9.2 mnist

xiaoxiao2021-02-28  8

tensorboard tensorflow中的可视化组件

在新版本的tensorflow 中tensorboard已经被整合,无需下载.其执行是利用了一个封装的内置服务器,性能不错. 我们可以将神经网络运行时的各类数据存储下来进行可视化展示,我首先展示其功能,然后再分解代码.本处例子源自tensorflow的官方源码,如果你需要了解更多,建议直接阅读官方文档







tf.summary使我们需要的 用来想tensorboard写入数据的方法tf.summary.scalar(‘accuracy’, accuracy) 如代码,scalar可以将数据传入,并在tensorboard中最终以表格的形式展示tf.summary.image(‘input’, image_shaped_input, NUM_CLASSES) image方法则是前面图片中image模块的数据传入方法


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 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' FLAGS = None # The MNIST dataset has 10 classes, representing the digits 0 through 9. NUM_CLASSES = 10 # The MNIST images are always 28x28 pixels. IMAGE_SIZE = 28 IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE



# Import data mnist = input_data.read_data_sets("/home/fonttian/Data/MNIST_data/", one_hot=True, fake_data=FLAGS.fake_data) sess = tf.InteractiveSession() # Create a multilayer model. # Input placeholders with tf.name_scope('input'): # 此处定义了input可视化节点,下面则是占位符的声明,在tensorflow中的函数一个共有的name,就是声明的节点的name(名字),该部分可以在上面的图片中展示 x = tf.placeholder(tf.float32, [None, IMAGE_PIXELS], name='x-input') y_ = tf.placeholder(tf.float32, [None, NUM_CLASSES], name='y-input') with tf.name_scope('input_reshape'): image_shaped_input = tf.reshape(x, [-1, IMAGE_SIZE, IMAGE_SIZE, 1]) tf.summary.image('input', image_shaped_input, NUM_CLASSES) # tf.summary 是将数据传入tensorboard的,image将会展示在我们刚刚展示的images部分. ``` 抽取代码部分内容,封装为函数 <div class="se-preview-section-delimiter"></div> # 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)


# 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 feed_dict(train):# 需要feed_dict参数 """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}


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, IMAGE_PIXELS, FLAGS.hidden1_units, 'layer1') with tf.name_scope('dropout'): # 定义dropout的可视化节点,dropout避免过拟合的方法 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, FLAGS.hidden1_units, NUM_CLASSES, '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()


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, 'stepd' % 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) # 停止writer 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('--hidden1_units', type=float, default=500, help='The number of neurons in the first hidden.') 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='/home/fonttian/Data/MNIST_data/', help='Directory for storing input data') parser.add_argument( '--log_dir', type=str, default='/home/fonttian/Documents/tensorflow/TensorFlow-Basics/tmp/tensorflow/mnist/logs/mnist_with_summaries', help='Summaries log directory') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)



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