• Could you try using python -m tensorboard --logdir "${MODEL_DIR}" instead? I suspect that this will fix your issue.
  • I should have written tensorboard.main instead of TensorBoard: python -m tensorboard.main --logdir "${MODEL_DIR}"
  • 但是虽然启动了6006端口,但是加载文件失败,这个问题留到后面解决,继续跟进tensorflow学习

  • 更新几个现有的demo

如路径为:E:\MyTensorBoard\logs, logs中又包含train和test。此时,TensorBoard通过读取事件文件来运行,通过在cmd 中键入命令:tensorboard --logdir=log文件路径。按照我们当前目录,若写成:

tensorboard --logdir=E:\MyTensorBoard\logs
显示结果是:No scalar、No image...,然而查了几遍代码也没有问题,事件文件也没有问题。 解决方法:方法一:将cmd的默认路径cd到log文件的上一层,即cd /d E:\MyTensorBoard,之后等号后面直接键入log文件名即可,不需写全路径,即 tensorboard --logdir=logs。方法二:双斜杠,即tensorboard --logdir=E://MyTensorBoard//logs。最后根据得到的网址http://hostIP:6006,在chrome里打开,就可以可视化我们的图表了,幸福来的太突然

Tensor与Graph可视化

  • Summary:所有需要在TensorBoard上展示的统计结果。
  • tf.name_scope():为Graph中的Tensor添加层级,TensorBoard会按照代码指定的层级进行展示,初始状态下只绘制最高层级的效果,点击后可展开层级看到下一层的细节。
  • tf.summary.scalar():添加标量统计结果。
  • tf.summary.histogram():添加任意shape的Tensor,统计这个Tensor的取值分布。
  • tf.summary.merge_all():添加一个操作,代表执行所有summary操作,这样可以避免人工执行每一个summary op。
  • tf.summary.FileWrite:用于将Summary写入磁盘,需要制定存储路径logdir,如果传递了Graph对象,则在Graph Visualization会显示Tensor Shape Information。执行summary op后,将返回结果传递给add_summary()方法即可。

import gzip
import struct
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
import tensorflow as tf # MNIST data is stored in binary format,
# and we transform them into numpy ndarray objects by the following two utility functions
def read_image(file_name):
with gzip.open(file_name, 'rb') as f:
buf = f.read()
index = 0
magic, images, rows, columns = struct.unpack_from('>IIII', buf, index)
index += struct.calcsize('>IIII') image_size = '>' + str(images * rows * columns) + 'B'
ims = struct.unpack_from(image_size, buf, index) im_array = np.array(ims).reshape(images, rows, columns)
return im_array def read_label(file_name):
with gzip.open(file_name, 'rb') as f:
buf = f.read()
index = 0
magic, labels = struct.unpack_from('>II', buf, index)
index += struct.calcsize('>II') label_size = '>' + str(labels) + 'B'
labels = struct.unpack_from(label_size, buf, index) label_array = np.array(labels)
return label_array print ("Start processing MNIST handwritten digits data...")
train_x_data = read_image("MNIST_data/train-images-idx3-ubyte.gz")
train_x_data = train_x_data.reshape(train_x_data.shape[0], -1).astype(np.float32)
train_y_data = read_label("MNIST_data/train-labels-idx1-ubyte.gz")
test_x_data = read_image("MNIST_data/t10k-images-idx3-ubyte.gz")
test_x_data = test_x_data.reshape(test_x_data.shape[0], -1).astype(np.float32)
test_y_data = read_label("MNIST_data/t10k-labels-idx1-ubyte.gz") train_x_minmax = train_x_data / 255.0
test_x_minmax = test_x_data / 255.0 # Of course you can also use the utility function to read in MNIST provided by tensorflow
# from tensorflow.examples.tutorials.mnist import input_data
# mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
# train_x_minmax = mnist.train.images
# train_y_data = mnist.train.labels
# test_x_minmax = mnist.test.images
# test_y_data = mnist.test.labels # We evaluate the softmax regression model by sklearn first
eval_sklearn = False
if eval_sklearn:
print ("Start evaluating softmax regression model by sklearn...")
reg = LogisticRegression(solver="lbfgs", multi_class="multinomial")
reg.fit(train_x_minmax, train_y_data)
np.savetxt('coef_softmax_sklearn.txt', reg.coef_, fmt='%.6f') # Save coefficients to a text file
test_y_predict = reg.predict(test_x_minmax)
print ("Accuracy of test set: %f" % accuracy_score(test_y_data, test_y_predict)) eval_tensorflow = True
batch_gradient = False # Summary:所有需要在TensorBoard上展示的统计结果。
def variable_summaries(var):
with tf.name_scope('summaries'):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', mean)
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) if eval_tensorflow:
print ("Start evaluating softmax regression model by tensorflow...")
# reformat y into one-hot encoding style
lb = preprocessing.LabelBinarizer()
lb.fit(train_y_data)
train_y_data_trans = lb.transform(train_y_data)
test_y_data_trans = lb.transform(test_y_data) x = tf.placeholder(tf.float32, [None, 784])
with tf.name_scope('weights'):
W = tf.Variable(tf.zeros([784, 10]))
variable_summaries(W)
with tf.name_scope('biases'):
b = tf.Variable(tf.zeros([10]))
variable_summaries(b)
with tf.name_scope('Wx_plus_b'):
V = tf.matmul(x, W) + b
tf.summary.histogram('pre_activations', V)
with tf.name_scope('softmax'):
y = tf.nn.softmax(V)
tf.summary.histogram('activations', y) y_ = tf.placeholder(tf.float32, [None, 10]) with tf.name_scope('cross_entropy'):
loss = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
tf.summary.scalar('cross_entropy', loss) with tf.name_scope('train'):
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss) with tf.name_scope('evaluate'):
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) init = tf.global_variables_initializer() sess = tf.Session()
sess.run(init) merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('tmp/train', sess.graph)
test_writer = tf.summary.FileWriter('tmp/test') if batch_gradient:
for step in range(300):
sess.run(train, feed_dict={x: train_x_minmax, y_: train_y_data_trans})
if step % 10 == 0:
print ("Batch Gradient Descent processing step %d" % step)
print ("Finally we got the estimated results, take such a long time...")
else:
for step in range(1000):
if step % 10 == 0:
summary, acc = sess.run([merged, accuracy], feed_dict={x: test_x_minmax, y_: test_y_data_trans})
test_writer.add_summary(summary, step)
print ("Stochastic Gradient Descent processing step %d accuracy=%.2f" % (step, acc))
else:
sample_index = np.random.choice(train_x_minmax.shape[0], 100)
batch_xs = train_x_minmax[sample_index, :]
batch_ys = train_y_data_trans[sample_index, :]
summary, _ = sess.run([merged, train], feed_dict={x: batch_xs, y_: batch_ys})
train_writer.add_summary(summary, step) np.savetxt('coef_softmax_tf.txt', np.transpose(sess.run(W)), fmt='%.6f') # Save coefficients to a text file
print ("Accuracy of test set: %f" % sess.run(accuracy, feed_dict={x: test_x_minmax, y_: test_y_data_trans}))

