标签：knn python tensorflow machine-learning
我正在努力在TensorFlow中实现K-Nearest Neighbor.我认为要么我忽略了一个错误,要么做了一些可怕的错误.
以下代码始终将Mnist标签预测为0.
from __future__ import print_function
import numpy as np
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
K = 4
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(55000) # whole training set
Xte, Yte = mnist.test.next_batch(10000) # whole test set
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
ytr = tf.placeholder("float", [None, 10])
xte = tf.placeholder("float", [784])
# Euclidean Distance
distance = tf.neg(tf.sqrt(tf.reduce_sum(tf.square(tf.sub(xtr, xte)), reduction_indices=1)))
# Prediction: Get min distance neighbors
values, indices = tf.nn.top_k(distance, k=K, sorted=False)
nearest_neighbors = []
for i in range(K):
nearest_neighbors.append(np.argmax(ytr[indices[i]]))
sorted_neighbors, counts = np.unique(nearest_neighbors, return_counts=True)
pred = tf.Variable(nearest_neighbors[np.argmax(counts)])
# not works either
# neighbors_tensor = tf.pack(nearest_neighbors)
# y, idx, count = tf.unique_with_counts(neighbors_tensor)
# pred = tf.slice(y, begin=[tf.arg_max(count, 0)], size=tf.constant([1], dtype=tf.int64))[0]
accuracy = 0.
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# loop over test data
for i in range(len(Xte)):
# Get nearest neighbor
nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
# Get nearest neighbor class label and compare it to its true label
print("Test", i, "Prediction:", nn_index,
"True Class:", np.argmax(Yte[i]))
# Calculate accuracy
if nn_index == np.argmax(Yte[i]):
accuracy += 1. / len(Xte)
print("Done!")
print("Accuracy:", accuracy)
任何帮助是极大的赞赏.
解决方法:
因此,一般来说,在定义TensorFlow模型时使用numpy函数并不是一个好主意.这正是您的代码无效的原因.我只对代码进行了两处更改.我用tf.argmax替换了np.argmax.我也删除了#This的评论也不起作用.
这是完整的工作代码：
from __future__ import print_function
import numpy as np
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
K = 4
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
# In this example, we limit mnist data
Xtr, Ytr = mnist.train.next_batch(55000) # whole training set
Xte, Yte = mnist.test.next_batch(10000) # whole test set
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
ytr = tf.placeholder("float", [None, 10])
xte = tf.placeholder("float", [784])
# Euclidean Distance
distance = tf.negative(tf.sqrt(tf.reduce_sum(tf.square(tf.subtract(xtr, xte)), reduction_indices=1)))
# Prediction: Get min distance neighbors
values, indices = tf.nn.top_k(distance, k=K, sorted=False)
nearest_neighbors = []
for i in range(K):
nearest_neighbors.append(tf.argmax(ytr[indices[i]], 0))
neighbors_tensor = tf.stack(nearest_neighbors)
y, idx, count = tf.unique_with_counts(neighbors_tensor)
pred = tf.slice(y, begin=[tf.argmax(count, 0)], size=tf.constant([1], dtype=tf.int64))[0]
accuracy = 0.
# Initializing the variables
init = tf.initialize_all_variables()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# loop over test data
for i in range(len(Xte)):
# Get nearest neighbor
nn_index = sess.run(pred, feed_dict={xtr: Xtr, ytr: Ytr, xte: Xte[i, :]})
# Get nearest neighbor class label and compare it to its true label
print("Test", i, "Prediction:", nn_index,
"True Class:", np.argmax(Yte[i]))
#Calculate accuracy
if nn_index == np.argmax(Yte[i]):
accuracy += 1. / len(Xte)
print("Done!")
print("Accuracy:", accuracy)
标签：knn,python,tensorflow,machine-learning 来源： https://codeday.me/bug/20190724/1523923.html
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