tensorflow23《TensorFlow实战Google深度学习框架》笔记-09-04 TensorBoard 监控指标可视化 code

# 《TensorFlow实战Google深度学习框架》09 TensorBoard可视化# win10 Tensorflow1.0.1 python3.5.3# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1# filename:ts09.04.py # 监控指标可视化import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data# 1. 生成变量监控信息并定义生成监控信息日志的操作# C:\Python35>tensorboard --port=6006 --debug --logdir=c:/python35/tensorlog/show04SUMMARY_DIR = "c:/python35/tensorlog/show04"BATCH_SIZE = 100TRAIN_STEPS = 3000def variable_summaries(var, name):    with tf.name_scope('summaries'):        tf.summary.histogram(name, var)        mean = tf.reduce_mean(var)        tf.summary.scalar('mean/' + name, mean)        stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))        tf.summary.scalar('stddev/' + name, stddev)# 2. 生成一层全链接的神经网络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 = tf.Variable(tf.truncated_normal([input_dim, output_dim], stddev=0.1))            variable_summaries(weights, layer_name + '/weights')        with tf.name_scope('biases'):            biases = tf.Variable(tf.constant(0.0, shape=[output_dim]))            variable_summaries(biases, layer_name + '/biases')        with tf.name_scope('Wx_plus_b'):            preactivate = tf.matmul(input_tensor, weights) + biases            tf.summary.histogram(layer_name + '/pre_activations', preactivate)        activations = act(preactivate, name='activation')        # 记录神经网络节点输出在经过激活函数之后的分布。        tf.summary.histogram(layer_name + '/activations', activations)        return activationsdef main():    mnist = input_data.read_data_sets("../../datasets/MNIST_data", one_hot=True)    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)    hidden1 = nn_layer(x, 784, 500, 'layer1')    y = nn_layer(hidden1, 500, 10, 'layer2', act=tf.identity)    with tf.name_scope('cross_entropy'):        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y, labels=y_))        tf.summary.scalar('cross_entropy', cross_entropy)    with tf.name_scope('train'):        train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)    with tf.name_scope('accuracy'):        with tf.name_scope('correct_prediction'):            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()    with tf.Session() as sess:        summary_writer = tf.summary.FileWriter(SUMMARY_DIR, sess.graph)        tf.global_variables_initializer().run()        for i in range(TRAIN_STEPS):            xs, ys = mnist.train.next_batch(BATCH_SIZE)            # 运行训练步骤以及所有的日志生成操作，得到这次运行的日志。            summary, _ = sess.run([merged, train_step], feed_dict={x: xs, y_: ys})            # 将得到的所有日志写入日志文件，这样TensorBoard程序就可以拿到这次运行所对应的            # 运行信息。            summary_writer.add_summary(summary, i)    summary_writer.close()if __name__ == '__main__':    main()