Embeddings

  • TensorBoard是TensorFlow自带的一个可视化工具,Embeddings是其中的一个功能,用于在二维或三维空间对高维数据进行探索
# -*- coding: utf-8 -*-
# @author: ranjiewen
# @date: 2017-02-08
# @description: hello world program to set up embedding projector in TensorBoard based on MNIST
# @ref: http://yann.lecun.com/exdb/mnist/, https://www.tensorflow.org/images/mnist_10k_sprite.png
# import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
from tensorflow.examples.tutorials.mnist import input_data
import os PATH_TO_MNIST_DATA = "MNIST_data"
LOG_DIR = "emd"
IMAGE_NUM = 10000 # Read in MNIST data by utility functions provided by TensorFlow
mnist = input_data.read_data_sets(PATH_TO_MNIST_DATA, one_hot=False) # Extract target MNIST image data
plot_array = mnist.test.images[:IMAGE_NUM] # shape: (n_observations, n_features) # Generate meta data
np.savetxt(os.path.join(LOG_DIR, 'metadata.tsv'), mnist.test.labels[:IMAGE_NUM], fmt='%d') # Download sprite image
# https://www.tensorflow.org/images/mnist_10k_sprite.png, 100x100 thumbnails
PATH_TO_SPRITE_IMAGE = os.path.join(LOG_DIR, 'mnist_10k_sprite.png') # To visualise your embeddings, there are 3 things you need to do:
# 1) Setup a 2D tensor variable(s) that holds your embedding(s)
session = tf.InteractiveSession()
embedding_var = tf.Variable(plot_array, name='embedding')
tf.global_variables_initializer().run() # 2) Periodically save your embeddings in a LOG_DIR
# Here we just save the Tensor once, so we set global_step to a fixed number
saver = tf.train.Saver()
saver.save(session, os.path.join(LOG_DIR, "model.ckpt"), global_step=0) # 3) Associate metadata and sprite image with your embedding
# Use the same LOG_DIR where you stored your checkpoint.
summary_writer = tf.summary.FileWriter(LOG_DIR) config = projector.ProjectorConfig()
# You can add multiple embeddings. Here we add only one.
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
# Link this tensor to its metadata file (e.g. labels).
embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv')
# Link this tensor to its sprite image.
embedding.sprite.image_path = PATH_TO_SPRITE_IMAGE
embedding.sprite.single_image_dim.extend([28, 28])
# Saves a configuration file that TensorBoard will read during startup.
projector.visualize_embeddings(summary_writer, config)

官网demo

  • 注意更改文件路径
"""A simple MNIST classifier which displays summaries in TensorBoard.
This is an unimpressive MNIST model, but it is a good example of using
tf.name_scope to make a graph legible in the TensorBoard graph explorer, and of
naming summary tags so that they are grouped meaningfully in TensorBoard.
It demonstrates the functionality of every TensorBoard dashboard.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function import argparse
import os
import sys import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data FLAGS = None #"F:\RANJIEWEN\Deep_learning\TensorFlow\log" 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)# log/train or log/test
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=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
# 'tensorflow/mnist/input_data'),
# help='Directory for storing input data')
# parser.add_argument(
# '--log_dir',
# type=str,
# default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
# 'tensorflow/mnist/logs/mnist_with_summaries'),
# help='Summaries log directory') parser.add_argument(
'--data_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', 'F:\RANJIEWEN\Deep_learning\TensorFlow\MNIST_data')),
help='Directory for storing input data')
parser.add_argument(
'--log_dir',
type=str,
default=os.path.join(os.getenv('TEST_TMPDIR', 'F:\RANJIEWEN\Deep_learning\TensorFlow\log')),
help='Summaries log directory')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

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