欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

tensorboard+win10+pycharm(记录自己的问题)

程序员文章站 2024-03-15 11:35:47
...

tensorboad使用血泪史

本来是按照师姐的吩咐,在做压缩神经的实验,改改师姐的代码,看起来都很容易,突然抽了个疯,想把这个网络的图画出来,结果,死在tensorboard上了:

前提

  1. 安装好anaconda 和 python 和tensorflow。下面这个博客,一站搞定:windows+anaconda+python
  2. 安装tensorboard

找一段代码然后执行

我用的是自己的代码改了改。尝试使用tensorboard的可以使用下面这个代码,传说是官网给的,有点长啊啊。这有个教程解释得还比较详细:tensorboard,辛苦他了。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

FLAGS = None


def train():
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir,
                                      one_hot=True,
                                      fake_data=FLAGS.fake_data)

    sess = tf.InteractiveSession()
    # Create a multilayer model.

    # Input placeholders
    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)

        # We can't initialize these variables to 0 - the network will get stuck.

    def weight_variable(shape):
        """Create a weight variable with appropriate initialization."""
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(shape):
        """Create a bias variable with appropriate initialization."""
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def variable_summaries(var):
        """Attach a lot of summaries to a Tensor (for TensorBoard visualization)."""
        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):
        """Reusable code for making a simple neural net layer.
        It does a matrix multiply, bias add, and then uses relu to nonlinearize.
        It also sets up name scoping so that the resultant graph is easy to read,
        and adds a number of summary ops.
        """
        # Adding a name scope ensures logical grouping of the layers in the graph.
        with tf.name_scope(layer_name):
            # This Variable will hold the state of the weights for the layer
            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 activations

    hidden1 = nn_layer(x, 784, 500, 'layer1')

    with tf.name_scope('dropout'):
        keep_prob = tf.placeholder(tf.float32)
        tf.summary.scalar('dropout_keep_probability', keep_prob)
        dropped = tf.nn.dropout(hidden1, keep_prob)

        # Do not apply softmax activation yet, see below.
    y = nn_layer(dropped, 500, 10, 'layer2', act=tf.identity)

    with tf.name_scope('cross_entropy'):
        # The raw formulation of cross-entropy,
        #
        # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.softmax(y)),
        #                               reduction_indices=[1]))
        #
        # can be numerically unstable.
        #
        # So here we use tf.nn.softmax_cross_entropy_with_logits on the
        # raw outputs of the nn_layer above, and then average across
        # the batch.
        diff = tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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(FLAGS.learning_rate).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)

    # Merge all the summaries and write them out to /tmp/tensorflow/mnist/logs/mnist_with_summaries (by default)
    merged = tf.summary.merge_all()
    train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
    test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
    tf.global_variables_initializer().run()

    # Train the model, and also write summaries.
    # Every 10th step, measure test-set accuracy, and write test summaries
    # All other steps, run train_step on training data, & add training summaries

    def feed_dict(train):
        """Make a TensorFlow feed_dict: maps data onto Tensor placeholders."""
        if train or FLAGS.fake_data:
            xs, ys = mnist.train.next_batch(100, fake_data=FLAGS.fake_data)
            k = FLAGS.dropout
        else:
            xs, ys = mnist.test.images, mnist.test.labels
            k = 1.0
        return {x: xs, y_: ys, keep_prob: k}

    for i in range(FLAGS.max_steps):
        if i % 10 == 0:  # Record summaries and test-set accuracy
            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:  # Record train set summaries, and train
            if i % 100 == 99:  # Record execution stats
                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)
                print('Adding run metadata for', i)
            else:  # Record a summary
                summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
                train_writer.add_summary(summary, i)
    train_writer.close()
    test_writer.close()


def main(_):
    if tf.gfile.Exists(FLAGS.log_dir):
        tf.gfile.DeleteRecursively(FLAGS.log_dir)
    tf.gfile.MakeDirs(FLAGS.log_dir)
    train()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
                        default=False,
                        help='If true, uses fake data for unit testing.')
    parser.add_argument('--max_steps', type=int, default=1000,
                        help='Number of steps to run trainer.')
    parser.add_argument('--learning_rate', type=float, default=0.001,
                        help='Initial learning rate')
    parser.add_argument('--dropout', type=float, default=0.9,
                        help='Keep probability for training dropout.')
    parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
                        help='Directory for storing input data')
    parser.add_argument('--log_dir', type=str, default='F:/mnist_with_summaries',
                        help='Summaries log directory')
    FLAGS, unparsed = parser.parse_known_args()
    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

代码中倒数第三句,至关重要,将log_dir的值改为自己打算存放日志的文件夹。
运行一遍代码,得到相关的文件,以我的为例,得到的文件如下:
tensorboard+win10+pycharm(记录自己的问题)

运行tensorboard

  1. 打开anaconda。进入如下图路径,点击小绿三角。打开terminal
    tensorboard+win10+pycharm(记录自己的问题)
  2. 切换路径 ,切换路径到前面那个log_dir文件夹内,如下图:
    tensorboard+win10+pycharm(记录自己的问题)
  3. 打开tensorboard。使用命令tensorboard --logdir=train。复制图片的链接,到浏览器就可以啦。
    tensorboard+win10+pycharm(记录自己的问题)
  4. 浏览器显示:浏览器中这些玩意儿的使用,这个博客也讲了还是前面那个连接,这是个棒棒的博主。
    tensorboard+win10+pycharm(记录自己的问题)

前期一直不行,,就是没有切换路径,搞了一下午,看了这个博客的内容才明白了。解决没数据的问题
后来又有点问题,自己感觉是父路径不能直接在盘里,比如F:/train,train文件夹内保存日志文件,这样就不行,父目录必须得是一个文件夹,比如F:/sum/train,这样就可以了呢。
顺道还学习了,如何在指定路径打开cmd ,打开指定文件夹,按住shift,然后点击右键。有一片新天地在等着你哈哈哈。百度知道。这个百度知道写得很明白。

相关标签: tensorboard