tensorflow42《TensorFlow实战》笔记-09-01 TensorBoard code

# 《TensorFlow实战》09 TensorBoard、多GPU并行及分布式并行# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:sz09.01.py # TensorBoard# 源码位置：# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py# tensorflow\tensorflow\examples\tutorials\mnist\mnist_with_summaries.py# 测试命令# tensorboard --port=6006 --logdir="C:/Python35/tensorlog/sz09"# tensorboard --port=6007 --logdir="C:/Python35/tensorlog/sz09/train"# tensorboard --port=6008 --logdir="C:/Python35/tensorlog/sz09/test"# http://localhost:6006import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamax_steps = 1000learning_rate=0.001dropout=0.9data_dir='MNIST_data/'log_dir='C:/Python35/tensorlog/sz09'mnist = input_data.read_data_sets(data_dir, one_hot=True)sess = tf.InteractiveSession()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('input_reshape'):    image_shaped_input=tf.reshape(x, [-1, 28, 28, 1])    tf.summary.image('input', image_shaped_input, 10)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 variable_summaries(var):    with tf.name_scope('summaries'):        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)def nn_layer(input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):    with tf.name_scope(layer_name):        with tf.name_scope('weights'):            weights = weight_variable([input_dim, output_dim])            variable_summaries(weights)        with tf.name_scope('biases'):            biases = bias_variable([output_dim])            variable_summaries(biases)        with tf.name_scope('Wx_plus_b'):            preactivate = tf.matmul(input_tensor, weights) + biases            tf.summary.histogram('pre_activations', preactivate)        activations = act(preactivate, name='activation')        tf.summary.histogram('activations', activations)        return activationshidden1 = nn_layer(x, 784, 500, 'layer1')with tf.name_scope('dropout'):    keep_prob = tf.placeholder(tf.float32)    tf.summary.scalar('dropout_keep_probalility', keep_prob)    dropped = tf.nn.dropout(hidden1, keep_prob)y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)with tf.name_scope('cross_entropy'):    diff = tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_)    with tf.name_scope('total'):        cross_entropy = tf.reduce_mean(diff)tf.summary.scalar('cross_entropy', cross_entropy)with tf.name_scope('train'):    train_step = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy)with tf.name_scope('accuracy'):    with tf.name_scope('correct_predictin'):        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))    with tf.name_scope('accuracy'):        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))tf.summary.scalar('accuracy', accuracy)merged = tf.summary.merge_all()train_writer = tf.summary.FileWriter(log_dir + '/train', sess.graph)test_writer = tf.summary.FileWriter(log_dir + '/test')tf.global_variables_initializer().run()def feed_dict(train):    if train:        xs, ys = mnist.train.next_batch(100)        k = dropout    else:        xs, ys = mnist.test.images, mnist.test.labels        k = 1.0    return {x:xs, y_: ys, keep_prob: k}saver = tf.train.Saver()for i in range(max_steps):    if i % 10 == 0:        summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))        test_writer.add_summary(summary, i)        print('Accuracy at step %s: %s' % (i, acc))    else:        if i % 100 == 99:            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)            run_metadata = tf.RunMetadata()            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True),                                  options=run_options, run_metadata=run_metadata)            train_writer.add_run_metadata(run_metadata, 'step%03d' % i)            train_writer.add_summary(summary, i)            saver.save(sess, log_dir + "/model.ckpt", i)            print('Adding run metadata for', i)        else:            summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))            train_writer.add_summary(summary, i)train_writer.close()test_writer.close()