隨心一記(沒什麼營養)

tf.abs

求絕對值的

tf.add()

相加的 支援broadacst

tf.negative()

取反的
理由有這樣的需求 求兩個tensor的相減的結果

tf.add(tensor1,tf.negtive(temsor2))

機器學習中的NN 實現出處:

資料下載

import tensorflow as tf
import numpy as np

# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist =
 
 input_data.read_data_sets("../mnist_data/", one_hot=True)

# In this example, we limit mnist data# In th 
Xtr, Ytr = mnist.train.next_batch(5000) #5000 for training (nn candidates)   注意這裡返回是 arr
Xte, Yte = mnist.test.next_batch(200) #200 for testing

xtr=tf.placeholder("float",[None,784])
xte=tf.placeholder("float"
 
,[784])

distance=tf.reduce_sum(tf.abs(tf.add(xtr,tf.negative(xte))),axis=1)
nn_index=tf.argmin(distance,axis=0)
init=tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    accuracy=0.0
    # loop over test data
    for i in range(len(Xte)):
        # Get nearest neighbor
        a =
 
 sess.run(nn_index, feed_dict={xtr: Xtr, xte: Xte[i, :]}) #注意在 feed_dict裡面 的變數上下文操作
        # Get nearest neighbor class label and compare it to its true label
        print("Test", i, "Prediction:", np.argmax(Ytr[a]), \
            "True Class:", np.argmax(Yte[i]))
        # Calculate accuracy
        if np.argmax(Ytr[a]) == np.argmax(Yte[i]):
            accuracy += 1./len(Xte)
    print("Done!")
    print("Accuracy:", accuracy)