto()config.gpu_options.allow_growth = Truesess = tf.InteractiveSession(config=config)#===============================================================x = tf.placeholder("float", shape=[None, 784])y_ = tf.placeholder("float", shape=[None, 10])W = tf.Variable(tf.zeros([784,10]))b = tf.Variable(tf.zeros([10]))sess.run(tf.initialize_all_variables())y = tf.nn.softmax(tf.matmul(x,W) + b)cross_entropy = -tf.reduce_sum(y_*tf.log(y))train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)for i in range(1000): batch = mnist.train.next_batch(50) train_step.run(feed_dict={x: batch[0], y_: batch[1]})correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))print(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}) )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')W_conv1 = weight_variable([5, 5, 1, 32])b_conv1 = bias_variable([32])x_image = tf.reshape(x, [-1,28,28,1])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, 32, 64])b_conv2 = bias_variable([64])h_conv2 = tf.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("float")h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)W_fc2 = weight_variable([1024, 10])b_fc2 = bias_variable([10])y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv))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, "float"))#======================================================sess.run(tf.initialize_all_variables())for i in range(20000): batch = mnist.train.next_batch(50) if i%100 == 0: train_accuracy = accuracy.eval(feed_dict={ x:batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, training accuracy %g"%(i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})print("test accuracy %g"%accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))#===============================================================sess.close()
【02:实验结果】
accuracy:0.9926
【03:gpu内存动态分配allow_gpu_memory_grow】
在sess定义前加入:
config = tf.ConfigProto()config.gpu_options.allow_growth = Truesess = tf.InteractiveSession(config=config)
在代码最后加入:
sess.close()
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