摘要:是自带的一个可视化工具。本文在学习笔记的基础上修改少量代码,以探索的使用方法。添加标量统计结果。执行后,将返回结果传递给方法即可。效果首先是,显示取值范围更细节的取值概率信息在里,如下双击后,可查看下一层级的详细信息
前言
本文基于TensorFlow官网How-Tos的Visualizing Learning和Graph Visualization写成。
TensorBoard是TensorFlow自带的一个可视化工具。本文在学习笔记(4)的基础上修改少量代码,以探索TensorBoard的使用方法。
代码# -*- coding=utf-8 -*- # @author: 陈水平 # @date: 2017-02-09 # @description: implement a softmax regression model upon MNIST handwritten digits # @ref: http://yann.lecun.com/exdb/mnist/ 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 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("log/train", sess.graph) test_writer = tf.summary.FileWriter("log/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})思考
主要修改点有:
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。
效果 Visualizing Learning Scalar Histogram首先是Distribution,显示取值范围:
更细节的取值概率信息在Historgram里,如下:
Graph Visualization双击train后,可查看下一层级的详细信息:
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