#coding = utf8 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets('../MNIST_data', one_hot=True) batch_size = 100 n_batch = mnist.train.num_examples // batch_size def variable_summaries(var): with tf.name_scope('summary'): mean = tf.reduce_mean(var) tf.summary.scalar('mean', mean) with tf.name_scope('stddev'): stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean))) tf.summary.scalar('stddev', stddev) tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var) #namescope with tf.name_scope('input'): x = tf.placeholder(tf.float32, [None, 784], name='x-input') y = tf.placeholder(tf.float32, [None, 10], name='y-input') with tf.name_scope('layer'): with tf.name_scope('weigh'): W = tf.Variable(tf.zeros([784, 10]), name = 'W') variable_summaries(W) with tf.name_scope('biases'): b = tf.Variable(tf.zeros([10]), name = 'b') variable_summaries(b) with tf.name_scope('wx_plus_b'): wx_plus_b = tf.matmul(x, W) + b with tf.name_scope('softmax'): prediction = tf.nn.softmax(wx_plus_b) with tf.name_scope('loss'): #loss = tf.reduce_mean(tf.square(y - prediction)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction)) tf.summary.scalar('loss', loss) with tf.name_scope('train'): train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss) init = tf.global_variables_initializer() with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1)) with tf.name_scope('accuracy'): accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) merged = tf.summary.merge_all() with tf.Session() as sess: sess.run(init) writer = tf.summary.FileWriter('logs/', sess.graph) for epoch in range(25): for batch in range(n_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) summary, _ = sess.run([merged, train_step], feed_dict={x:batch_xs, y:batch_ys}) writer.add_summary(summary, epoch) acc = sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels}) print 'Iter' + str(epoch) + ', Testing Accuracy' + str(acc)