TensorBoard 实践 1

从新查看图的时候，删除旧的logs/下面的文件

 

tf.scalar_summary('loss',self.loss)

AttributeError: 'module' object has no attribute 'scalar_summary'

解决：

tf.scalar_summary('images', images)改为：tf.summary.scalar('images', images)

tf.image_summary('images', images)改为：tf.summary.image('images', images)

类似的有：

tf.train.SummaryWriter改为：tf.summary.FileWriter()

tf.merge_all_summaries()改为：summary_op = tf.summaries.merge_all()

tf.histogram_summary(var.op.name, var)改为：  tf.summaries.histogram()

concated = tf.concat(1, [indices, sparse_labels])改为：concated = tf.concat([indices, sparse_labels], 1)

通过对命名空间管理，改进代码，使得可视化效果图更加清晰。

#coding=utf8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import mnist_inference

BATCH_SIZE = 100
LEARNING_RATE_BASE = 0.8
LEARNING_RATE_DECAY = 0.99
REGULARIZATION_RATE = 0.0001
TRAINING_STEPS = 3000
MOVING_AVERAGE_DECAY = 0.99

def train(mnist):
    #  输入数据的命名空间。
    with tf.name_scope('input'):
        x = tf.placeholder(tf.float32, [None, mnist_inference.INPUT_NODE], name='x-input')
        y_ = tf.placeholder(tf.float32, [None, mnist_inference.OUTPUT_NODE], name='y-input')
    regularizer = tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)
    y = mnist_inference.inference(x, regularizer)
    global_step = tf.Variable(0, trainable=False)
    
    # 处理滑动平均的命名空间。
    with tf.name_scope("moving_average"):
        variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step)
        variables_averages_op = variable_averages.apply(tf.trainable_variables())
   
    # 计算损失函数的命名空间。
    with tf.name_scope("loss_function"):
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
        cross_entropy_mean = tf.reduce_mean(cross_entropy)
        loss = cross_entropy_mean + tf.add_n(tf.get_collection('losses'))
    
    # 定义学习率、优化方法及每一轮执行训练的操作的命名空间。
    with tf.name_scope("train_step"):
        learning_rate = tf.train.exponential_decay(
            LEARNING_RATE_BASE,
            global_step,
            mnist.train.num_examples / BATCH_SIZE, LEARNING_RATE_DECAY,
            staircase=True)

        train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)

        with tf.control_dependencies([train_step, variables_averages_op]):
            train_op = tf.no_op(name='train')
    
    writer = tf.summary.FileWriter("./log/modified_mnist_train.log", tf.get_default_graph())
    
    # 训练模型。
    with tf.Session() as sess:
        tf.global_variables_initializer().run()
        for i in range(TRAINING_STEPS):
            xs, ys = mnist.train.next_batch(BATCH_SIZE)

            if i % 1000 == 0:
                # 配置运行时需要记录的信息。
                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
                # 运行时记录运行信息的proto。
                run_metadata = tf.RunMetadata()
                _, loss_value, step = sess.run(
                    [train_op, loss, global_step], feed_dict={x: xs, y_: ys},
                    options=run_options, run_metadata=run_metadata)
                writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d" % i), global_step=i)
                print("After %d training step(s), loss on training batch is %g." % (step, loss_value))
            else:
                _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})
                
    writer.close()


def main(argv=None): 
    mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
    train(mnist)

if __name__ == '__main__':
    main()

 

可视化效果图：