MNIST机器学习入门 (http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_beginners.html)
终端输入 tensorboard --logdir='logs/'可以得到一个地址,浏览器中打开这个网址就可以看可视化结果了。
TensorFlow学习笔记2:构建CNN模型 博客写的非常好。 (http://www.jeyzhang.com/tensorflow-learning-notes-2.html) 代码:
#coding=utf-8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data def weight_variable(shape): initial = tf.truncated_normal(shape, stddev=0.1)#为了避免网络对称性,初始化时加入小的噪声 return tf.Variable(initial) def bias_variable(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial) def conv2d(x, W): #卷积,滑动窗口步长1,padding='SAME'表示通过填充0,使得输入和输出的形状一致。 return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') def max_pool_2x2(x): #池化:最大池化,2*2窗口 return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') #读入数据和初始化 flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_string('data_dir', '/media/zhangsp/softwares/tensorflowUbuntu/mnist/data/', 'Directory for storing data') mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) sess = tf.InteractiveSession() 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])) W_conv1 = weight_variable([5, 5, 1, 32]) #第一个卷积层,5×5的patch,输入1通道,输出32通道(32个特征映射/卷积核) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) #reshape成4维,第2、3维是图片的宽和高,第四维是通道数 h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)#卷积+偏置,再relu激活 h_pool1 = max_pool_2x2(h_conv1) #池化 W_conv2 = weight_variable([5, 5, 32, 64]) #第二个卷积层,提取64个特征,每个7*7大小 b_conv2 = bias_variable([64]) h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) h_pool2 = max_pool_2x2(h_conv2) # Now image size is reduced to 7*7 W_fc1 = weight_variable([7 * 7 * 64, 1024]) #全连接层 b_fc1 = bias_variable([1024]) h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])#reshape成向量 h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)#向量乘法,h*w+b keep_prob = tf.placeholder("float") #不被droupout丢弃的概率 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)#softmax输出 cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)#没用梯度下降哟 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(2000): #我减少了一下训练量。。。本来是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 %.3f" % (i, train_accuracy) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) print "Training finished" print "test accuracy %.3f" % accuracy.eval(feed_dict={ x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})