TensorFlow：繪圖描述的數據點，我想圖和決策界

Question

實現類似的東西：

https://rootpy.github.io/root_numpy/_images/plot_multiclass_1.png

什麼是最優雅的解決方案？獲取權重，偏差，函數和數據並用其他工具繪製它或者TensorFlow是否支持它？

Answer 1

據我所知，Tensorflow並不直接支持繪製決策邊界。

這當然不是最優雅的解決方案，但您可以創建一個網格。分類網格的每個點，然後繪製它。例如：

#!/usr/bin/env python 

""" 
Solve the XOR problem with Tensorflow. 

The XOR problem is a two-class classification problem. You only have four 
datapoints, all of which are given during training time. Each datapoint has 
two features: 

     x o 

     o x 

As you can see, the classifier has to learn a non-linear transformation of 
the features to find a propper decision boundary. 
""" 

__author__ = "Martin Thoma" 

import tensorflow as tf 
import matplotlib.pyplot as plt 
import numpy as np 

# The training data 
XOR_X = [[0, 0], [0, 1], [1, 0], [1, 1]] # Features 
XOR_Y = [[0], [1], [1], [0]] # Class labels 
XOR_Y = [[1, 0], [0, 1], [0, 1], [1, 0]] # Target values 

assert len(XOR_X) == len(XOR_Y) # sanity check 

# The network 
nb_classes = 2 
input_ = tf.placeholder(tf.float32, 
         shape=[None, len(XOR_X[0])], 
         name="input") 
target = tf.placeholder(tf.float32, 
         shape=[None, nb_classes], 
         name="output") 
nb_hidden_nodes = 2 
# enc = tf.one_hot([0, 1], 2) 
w1 = tf.Variable(tf.random_uniform([2, nb_hidden_nodes], -1, 1), 
       name="Weights1") 
w2 = tf.Variable(tf.random_uniform([nb_hidden_nodes, nb_classes], -1, 1), 
       name="Weights2") 
b1 = tf.Variable(tf.zeros([nb_hidden_nodes]), name="Biases1") 
b2 = tf.Variable(tf.zeros([nb_classes]), name="Biases2") 
activation2 = tf.sigmoid(tf.matmul(input_, w1) + b1) 
hypothesis = tf.nn.softmax(tf.matmul(activation2, w2) + b2) 
cross_entropy = -tf.reduce_sum(target * tf.log(hypothesis)) 
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(cross_entropy) 

# Start training 
init = tf.initialize_all_variables() 
with tf.Session() as sess: 
    sess.run(init) 

    for i in range(100000): 
     sess.run(train_step, feed_dict={input_: XOR_X, target: XOR_Y}) 

     if i % 10000 == 0: 
      print('Epoch ', i) 
      print('Hypothesis ', sess.run(hypothesis, 
              feed_dict={input_: XOR_X, 
                target: XOR_Y})) 
      print('w1 ', sess.run(w1)) 
      print('b1 ', sess.run(b1)) 
      print('w2 ', sess.run(w2)) 
      print('b2 ', sess.run(b2)) 
      print('cost (ce)', sess.run(cross_entropy, 
             feed_dict={input_: XOR_X, 
                target: XOR_Y})) 

      # Visualize classification boundary 
      xs = np.linspace(-5, 5) 
      ys = np.linspace(-5, 5) 
      pred_classes = [] 
      for x in xs: 
       for y in ys: 
        pred_class = sess.run(hypothesis, 
              feed_dict={input_: [[x, y]]}) 
        pred_classes.append((x, y, pred_class.argmax())) 
      xs_p, ys_p = [], [] 
      xs_n, ys_n = [], [] 
      for x, y, c in pred_classes: 
       if c == 0: 
        xs_n.append(x) 
        ys_n.append(y) 
       else: 
        xs_p.append(x) 
        ys_p.append(y) 
      plt.plot(xs_p, ys_p, 'ro', xs_n, ys_n, 'bo') 
      plt.show() 

其給出